Best AI Cost Management Software in the Middle East

Around 25 million engineers work across dozens of distinct functions. Engineers are using New Relic as every company is becoming a software company to gather real-time insight and trending data on the performance of their software. This allows them to be more resilient and provide exceptional customer experiences. New Relic is the only platform that offers an all-in one solution. New Relic offers customers a secure cloud for all metrics and events, powerful full-stack analytics tools, and simple, transparent pricing based on usage. New Relic also has curated the largest open source ecosystem in the industry, making it simple for engineers to get started using observability.

CloudZero helps businesses optimize cloud spend with full visibility into costs—so they can reduce wasteful spending and improve their unit economics. Unlike other solutions, we take an engineering-led approach to cost optimization, helping teams understand what drives 100% of their operational cloud spend, empowering them to reduce risk, minimize waste, and maximize profit.

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.

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.

Datadog is the cloud-age monitoring, security, and analytics platform for developers, IT operation teams, security engineers, and business users. Our SaaS platform integrates monitoring of infrastructure, application performance monitoring, and log management to provide unified and real-time monitoring of all our customers' technology stacks. Datadog is used by companies of all sizes and in many industries to enable digital transformation, cloud migration, collaboration among development, operations and security teams, accelerate time-to-market for applications, reduce the time it takes to solve problems, secure applications and infrastructure and understand user behavior to track key business metrics.

Binadox is a multicloud spend optimization solution. It combines SaaS and IaaS management into one solution. * Manage SaaS subscriptions and optimize spend * Cloud (AWS Azure, Azure) spend visibility and overspend prevention * Shadow IT & SaaS: * Cloud Spend Drilldown Analysis & Optimization Recommendation Binadox dashboard provides a view of all SaaS applications within your organization. This includes all authorized users, actual consumption, as well as costs. Get all the information you need to make informed decisions. Multi-cloud monitoring for both AWS and Azure. Proactive granular spend monitoring, and notification to avoid Bill Shocks. Monitoring of major Cloud services like compute, storage, and network You can drill down to the most atomic level, such as a single virtual computer in EC2. Get insights into the cost and usage of each virtual machine. Get actionable optimization suggestions Use an API, Proxy, or Agent to discover SaaS app usage

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.

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.

CloudQuell is an innovative cost management solution tailored for teams managing expenditures that are dispersed across various platforms. It efficiently retrieves AWS billing data on a daily basis via a scoped read-only cross-account IAM role, while also integrating seamlessly with OpenAI, Anthropic, and Snowflake through its dedicated Integrations page. Additionally, the platform offers features such as cost centers, allocation guidelines, tagging systems, and the ability to view costs across multiple accounts, enabling precise attribution of expenses to the respective team or product responsible. With functionalities like anomaly detection, budget tracking, and alert notifications, it proactively identifies issues as they arise, while providing prioritized savings suggestions to help users pinpoint where financial resources can be optimized. Furthermore, every user tier benefits from a weekly email summarizing their accrued costs, ensuring that all teams stay informed about their spending.

Finout streamlines the billing from Cloud Providers, Data Warehouses, and CDNs into a comprehensive single invoice, providing an exceptional overview of your cloud expenses without the need for extensive setup. You can easily track irregularities, access tailored suggestions, and anticipate costs as your business expands. Unlike AWS, which bills based on instances, Finout allows you to focus on the actual costs associated with your pods. By integrating seamlessly without agents, you can leverage your current Datadog or Prometheus setups to gain detailed insights into pod-level spending quickly. Move beyond simply understanding total cloud expenses; instead, focus on the costs tied to your actual usage rather than just payments made. For instance, instead of analyzing EC2 instances and DynamoDB indexes, you can directly observe Kubernetes pods. Moreover, Finout fosters a shared vocabulary across your organization, benefiting not just the DevOps team but the entire company as well. This unified approach enhances collaboration and understanding across departments, leading to more informed financial decisions.

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.

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.

LiteLLM serves as a comprehensive platform that simplifies engagement with more than 100 Large Language Models (LLMs) via a single, cohesive interface. It includes both a Proxy Server (LLM Gateway) and a Python SDK, which allow developers to effectively incorporate a variety of LLMs into their applications without hassle. The Proxy Server provides a centralized approach to management, enabling load balancing, monitoring costs across different projects, and ensuring that input/output formats align with OpenAI standards. Supporting a wide range of providers, this system enhances operational oversight by creating distinct call IDs for each request, which is essential for accurate tracking and logging within various systems. Additionally, developers can utilize pre-configured callbacks to log information with different tools, further enhancing functionality. For enterprise clients, LiteLLM presents a suite of sophisticated features, including Single Sign-On (SSO), comprehensive user management, and dedicated support channels such as Discord and Slack, ensuring that businesses have the resources they need to thrive. This holistic approach not only improves efficiency but also fosters a collaborative environment where innovation can flourish.

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.

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.

AICostGuardian serves as a comprehensive platform for managing AI expenses, enabling organizations to monitor, enhance, and regulate their spending on over 25 different AI service providers through a centralized interface. It meticulously tracks every API interaction with millisecond accuracy, providing instantaneous cost calculations and integrating provider data into comprehensive analytics, automated reporting, forecasting, and visual dashboards. Teams can evaluate spending patterns, benchmark usage against peers, pinpoint areas for cost savings, and leverage machine-learning insights alongside intelligent recommendations to minimize avoidable AI costs. With predictive alerts and anomaly detection features, users receive timely notifications about unusual spikes in usage and potential budget exceedances, while customizable spending thresholds ensure that consumption remains manageable. Additionally, department-specific cost tracking, team performance analytics, detailed permission settings, and role-based access facilitate clearer ownership accountability and regulation of AI resource utilization throughout the organization, ensuring informed decision-making and strategic oversight. As organizations increasingly adopt AI technologies, AICostGuardian stands out as a vital tool for fostering financial prudence and operational efficiency.

SatGate functions as a governance and accountability layer for AI agents, regulating their access, expenditure, delegation, and execution capabilities prior to any interaction with APIs, models, MCP tools, or external paid services. Operating as an HTTP reverse proxy and MCP proxy, it implements scoped authority, individual agent budgets, routing policies, and next-request revocation directly within the request workflow. Agents initiate their access by authenticating through established systems like Kubernetes, AWS, or OIDC, after which SatGate Mint converts that identity into a cryptographically signed Macaroon that delineates limits regarding scope, budget, expiration, and delegation depth. The architecture ensures that permissions can only tighten as requests traverse through agent chains, effectively stopping sub-agents from exceeding their authorized capabilities. In addition, the Observe mode tracks requests and analyzes resource usage categorized by agent, team, tool, route, and cost center while preserving existing workflows, whereas the Control mode imposes strict budgetary limits to prevent unauthorized or costly actions from being executed. This dual functionality allows organizations to maintain oversight while granting necessary freedoms to their AI agents.

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.

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.

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.

TokenAtlas is an innovative platform focused on AI FinOps and cost intelligence, designed to assist teams in comprehending, predicting, and managing AI expenses before they escalate. Users can define their workload by providing details such as model specifications, token input and output volumes, request frequencies, and anticipated growth, allowing TokenAtlas to evaluate the scenario against a curated list of API pricing. The cost modeling dashboard consolidates all configured workloads into a single interface, while the model comparison feature juxtaposes various provider and model options using clear and transparent assumptions. Additionally, the what-if scenario planning tool assesses the potential financial impact of introducing a new prompt, switching models, modifying retrieval pipelines, or increasing traffic prior to actual implementation. Moreover, cost risk analysis pinpoints the workloads that are particularly vulnerable to fluctuations in volume, prompt size, or model selection, while benchmark comparisons reveal how the configured model mix stands in relation to standard AI product and infrastructure profiles. This comprehensive approach empowers teams to make informed financial decisions, enhancing overall efficiency and cost-effectiveness in AI operations.

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.

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.

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.

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.