Alternatives to TokenAtlas

FinOpsly is an AI-native control plane for managing Cloud, Data, and AI spend at enterprise scale. Built for organizations operating across multiple clouds and data platforms, FinOpsly shifts FinOps from passive reporting to active, governed execution. The platform connects cost, usage, and business context into a unified operating model—allowing teams to anticipate spend, enforce guardrails, and take automated action with confidence. FinOpsly brings together infrastructure (AWS, Azure, GCP), data platforms (Snowflake, Databricks, BigQuery), and AI workloads into a single decision and execution layer. With explainable AI agents operating under policy-based controls, teams can safely automate optimization, trace cost drivers to real workloads, and stop budget drift before it becomes a problem. Key capabilities include: Business-aware cost attribution across products, teams, and services Predictive insight into cost drivers with clear, explainable reasoning Policy-controlled automation to optimize spend without disrupting performance Early detection and prevention of overruns, inefficiencies, and financial drift FinOpsly enables engineering, finance, and platform teams to operate from the same source of truth—turning cloud and data spend into a controllable, measurable part of the business.

Mavvrik operates as a sophisticated platform for managing costs associated with AI and hybrid infrastructure, providing a centralized hub for finance, FinOps, IT, and engineering teams to oversee GenAI, autonomous agents, GPUs, cloud systems, on-premises resources, Kubernetes, data platforms, and SaaS solutions. By consolidating cost, usage, and telemetry data from major providers such as AWS, Azure, Google Cloud, Oracle, VMware, NVIDIA, OpenAI, Anthropic, Gemini, Snowflake, Databricks, and LiteLLM, it establishes a comprehensive source of truth for the entire technology ecosystem. Teams can meticulously monitor each model interaction, agent engagement, GPU utilization, and resource workload, allowing for precise spending allocation across various dimensions, including customer, product, feature, project, application, environment, team, or cost center. Through in-depth analysis of cost-to-serve and unit economics, Mavvrik uncovers margin losses, identifies costly workloads, and clarifies the actual expenses involved in delivering each service. Additionally, its capability for real-time anomaly detection and alerts serves to flag unusual usage patterns before they escalate into unexpected budget overruns, while its predictive forecasting tools assist organizations in effectively modeling their cloud, GPU, and AI-related expenditures. This holistic approach empowers teams to make informed financial decisions and optimize resource utilization for sustained growth.

LLMetrics serves as a comprehensive cost tracking solution for teams involved in the development of AI products, integrating model expenses, token consumption, feature attribution, and usage notifications into a single, interactive dashboard. This powerful tool accommodates over 100 models from various providers, including OpenAI, Anthropic, Google Gemini, Mistral, Cohere, Together AI, and Groq, with pricing information updated on a daily basis. Teams can label each model interaction with details such as feature name, provider, model type, input tokens, and output tokens, enabling them to pinpoint which specific functionalities—be it a chatbot, summarizer, search tool, or lesson creator—are contributing to their expenditures. The platform offers real-time updates and daily trend visualizations, illustrating how costs fluctuate in response to software releases, modifications to prompts, increases in traffic, or transitions between models. Additionally, it includes spend thresholds and spike-detection features that can alert teams via email or Slack when unusual usage patterns are identified, aiding them in preventing runaway loops and unforeseen cost surges prior to receiving the provider invoice. By leveraging these insights, teams can make informed decisions regarding their AI product strategies and budget management.

ZenLLM serves as an AI-driven platform focused on optimizing costs for engineering teams that deploy LLM applications in live environments. By linking provider invoices to the underlying application activities, it identifies which specific prompts, workflows, models, customers, retries, and request paths contribute to financial expenditures. Teams can utilize the ZenLLM SDK to transmit request-level telemetry, allowing them to incorporate relevant business context—such as workflow, owner, customer, team, or product feature—without having to store the content of prompts or responses. In addition, it keeps track of token consumption, model selection, latency, errors, retries, and overall costs, revealing wasteful patterns that provider dashboards often obscure. The platform is capable of recognizing instances of context accumulation when conversations or agents repeatedly send extended histories, excessive use of premium models for low-risk tasks, retry loops that lead to unnecessary expenses, outdated system prompts, routing errors, anomalies, and a lack of accountability regarding costs. Furthermore, ZenLLM empowers teams to make informed decisions that can significantly enhance cost efficiency in their LLM application operations.

Cloptima is an innovative platform that integrates AI and cloud FinOps, offering governance for LLM expenditures, insights into multicloud costs, optimization for Kubernetes, analysis of queries, and controls on engineering costs within a unified framework. Through its AI gateway, teams can securely utilize their own credentials from OpenAI, Anthropic, Gemini, Vertex AI, and Amazon Bedrock, applying a range of protections like encrypted controls, virtual keys, model policies, token limits, budgets, guardrails, and attribution before any requests are sent to the providers. The platform's spend analytics provide a comprehensive breakdown of usage categorized by provider, model, team, application, environment, user, agent session, tool, workflow, and other dimensions, while the agent controls monitor retries, loops, tool interactions, and the potential for runaway costs. Additionally, exact and semantic response caching can help minimize redundant usage, whereas intelligent routing capabilities allow for the redirection of eligible traffic to more cost-effective or faster models, with the option for canary rollout and rollback if there are regressions in quality, latency, or error rates. This holistic approach ensures that organizations can effectively manage their AI-related expenditures while maximizing efficiency and performance across their operations.

AI Cost Board serves as a comprehensive platform for monitoring AI API usage and managing associated costs, consolidating important metrics like expenses, requests, tokens, latency, errors, and overall usage from various model providers into a unified real-time dashboard. By directing LLM traffic through a single proxy endpoint, applications can efficiently forward requests to the designated provider while capturing detailed logs that include model information, token usage, status, timing, costs, input, output, and raw JSON context. Typically, teams only need to adjust the base URL of the provider and utilize an AI Cost Board project key, thereby maintaining the integrity of the original request structure. This platform accommodates a variety of providers such as OpenAI, Anthropic, and Google Gemini, offering a standardized setup that harmonizes usage data across different integrations. Cost analytics provide a breakdown of spending categorized by project, provider, model, and timeframe, enabling users to identify trends, calculate cost per request, assess success rates, and evaluate operational performance. Moreover, the searchable request logs empower developers to analyze payloads, address failures, compare various models, and probe into instances of slow or costly API calls. Overall, AI Cost Board enhances transparency and control over AI API expenditures, facilitating informed decision-making for teams utilizing AI technology.

FinOps LLM serves as an advanced platform for AI cost management and observability, specifically designed for engineering teams utilizing production GenAI. It enables transparency in token expenditures across a variety of providers such as OpenAI, Anthropic, Amazon Bedrock, Google Gemini, Azure, and Groq, while also aligning internal usage data with invoices from these providers. Users can filter token-level expenses based on provider, model, feature, team, customer, environment, and other custom metrics, ensuring that each dollar spent has a designated owner. Additionally, the platform includes attribution and chargeback functionalities that correlate usage with product interfaces and customer demographics, facilitating showback processes and allowing for data exports to systems like NetSuite, QuickBooks, CSV, or through APIs. Furthermore, real-time anomaly detection features track spending, latency, and quality, comparing them against dynamic feature baselines, and issue alerts via Slack, PagerDuty, email, or webhooks whenever notable changes occur. To further enhance cost control, optional budget enforcement and auto-throttling measures can prevent excessive spending due to runaway agents, excessive retries, or unexpected model shifts. This comprehensive approach ensures that engineering teams can manage their AI resources effectively while maintaining financial oversight.

Toolspend is an innovative spend management platform powered by AI, aimed at providing organizations with comprehensive insights into their expenses related to AI and SaaS through a cohesive, automated dashboard. By linking seamlessly with AI service providers and financial systems, it uncovers actual usage trends, highlights which teams are responsible for spending, and aligns token metrics with billing details. This platform surpasses basic subscription monitoring by evaluating usage behaviors, allowing it to identify underused licenses, duplicated tools across different departments, and areas where overpayments may occur. With features such as real-time monitoring, alerts for unexpected usage spikes, and month-end forecasting, teams can better prepare for costs prior to receiving invoices. Additionally, it offers AI-generated suggestions, like transitioning to more affordable models or halting resources that are not in use, which assists companies in minimizing waste and managing budget increases effectively. Furthermore, by leveraging its insights, organizations can make informed decisions that enhance their operational efficiency.

StackSpend is an advanced cost management platform leveraging cloud and AI technologies, designed to offer engineering, finance, and FinOps teams a consolidated daily overview of their contemporary AI infrastructure. By establishing read-only connections to a variety of providers such as AWS, Google Cloud, Azure, Snowflake, and others, it seamlessly imports historical billing information and standardizes expenditure across different services. The platform features comprehensive dashboards and exploration tools that dissect costs by various dimensions, including provider, service, model, project, user, team, feature, and customer, thereby aiding teams in analyzing AI COGS, cost per request, and profit margins at the product level. Additionally, it provides insights into budgets and projected spending trends, while its same-day anomaly detection feature identifies unexpected cost spikes triggered by factors such as traffic surges, prompt errors, model adjustments, deployment activities, or specific user actions. Notifications and daily indicators, categorized as green, amber, or red based on spending levels, can be dispatched through communication platforms like Slack, Microsoft Teams, email, or webhooks, ensuring teams remain informed about their spending patterns. Ultimately, StackSpend empowers organizations to maintain a firm grip on their AI expenditures, fostering enhanced financial accountability and strategic decision-making.

Burnwise serves as a financial assistant powered by AI, providing insights into an organization's AI expenditure, the reasons behind spending fluctuations, and strategies for cost reduction without compromising on product quality. It monitors usage metrics across large language models, image generation, video, and audio services from leading providers through a consolidated SDK and a cohesive dashboard. Rather than merely presenting aggregate token statistics, Burnwise breaks down costs by specific product features, users, sessions, teams, and agent workflows, allowing teams to gain a clearer understanding of the actual expenses associated with functions such as chat support, document assessment, summaries, or translation services. The platform's usage intelligence uncovers discrepancies between cost and value, while real-time anomaly alerts detect unexpected surges and excessive prompt usage. Additionally, Burnwise provides a concise set of prioritized decision cards that outline potential savings, risk factors, and quality implications, suggesting actions such as changing models, activating semantic caching, imposing limits, or altering feature operations. By offering these insights, Burnwise empowers organizations to make informed decisions that enhance efficiency and optimize resource allocation.

LLMeter is a comprehensive open-source platform designed for monitoring AI costs, allowing developers to manage their expenditures across various providers like OpenAI, Anthropic, DeepSeek, OpenRouter, Mistral, and Azure OpenAI from a single dashboard. By simply connecting read-only provider keys, teams can instantly access detailed insights into actual costs, daily usage trends, model-specific analytics, and potential areas for optimization, all within approximately 30 seconds and without any need for SDK installation, endpoint modifications, or rerouting production traffic through a proxy. Since it facilitates direct communication with model providers, LLMeter introduces no additional latency, avoids becoming a single point of failure, and does not access or store any user prompts or completions. Additionally, budget alerts notify teams prior to exceeding their daily or monthly spending thresholds, while anomaly detection features help catch unexpected usage surges before they escalate. The platform's dashboard provides a clear overview of the costs associated with various providers, models, endpoints, customers, and environments, and its integration with OpenRouter enhances transparency by covering over 500 models, ensuring users have a robust tool for managing their AI-related expenditures efficiently. Ultimately, LLmeter empowers teams to make informed financial decisions regarding their AI usage.

We provide a unified platform for engineering, finance, and FinOps teams to monitor, allocate, and enhance spending on LLM APIs from various providers. Expenses are categorized based on customizable dimensions that align with your organization's financial reporting practices. Engineering teams experience seamless cost monitoring that doesn't impede their workflow. CTOs benefit from a consolidated view that facilitates model governance and mitigates unauthorized usage. CFOs receive high-quality financial reports for accurate forecasting, budgeting, and chargebacks, all tailored to their specific reporting frameworks. FinOps teams have access to real-time cost information across multiple providers, integrating effortlessly into their existing cloud management processes. When your organization utilizes LLM APIs and the board inquires about spending and its justification, we serve as the definitive solution to those questions. Furthermore, our platform empowers teams to make informed financial decisions, increasing accountability and optimizing resource allocation.

Waterfall serves as a credit infrastructure tailored for platforms that leverage large language models, enabling the transformation of AI applications into profitable business ventures without the need for teams to develop a proprietary billing system. It offers each user, agent, or team a credit wallet secured by stablecoins, meticulously tracking every model interaction based on provider, model, token count, and associated costs. Users can either route their requests through the Waterfall Gateway or utilize TypeScript and Python SDKs for integration, ensuring that usage is accurately attributed to the appropriate wallet in real time. Each API request is settled instantly against the wallet, leading to a decrease in credits while allowing for immediate revenue recognition for every request, eliminating the delays associated with traditional invoicing and manual accounting processes. With support for over 300 models from various providers, including OpenAI, Anthropic, DeepSeek, and xAI, Waterfall enables products to seamlessly deploy multiple AI services while managing a unified accounting framework. This innovative approach simplifies financial management for AI-driven applications, making it easier for businesses to scale their operations efficiently.

Requesty is an innovative platform tailored to enhance AI workloads by smartly directing requests to the best-suited model for each specific task. It boasts sophisticated capabilities like automatic fallback systems and queuing processes, guaranteeing seamless service continuity even when certain models are temporarily unavailable. Supporting an extensive array of models, including GPT-4, Claude 3.5, and DeepSeek, Requesty also provides AI application observability, enabling users to monitor model performance and fine-tune their application usage effectively. By lowering API expenses and boosting operational efficiency, Requesty equips developers with the tools to create more intelligent and dependable AI solutions. This platform not only optimizes performance but also fosters innovation in AI development, paving the way for groundbreaking applications.

PromptUnit serves as an AI inference intermediary that automatically minimizes AI expenses by acting as a bridge between an application and its AI service providers, requiring no modifications to existing code. Teams simply replace the base URL while maintaining the same SDK, endpoints, response parsing, and error management, allowing PromptUnit to take care of routing, failover, cost monitoring, and quality assessment. It meticulously logs every API interaction, detailing aspects such as model, feature, user segment, token count, latency, and cost, thereby providing immediate insights into AI expenditures before any routing adjustments are implemented. In its observation mode, PromptUnit meticulously monitors traffic, shadow-classifies incoming requests, predicts potential savings, and clarifies routing choices, enabling teams to visualize exact savings prior to activating live routing. After activation, Smart Routing intelligently classifies tasks to direct each request to the most cost-effective model that meets the established quality standards. Additionally, PromptUnit incorporates features like prompt compression, token inflation protection, efficiency scoring for prompts, semantic request caching, and multi-model consensus for enhanced performance. Its comprehensive approach ensures that organizations can optimize their AI usage and manage budgets effectively.

Together AI offers a cloud platform purpose-built for developers creating AI-native applications, providing optimized GPU infrastructure for training, fine-tuning, and inference at unprecedented scale. Its environment is engineered to remain stable even as customers push workloads to trillions of tokens, ensuring seamless reliability in production. By continuously improving inference runtime performance and GPU utilization, Together AI delivers a cost-effective foundation for companies building frontier-level AI systems. The platform features a rich model library including open-source, specialized, and multimodal models for chat, image generation, video creation, and coding tasks. Developers can replace closed APIs effortlessly through OpenAI-compatible endpoints. Innovations such as ATLAS, FlashAttention, Flash Decoding, and Mixture of Agents highlight Together AI’s strong research contributions. Instant GPU clusters allow teams to scale from prototypes to distributed workloads in minutes. AI-native companies rely on Together AI to break performance barriers and accelerate time to market.

LLM Scout serves as a thorough platform for evaluation and analysis, assisting users in benchmarking, comparing, and interpreting the capabilities of large language models across various tasks, datasets, and real-world prompts, all within a cohesive environment. By allowing side-by-side comparisons, it assesses models based on accuracy, reasoning, factuality, bias, safety, and other vital metrics through customizable evaluation suites, curated benchmarks, and specialized tests. Users can integrate their own data and queries to evaluate how different models perform in relation to their specific workflows or industry requirements, with results visualized in an intuitive dashboard that underscores performance trends, strengths, and weaknesses. Additionally, LLM Scout offers functionalities for examining token usage, latency, cost effects, and model behavior under different scenarios, thereby equipping stakeholders with the insights needed to make educated choices regarding which models align best with particular applications or quality standards. This comprehensive approach not only enhances decision-making but also fosters a deeper understanding of model dynamics in practical contexts.

LayerLens serves as an autonomous platform dedicated to evaluating AI models, providing insights into their performance through verified benchmarks, prompt-specific outcomes, agentic comparisons, and audit-ready assessments across different vendors. This platform enables teams to conduct side-by-side comparisons of over 200 AI models, utilizing transparent benchmarks and consistent evaluation techniques focused on accuracy, latency, behavior, and practical application in real-world scenarios. Designed for comprehensive model analysis, LayerLens features Spaces that allow teams to organize benchmarks and evaluations, identify strengths in tasks, and monitor performance trends in relevant contexts. The platform also facilitates ongoing evaluations by continuously assessing model updates, prompt modifications, judge changes, and live traces, thereby empowering teams to identify issues like quality regressions, drift, silent failures, contamination, and policy concerns before they impact production. By prioritizing transparency and collaboration, LayerLens ensures that teams can make informed decisions about their AI model choices.

Stay informed about your OpenAI usage and expenses with AI Spend, ensuring you're never caught off guard. With its intuitive dashboard and notification features, AI Spend efficiently tracks your costs while actively monitoring your usage. The detailed analytics and visual charts offer valuable insights that empower you to optimize your engagement with OpenAI and prevent unexpected bills. Receive notifications daily, weekly, and monthly to stay updated on your spending patterns. Understand which models you're utilizing and the number of tokens consumed, allowing for a comprehensive view of your OpenAI costs. By using AI Spend, you can take control of your expenses and make informed decisions about your usage.

Braintrust is a powerful AI observability and evaluation platform built to help organizations monitor, analyze, and improve the performance of their AI systems in real-world environments. It captures detailed production traces, giving teams visibility into prompts, outputs, tool calls, and system behavior in real time. The platform enables users to evaluate AI performance using automated scoring, human feedback, or custom metrics to ensure consistent quality. Braintrust helps detect issues such as hallucinations, latency spikes, and regressions before they affect end users. It also allows teams to compare prompts and models side by side, making it easier to refine and optimize AI workflows. With scalable infrastructure, Braintrust can handle large volumes of AI trace data efficiently. The platform integrates seamlessly with existing development tools and supports multiple programming languages. It includes features like automated alerts and performance monitoring to proactively identify problems. Braintrust also supports building evaluation datasets directly from production data, improving testing accuracy. Its flexible and framework-agnostic design ensures compatibility with any AI stack. Overall, Braintrust empowers teams to continuously improve AI systems while maintaining reliability and performance at scale.

Make informed decisions by efficiently testing and modeling intricate business assumptions, whether in the cloud or on-premises. Oracle Essbase empowers organizations to swiftly extract insights from multidimensional datasets through what-if analyses and data visualization tools. Forecasting both company and departmental performance becomes a straightforward task, enabling the development and management of analytic applications that leverage business drivers to simulate various what-if scenarios. Users can oversee workflows for multiple scenarios all within a unified interface, simplifying submissions and approvals. The sandboxing features allow for rapid testing and evaluation of models, ensuring the best-suited model is chosen for production. Additionally, financial and business analysts benefit from over 100 ready-to-use mathematical functions that can be effortlessly implemented to generate new data insights. This comprehensive approach enhances the strategic capabilities of organizations, ultimately driving better performance outcomes.

Tokonomics serves as an intermediary cost measurement tool that connects your application to various LLM providers. By simply altering a URL, you can access real-time expense monitoring, receive budget notifications, and enforce strict spending limits across platforms like OpenAI, Anthropic, DeepSeek, Google Gemini, Mistral, Groq, and others. To implement, just swap your LLM base URL with Tokonomics while retaining your current code. Each API interaction is meticulously documented, capturing token usage, cost in precise 8-decimal USD, response time, and personalized tags for attributing costs to specific teams or features. Highlighted features include: - Notifications for budget thresholds through email, Slack, or Teams - Enforced spending limits that prevent further requests once the monthly budget is reached - An analytics dashboard that provides insights on spending by model, daily patterns, and opportunities for cost reduction - Support for BYOK (Bring Your Own Keys) with robust AES-256 encryption - Rate limiting for each API key to manage usage - Compatibility with a wide array of programming languages and HTTP clients, such as PHP, Python, Node.js, Go, and Ruby, ensuring versatility for developers. Additionally, Tokonomics empowers teams to take control of their spending while enhancing their capability to manage diverse LLM integrations efficiently.

FinForecast is a financial forecasting tool tailored for CFOs, finance professionals, and accountants in need of precise and dependable financial predictions. This platform enables organizations to generate interconnected Profit & Loss, Balance Sheet, and Cash Flow forecasts without the complications often associated with traditional spreadsheet models. Conventional Excel forecasting approaches frequently encounter issues as they expand, necessitating manual tweaks to ensure that financial statements remain consistent. In contrast, FinForecast eliminates the vulnerabilities of fragile spreadsheets by employing a robust forecasting engine that automatically refreshes financial statements whenever underlying assumptions are modified. Users can swiftly develop forecasts by inputting parameters such as revenue, costs, headcount, and funding assumptions, with the platform seamlessly producing integrated financial statements. Additionally, the scenario planning feature empowers finance teams to explore various growth, funding, and cost situations, while forecasts can easily be adjusted with actual performance data to sustain rolling forecasts. With its ability to streamline the forecasting process, FinForecast is particularly well-suited for businesses aiming for growth and efficient cash flow management, making it an invaluable asset in today's dynamic financial landscape. This tool not only enhances accuracy but also boosts confidence in financial decision-making.

Qarar is a collaborative platform powered by AI that allows teams to swiftly create CFO-ready business cases using tailored templates, established assumptions, and an interface that eliminates the need for spreadsheets. This tool automates the modeling of ROI and cost benefits, offers structured frameworks for articulating value propositions, and facilitates the real-time evaluation and comparison of various scenarios. Aimed at centralizing all workflows related to business cases, Qarar organizes assumptions, financial data, and narrative justifications to ensure uniformity across all proposals while preserving the organization’s knowledge as team members transition. It features integrated sales enablement tools that enhance the development of refined, investor-quality documents that include charts, executive summaries, and analyses tailored to stakeholders. Additionally, Qarar provides version control and portfolio management capabilities, enabling the CFO's office to efficiently track, compare, and approve multiple business cases simultaneously. With its user-friendly interface and comprehensive features, Qarar not only improves efficiency but also fosters collaboration among team members.

WrangleAI is a robust platform designed for enterprises, providing essential oversight, control, and governance regarding their AI deployments and expenditures. Serving as a "control plane" for generative AI tools such as GPT-4, Claude, and Gemini, it allows organizations to track usage in real-time, gain insights into costs, monitor infrastructure, and implement spending limits to prevent excessive budgets. Additionally, WrangleAI enhances AI observability by enabling teams to discern which models are utilized, by whom, and for which objectives, while also offering intelligent workload routing to more economical models without compromising quality. The platform further incorporates governance mechanisms, including role-based access control and compliance assistance with standards like SOC 2 and ISO 27001, facilitating collaboration among finance, engineering, and leadership teams to enforce policies and receive actionable insights for optimizing AI investments. This comprehensive approach not only streamlines AI management but also empowers organizations to make informed decisions about their AI strategies.

Gemini 3.1 Flash-Lite represents Google’s newest addition to the Gemini 3 family, built specifically for speed and affordability at scale. Engineered for developers managing high-frequency workloads, the model balances performance and cost efficiency without sacrificing quality. It is competitively priced at $0.25 per million input tokens and $1.50 per million output tokens, making it accessible for large production deployments. Compared to Gemini 2.5 Flash, it delivers substantially faster responses, including a 2.5x improvement in time to first token and a 45% boost in output speed. Benchmark evaluations show strong results, with an Elo score of 1432 and leading scores in reasoning and multimodal understanding tests. The model rivals or surpasses similarly tiered competitors while even outperforming some previous-generation Gemini models. A key feature is its adjustable reasoning control, enabling developers to fine-tune how much computational “thinking” is applied to each request. This flexibility makes it ideal for both lightweight tasks like translation and more complex use cases such as dashboard generation or simulation design. Early enterprise adopters have praised its ability to follow instructions accurately while handling complex inputs efficiently. Gemini 3.1 Flash-Lite is currently rolling out in preview within Google AI Studio and Vertex AI for enterprise customers.

LMOps is a stack that allows you to launch production-ready applications for monitoring, model management and more. Portkey is a replacement for OpenAI or any other provider APIs. Portkey allows you to manage engines, parameters and versions. Switch, upgrade, and test models with confidence. View aggregate metrics for your app and users to optimize usage and API costs Protect your user data from malicious attacks and accidental exposure. Receive proactive alerts if things go wrong. Test your models in real-world conditions and deploy the best performers. We have been building apps on top of LLM's APIs for over 2 1/2 years. While building a PoC only took a weekend, bringing it to production and managing it was a hassle! We built Portkey to help you successfully deploy large language models APIs into your applications. We're happy to help you, regardless of whether or not you try Portkey!

Amnic is an innovative FinOps solution that utilizes context-aware AI agents to provide organizations with enhanced visibility and management over their cloud expenditures. By automating the processes involved in cloud cost management, it employs role-specific agents that evaluate usage patterns, identify anomalies, and deliver insights customized for various stakeholders. With robust cloud cost observability features, Amnic allows teams to effectively visualize, analyze, and optimize their infrastructure costs, transforming intricate cloud billing statements into easily understandable actionable data. The tool accelerates cloud financial health assessments, offers insights in natural language, and streamlines reporting processes, thereby minimizing the manual tasks usually associated with FinOps practices. Additionally, its integrated governance mechanisms help track budget variances, ensure proper tagging protocols, and designate ownership, fostering accountability among engineering and finance units. As a result, Amnic not only simplifies financial oversight but also enhances collaborative efforts within organizations.

Altyst is an innovative software solution for commercial real estate underwriting that utilizes artificial intelligence to transform deal documentation into a dynamic and auditable financial model. By allowing users to upload offering memorandums, rent rolls, T-12s, leases, and other relevant documents, Altyst efficiently extracts and organizes essential property data. The system identifies discrepancies and missing information for users to address, while also providing the option to confirm or modify assumptions prior to calculations. It expertly models various financial aspects such as operating cash flow, rent and expense growth, vacancy rates, debt, acquisition and disposition strategies, lease rollovers, different scenarios, and sensitivity analyses across diverse property sectors, including multifamily, office, retail, industrial, and hospitality. The platform calculates critical financial metrics like NOI, cash flow, DSCR, IRR, equity multiple, cash-on-cash return, and overall valuation with detail that links outputs back to the original source documents and assumptions. Users can easily compare multiple scenarios and generate exports in the form of Excel workbooks, underwriting memos, and PDF reports. As a web-based platform that emphasizes self-service, Altyst offers flexible pricing plans beginning at approximately $12 per month, making it accessible for various users. The user-friendly interface and comprehensive functionality cater to the diverse needs of real estate professionals seeking to enhance their underwriting processes.

Trismik serves as a platform for evaluating AI models, aimed at assisting teams in selecting the most suitable large language model tailored to their unique needs by utilizing actual data rather than mere assumptions or standard benchmarks. The platform emphasizes transforming the process of model experimentation into straightforward, evidence-based choices by giving users the ability to test and contrast various models directly with their own datasets, avoiding the pitfalls of public leaderboards or limited manual evaluations. Alongside this, it features innovative tools like QuickCompare, which allows for side-by-side assessments of over 50 models across essential metrics such as quality, cost, and speed, thus rendering trade-offs visible and quantifiable in practical scenarios. Additionally, Trismik employs adaptive evaluation methods inspired by psychometrics, which intelligently select the most informative test cases and automatically assess outputs across multiple dimensions, including factual accuracy, bias, and reliability, ensuring a comprehensive evaluation process. This holistic approach not only enhances the decision-making process but also empowers teams to make informed choices that align with their specific operational requirements.

Pluvo is a decision intelligence and financial planning platform that leverages AI to assist finance and strategy teams in modeling various scenarios, predicting performance, and accelerating data-informed decision-making. By unifying operational and financial data, it enables users to create forecasts, budgets, and adaptable models with straightforward prompts, eliminating the need for complex spreadsheets. The platform prioritizes transparency, ensuring that assumptions, formulas, and reasoning are clearly defined and can be traced back to the original data, allowing teams to confidently validate and explain their outcomes. Furthermore, Pluvo seamlessly integrates with accounting and ERP systems to automatically update real financial data, presenting it in customizable dashboards while continuously monitoring progress against initial forecasts. Additionally, its driver-based modeling capabilities empower businesses to explore different scenarios, assess strategic alternatives, and quickly comprehend the financial implications of operational adjustments. This comprehensive approach not only enhances decision-making but also fosters a deeper understanding of the financial landscape within an organization.

Domino is a comprehensive enterprise AI platform that enables organizations to transform AI initiatives into scalable, production-ready systems. It supports the full AI lifecycle, including data access, model development, deployment, and ongoing management. The platform provides a self-service environment where data scientists can access tools, datasets, and compute resources with built-in governance and security controls. Domino allows teams to build machine learning models, generative AI applications, and intelligent agents using their preferred development environments. It also includes advanced orchestration capabilities to manage workloads across hybrid, multi-cloud, and on-premises infrastructures. Governance features such as model registries, audit trails, and policy enforcement ensure compliance and reproducibility. The platform enhances collaboration by providing a centralized system of record for all AI assets and experiments. Additionally, it helps organizations optimize costs through resource management and usage tracking. Domino is designed to meet enterprise standards for security and regulatory compliance. Ultimately, it empowers businesses to accelerate AI innovation while maintaining operational control and accountability.

Uncover the top AI-generated responses by engaging in comparison, sharing, and voting at thisorthis.ai, a platform designed to simplify the evaluation of different AI models and save you valuable time. You can test various prompts across multiple AI models, analyze their differences, and share your findings in real-time, effectively enhancing your AI strategy through insightful, data-driven comparisons that lead to quicker, informed decisions. As your definitive resource for AI model comparisons, thisorthis.ai offers a seamless side-by-side view of responses generated by different models, allowing you to determine which one delivers the most accurate answers or simply to enjoy exploring the range of available responses. By entering any prompt, you can effortlessly view and compare the outputs of renowned models like GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Flash, and others with just a simple click. Additionally, your participation in voting for the best responses helps to emphasize which models are performing exceptionally well. You can also easily share links to your prompts along with the AI-generated responses with others, fostering a collaborative exploration of AI capabilities. This interactive experience not only enhances your understanding of AI but also connects you with a community of users interested in the evolving landscape of artificial intelligence.

Spanlens is an open-source observability platform licensed under MIT that enables developers to effectively track each interaction their applications have with services like OpenAI, Anthropic, Gemini, Mistral, OpenRouter, Azure OpenAI, or a local Ollama model. The integration process is incredibly simple, requiring just a single line of code to change the client's baseURL to the Spanlens proxy, or by executing "npx @spanlens/cli init," which prompts a wizard to automatically adjust your code. Once integrated, all requests are meticulously logged, capturing details such as the model used, token counts, latency, cost, and the complete prompt and response body, while also seamlessly reconstructing streaming responses. The accompanying dashboard transforms this raw log data into actionable operational insights. Cost tracking functionality allows users to break down expenditures by individual requests, models, and end users, while also distinguishing prompt-cache tokens to provide clarity on actual savings rather than simply the total costs. Additionally, agent tracing presents multi-step workflows visually, using Gantt waterfalls and node-and-edge graphs to emphasize the critical path, enabling developers to pinpoint the slowest dependencies in a fan-out scenario. This comprehensive approach not only enhances visibility but also empowers users to optimize their model interactions for better efficiency and cost management.

AICosts.ai serves as a comprehensive platform for managing AI-related expenses, consolidating billing and usage information from over 50 different providers into a single dashboard. Users can easily upload invoices and data exports in various formats such as PDF, CSV, or JSON, or they can utilize the developer API to send usage events, with the platform efficiently parsing this information into a standardized format without needing any proxy setups or alterations to production requests. It accommodates a wide array of services including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Vertex AI, Cohere, Groq, Hugging Face, Pinecone, RunwayML, Make, Zapier, and n8n. Daily insights break down expenditures by platform, model, and billed units, which encompass tokens, operations, characters, and other specific metrics from providers, enabling users to compare different services and understand the origins of their charges. Additionally, users can set budgets that may either encompass the entire AI landscape or focus on specific platforms or features, while also receiving email notifications whenever their rolling 30-day expenses surpass predetermined thresholds, ensuring they stay informed and within their financial limits. This level of detail and control empowers teams to manage their AI costs more effectively.

CatmanAi utilizes advanced AI and enhanced data to provide outcomes that surpass the accuracy and reliability of conventional manual forecasting methods, allowing businesses to make informed decisions that enhance their volume, value, share, and profitability. By registering for this leading forecasting and analytical platform tailored for the consumer retail sector, you gain insights into consumer trends, true incremental sales across various channels, product formats, geographical regions, and consumer demographics. The platform also enables sensitivity analysis, helping you identify critical assumptions and evaluate potential risks effectively. It elucidates the reasons and mechanisms behind a new product's ability to expand its category and incorporates cutting-edge AI modeling for enhanced precision. With automated and augmented scenario evaluations, you can make optimal decisions based on a comprehensive understanding of all causal factors and their interactions within the marketplace. Furthermore, the tool adapts to evolving market dynamics and business drivers, considering both short-term and long-term impacts, providing a holistic view of your business landscape. Ultimately, this innovative approach empowers businesses to thrive by accurately forecasting growth potential and market trends.

Cost Reports offer user-friendly dashboards that enable sophisticated reporting and filtering of accrued expenses. You can apply filters to observe daily cost patterns by service, business unit, tag, or account. Additionally, you can link intricate logic to meet any reporting requirement. The forecasts come with confidence intervals that update daily in response to your changing infrastructure, allowing you to gauge future costs effectively. Notifications regarding costs and trends can be sent to you via Slack, Teams, or email on a daily, weekly, or monthly schedule. You will also receive alerts for any cost anomalies detected. Autopilot assesses your EC2 workloads and procures three-year, no-upfront reserved instances to help you cut costs. You have the ability to specify which compute categories or regions Autopilot oversees. Furthermore, managing commitments and infrastructure adjustments becomes a seamless process, ensuring you stay on track with your budgetary goals. This way, you maintain full control over your cost management strategy while optimizing resource usage.

Model Playground AI is an online platform that allows users to investigate, contrast, and prototype with more than 150 leading AI models within a cohesive interface. It features two primary modes: Explore for free-form prompt experimentation and Workflows for structured, repeatable tasks, where users can modify parameters such as temperature and max tokens, submit prompts to multiple models at once, and observe results side by side in real time. Additionally, it offers presets and saving capabilities to store settings and chat histories for convenient reproducibility, while API endpoints and a credit-based subscription model facilitate smooth integration into personal applications without hidden fees. With its lightweight, no-code design, the platform accommodates tasks related to text, images, video, and code generation, all from a single dashboard, simplifying the process of evaluating model performance, refining prompts, and speeding up AI-driven initiatives. Furthermore, the user-friendly interface enhances accessibility for both beginners and seasoned developers alike, making it an ideal choice for anyone looking to harness the potential of AI technology.

Monitor expenses, usage, and latency for GPT applications seamlessly with just one line of code. Renowned organizations that leverage OpenAI trust our service. We are expanding our support to include Anthropic, Cohere, Google AI, and additional platforms in the near future. Stay informed about your expenses, usage patterns, and latency metrics. With Helicone, you can easily integrate models like GPT-4 to oversee API requests and visualize outcomes effectively. Gain a comprehensive view of your application through a custom-built dashboard specifically designed for generative AI applications. All your requests can be viewed in a single location, where you can filter them by time, users, and specific attributes. Keep an eye on expenditures associated with each model, user, or conversation to make informed decisions. Leverage this information to enhance your API usage and minimize costs. Additionally, cache requests to decrease latency and expenses, while actively monitoring errors in your application and addressing rate limits and reliability issues using Helicone’s robust features. This way, you can optimize performance and ensure that your applications run smoothly.

Edgee operates as an AI intermediary that integrates seamlessly with your application and various large language model providers, functioning as an intelligence layer at the edge that minimizes prompt size before they are sent to the model, ultimately decreasing token consumption, lowering expenses, and enhancing response times without requiring alterations to your current codebase. Users can access Edgee via a single API that is compatible with OpenAI, allowing it to implement various edge policies, including smart token compression, routing, privacy measures, retries, caching, and financial oversight, before passing the requests to chosen providers like OpenAI, Anthropic, Gemini, xAI, and Mistral. The advanced token compression feature efficiently eliminates unnecessary input tokens while maintaining the meaning and context, which can lead to a substantial reduction of up to 50% in input tokens, making it particularly beneficial for extensive contexts, retrieval-augmented generation (RAG) workflows, and multi-turn conversations. Furthermore, Edgee allows users to label their requests with bespoke metadata, facilitating the monitoring of usage and expenses by different criteria such as features, teams, projects, or environments, and it sends notifications when there is an unexpected increase in spending. This comprehensive solution not only streamlines interactions with AI models but also empowers users to manage costs and optimize their application’s performance effectively.

Cloudgov.ai serves as an intelligent AI-driven FinOps platform designed for ongoing management of costs and policy adherence across various environments, including cloud, multicloud, data systems, containers, and artificial intelligence. By integrating major platforms such as AWS, Azure, Google Cloud, Oracle Cloud, Snowflake, Databricks, Kubernetes, OpenAI, Anthropic, and Gemini into a unified control panel, it enables teams to monitor expenses, allocation, policies, and associated risks in real time. Its Continuous Multicloud Observability feature links accounts, reviews past expenditures, categorizes costs based on region, account, and service, and projects future spending based on historical data. With AI-generated insights, the platform uncovers areas of waste and potential optimization, and its anomaly detection functionality alerts users to unexpected spikes in spending along with their financial implications. Furthermore, it provides ready-to-use Infrastructure as Code snippets for remediation, which allows engineering teams to implement suggested adjustments seamlessly, and integrates with Jira to convert insights and anomalies into actionable tasks for team members, thereby streamlining the workflow for cost management. Overall, Cloudgov.ai empowers organizations to maintain financial control while enhancing efficiency across their cloud operations.

Uncover concealed cloud expenditures and foster ongoing cost-efficiency throughout your entire infrastructure. Empower your team with practical analytics that promote their dedication to perpetual cost optimization. PointFive delves deeper into your cloud setup to uncover innovative savings opportunities. By providing insights tailored to your business context, you gain a comprehensive view at a glance, while easy-to-follow remediation workflows facilitate seamless implementation. Offer stakeholders customized perspectives and cultivate a culture of collective responsibility among your FinOps and engineering teams. Our dedicated research team continually refines our detection algorithms, allowing them to produce fresh recommendations that boost both cost efficiency and performance. Ongoing resource scanning identifies issues promptly to prevent budget overruns, ensuring you can mine your entire cloud architecture and Kubernetes environments for previously overlooked savings. With extensive coverage, your team is equipped to optimize every aspect of your resources and services effectively. This comprehensive approach not only maximizes savings but also enhances overall operational efficiency.

Atlas DEX serves as an advanced decentralized aggregator for cross-chain exchanges, enabling users to effortlessly trade cryptocurrencies across various blockchain networks. By aggregating liquidity, Atlas ensures that users receive the most competitive prices from a variety of decentralized exchanges and automated market makers, which helps to reduce slippage on all transactions. Users can initiate trades for any token across several blockchains, utilizing permissionless bridging technology. The platform smartly divides trades among different liquidity pools to secure the best available rates while minimizing slippage. With the Solana blockchain at its core, Atlas DEX offers rapid transaction speeds and low fees for its users. Currently, it facilitates swaps from Solana to prominent networks including Ethereum, Binance Smart Chain (BSC), and Polygon. Simply connect your wallets, select your preferred trading pairs, and let Atlas DEX manage the rest of the process! Leveraging Solana's Wormhole technology, the platform guarantees a secure and decentralized token bridging experience, enhancing user confidence and satisfaction. Overall, Atlas DEX simplifies the trading experience significantly while maximizing efficiency and effectiveness for cryptocurrency enthusiasts.

Thread Deck is an innovative workspace designed primarily for AI operations, allowing users to organize notes, ideas, and links on a single cohesive canvas while integrating their preferred large language models for execution, testing, and refinement. Users can conveniently place research materials, snippets, and hyperlinks alongside their prompts, maintain tone guidelines, personas, and reusable prompt templates, and connect all elements into a cohesive visual workflow. It meticulously records each model run, monitors token consumption and associated costs, and features a complimentary “LLM Pricing Calculator” to help users estimate their usage and budgeting with various providers, including GPT, Claude, and Gemini. Collaboration is seamlessly integrated; you can invite colleagues, share real-time canvases, evaluate model outputs in a side-by-side format, and develop collective prompt libraries. The overarching aim is to minimize the disorganization often found in notes, browser tabs, and AI discussions, providing a clear canvas where both ideation and generation can occur in harmony. In doing so, Thread Deck empowers users to streamline their AI workflows and enhance productivity across teams.

Transform your budgeting process by replacing cumbersome and error-prone spreadsheets with Yardi Advanced Budgeting & Forecasting, a powerful tool that provides swift and precise budgets, tailored forecasts, and valuations all from one cohesive platform. You can create budgets based on model properties, allowing for easy modifications or deletions of units and records to align with your leasing assumptions. With the flexibility to update your assumptions in real-time and integrate ongoing deals through Yardi Deal Manager, you can streamline your financial planning. This solution enables automatic calculations of future cash flows while determining net present values (NPV) and internal rates of return (IRR) at both the property and portfolio levels. Enhance your acquisition and disposition strategies by exploring limitless "what if" scenarios, making it easier to arrive at informed decisions. Gain access to critical information necessary for executing smart transactions by merging real-time data on properties, leases, and investors with current market leasing assumptions and various other factors. Additionally, generate detailed reports on budget accounts complete with narratives, and effortlessly monitor revenue, lease inducements, and market leasing assumptions among other key metrics. Ultimately, this innovative approach could revolutionize how you manage financial forecasting in your organization.