Alternatives to Langfuse

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.

Literal AI is an open-source platform that helps engineering and product teams develop production-grade Large Language Model applications. It provides a suite for observability and evaluation, as well as analytics. This allows for efficient tracking, optimization and integration of prompt version. The key features are multimodal logging encompassing audio, video, and vision, prompt management, with versioning and testing capabilities, as well as a prompt playground to test multiple LLM providers. Literal AI integrates seamlessly into various LLM frameworks and AI providers, including OpenAI, LangChain and LlamaIndex. It also provides SDKs for Python and TypeScript to instrument code. The platform supports the creation and execution of experiments against datasets to facilitate continuous improvement in LLM applications.

Langtail is a cloud-based development tool designed to streamline the debugging, testing, deployment, and monitoring of LLM-powered applications. The platform provides a no-code interface for debugging prompts, adjusting model parameters, and conducting thorough LLM tests to prevent unexpected behavior when prompts or models are updated. Langtail is tailored for LLM testing, including chatbot evaluations and ensuring reliable AI test prompts. Key features of Langtail allow teams to: • Perform in-depth testing of LLM models to identify and resolve issues before production deployment. • Easily deploy prompts as API endpoints for smooth integration into workflows. • Track model performance in real-time to maintain consistent results in production environments. • Implement advanced AI firewall functionality to control and protect AI interactions. Langtail is the go-to solution for teams aiming to maintain the quality, reliability, and security of their AI and LLM-based applications.

The first platform designed for prompt engineers. Log OpenAI requests, track usage history, visual manage prompt templates, and track performance. Manage Never forget one good prompt. GPT in Prod, done right. Trusted by more than 1,000 engineers to monitor API usage and version prompts. Your prompts can be used in production. Click "log in" to create an account on PromptLayer. Once you have logged in, click on the button to create an API Key and save it in a secure place. After you have made your first few requests, the API key should be visible in the PromptLayer dashboard. LangChain can be used with PromptLayer. LangChain is a popular Python library that assists in the development and maintenance of LLM applications. It offers many useful features such as memory, agents, chains, and agents. Our Python wrapper library, which can be installed with pip, is the best way to access PromptLayer at this time.

AI engineering does not have to be a mystery. You can get full visibility using tools for tracing and evaluation, prompt management and more. HoneyHive is a platform for AI observability, evaluation and team collaboration that helps teams build reliable generative AI applications. It provides tools for evaluating and testing AI models and monitoring them, allowing engineers, product managers and domain experts to work together effectively. Measure the quality of large test suites in order to identify improvements and regressions at each iteration. Track usage, feedback and quality at a large scale to identify issues and drive continuous improvements. HoneyHive offers flexibility and scalability for diverse organizational needs. It supports integration with different model providers and frameworks. It is ideal for teams who want to ensure the performance and quality of their AI agents. It provides a unified platform that allows for evaluation, monitoring and prompt management.

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!

Pezzo is an open-source LLMOps tool for developers and teams. With just two lines of code you can monitor and troubleshoot your AI operations. You can also collaborate and manage all your prompts from one place.

TruLens, an open-source Python Library, is designed to evaluate and track Large Language Model applications. It offers fine-grained instruments, feedback functions and a user-interface to compare and iterate app versions. This facilitates rapid development and improvement of LLM based applications. Tools that allow scalable evaluation of the inputs, outputs and intermediate results of LLM applications. Instrumentation that is fine-grained and stack-agnostic, and comprehensive evaluations can help identify failure modes. A simple interface allows developers to compare versions of their application, facilitating informed decisions and optimization. TruLens supports a variety of use cases, such as question-answering and summarization. It also supports retrieval-augmented generation and agent-based apps.

Phoenix is a free, open-source library for observability. It was designed to be used for experimentation, evaluation and troubleshooting. It allows AI engineers to visualize their data quickly, evaluate performance, track issues, and export the data to improve. Phoenix was built by Arize AI and a group of core contributors. Arize AI is the company behind AI Observability Platform, an industry-leading AI platform. Phoenix uses OpenTelemetry, OpenInference, and other instrumentation. The main Phoenix package arize-phoenix. We offer a variety of helper packages to suit specific use cases. Our semantic layer adds LLM telemetry into OpenTelemetry. Automatically instrumenting popular package. Phoenix's open source library supports tracing AI applications via manual instrumentation, or through integrations LlamaIndex Langchain OpenAI and others. LLM tracing records requests' paths as they propagate across multiple steps or components in an LLM application.

Traceloop is an observability platform that allows you to monitor, debug and test the output quality from Large Language Models. It provides real-time alerts when unexpected output quality changes occur, execution tracing of every request and the ability to roll out changes to prompts and models in a gradual manner. Developers can debug issues directly from production in their Integrated Development Environment. Traceloop integrates seamlessly with the OpenLLMetry SDK, supporting multiple programming languages including Python, JavaScript/TypeScript, Go, and Ruby. The platform offers a wide range of semantic, syntax, safety and structural metrics for assessing LLM outputs. These include QA relevance, faithfulness and text quality. It also includes redundancy detection and focus assessment.

The prompt engineering platform allows you to experiment with different prompt versions. You can also evaluate and compare prompts in a series of tests, optimize prompts by one click, share and more. Optimize your AI development workflow. Key features that help you identify and get the best prompts for production use cases. Evaluation allows for a side-by-side comparison between prompts in test cases. Import test cases from CSV and define custom metrics for evaluation. Automatic template and prompt optimization can improve LLM results. View and manage all versions of the prompt and create OpenAI Functions. You can access all your prompts programmatically. This includes observability and analytics. Calculate the cost, latency and effectiveness of each prompt. Parea can help you improve your prompt engineering workflow. Parea helps developers improve the performance of LLM apps by implementing rigorous testing and versioning.

With a suite observability tools, you can confidently evaluate, test and ship LLM apps across your development and production lifecycle. Log traces and spans. Define and compute evaluation metrics. Score LLM outputs. Compare performance between app versions. Record, sort, find, and understand every step that your LLM app makes to generate a result. You can manually annotate and compare LLM results in a table. Log traces in development and production. Run experiments using different prompts, and evaluate them against a test collection. You can choose and run preconfigured evaluation metrics, or create your own using our SDK library. Consult the built-in LLM judges to help you with complex issues such as hallucination detection, factuality and moderation. Opik LLM unit tests built on PyTest provide reliable performance baselines. Build comprehensive test suites for every deployment to evaluate your entire LLM pipe-line.

Work on prompts together with your team. Share templates and data, and manage all API fees on a single platform. Write complex prompts easily, iterate and compare the output quality. Quartzite's superior Markdown Editor allows you to compose complex prompts, save a draft and submit the completed document. Test different models and variations to improve your prompts. Switch to GPT pricing that is based on pay-per-use and keep track of all your spending within the app. Stop writing the same prompts repeatedly. Create your own library of templates or use the default. We are constantly integrating the best models. You can toggle them on and off according to your needs. Fill templates with variables, or import CSV files to create multiple versions. Download your prompts, completions and other data in different file formats to use later. Quartzite AI communicates with OpenAI directly, and your data will be stored locally on your browser to ensure your privacy.

MLflow is an open-source platform that manages the ML lifecycle. It includes experimentation, reproducibility and deployment. There is also a central model registry. MLflow currently has four components. Record and query experiments: data, code, config, results. Data science code can be packaged in a format that can be reproduced on any platform. Machine learning models can be deployed in a variety of environments. A central repository can store, annotate and discover models, as well as manage them. The MLflow Tracking component provides an API and UI to log parameters, code versions and metrics. It can also be used to visualize the results later. MLflow Tracking allows you to log and query experiments using Python REST, R API, Java API APIs, and REST. An MLflow Project is a way to package data science code in a reusable, reproducible manner. It is based primarily upon conventions. The Projects component also includes an API and command line tools to run projects.

Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.

PromptHub allows you to test, collaborate, version and deploy prompts from a single location. Use variables to simplify prompt creation and stop copying and pasting. Say goodbye to spreadsheets and compare outputs easily when tweaking prompts. Batch testing allows you to test your datasets, and prompts, at scale. Test different models, parameters, and variables to ensure consistency. Test different models, system messaging, or chat templates. Commit prompts, branch out, and collaborate seamlessly. We detect prompts changes so you can concentrate on outputs. Review changes in a team setting, approve new versions and keep everyone on track. Monitor requests, costs and latencies easily. PromptHub allows you to easily test, collaborate, and version prompts. With our GitHub-style collaboration and versioning, it's easy to iterate and store your prompts in one place.

You can innovate faster in the cloud, improve user experience and future-proof applications. Splunk is designed for cloud-native enterprises and helps you solve current problems. Splunk helps you detect any problem before it becomes a customer problem. Our AI-driven Directed Problemshooting reduces MTTR. Flexible, open-source instrumentation eliminates lock-in. Optimize performance by seeing all of your application and using AI-driven analytics. You must observe everything in order to deliver an excellent end-user experience. NoSample™, full-fidelity trace ingestion allows you to leverage all your trace data and identify any anomalies. Directed Troubleshooting reduces MTTR to quickly identify service dependencies, correlations with the underlying infrastructure, and root-cause errors mapping. You can break down and examine any transaction by any dimension or metric. You can quickly and easily see how your application behaves in different regions, hosts or versions.

SigNoz can be used as an open-source alternative to Datadog or New Relic. A single tool that can handle all your observability requirements, including APM, logs and metrics, exceptions and alerts, dashboards, and dashboards. You don't have to manage multiple tools. You can use the powerful query builder and great charts that come with the software to dig deeper into data. By using an open-source standard, you are not locked into a vendor. OpenTelemetry's auto-instrumentation libraries can help you get started quickly and with minimal code changes. OpenTelemetry provides a single-stop solution to all your telemetry requirements. A single standard for telemetry signals increases developer productivity and consistency within teams. Write queries for all telemetry signals. Apply filters and formulas and run aggregates to gain deeper insights. SigNoz uses ClickHouse, a fast open source distributed columnar database. Ingestion and aggregates are lightning fast.

Entry Point AI is a modern AI optimization platform that optimizes proprietary and open-source language models. Manage prompts and fine-tunes in one place. We make it easy to fine-tune models when you reach the limits. Fine-tuning involves showing a model what to do, not telling it. It works in conjunction with prompt engineering and retrieval augmented generation (RAG) in order to maximize the potential of AI models. Fine-tuning your prompts can help you improve their quality. Imagine it as an upgrade to a few-shot model that incorporates the examples. You can train a model to perform at the same level as a high-quality model for simpler tasks. This will reduce latency and costs. For safety, to protect the brand, or to get the formatting correct, train your model to not respond in a certain way to users. Add examples to your dataset to cover edge cases and guide model behavior.

DeepEval is an open-source, easy-to-use framework for evaluating large-language-model systems. It is similar Pytest, but is specialized for unit-testing LLM outputs. DeepEval incorporates research to evaluate LLM results based on metrics like G-Eval (hallucination), answer relevancy, RAGAS etc. This uses LLMs as well as various other NLP models which run locally on your computer for evaluation. DeepEval can handle any implementation, whether it's RAG, fine-tuning or LangChain or LlamaIndex. It allows you to easily determine the best hyperparameters for your RAG pipeline. You can also prevent drifting and even migrate from OpenAI to your own Llama2 without any worries. The framework integrates seamlessly with popular frameworks and supports synthetic dataset generation using advanced evolution techniques. It also allows for efficient benchmarking and optimizing of LLM systems.

It's not enough to just look at a few examples. To get actionable insights about how to improve your models, gather feedback from end-users at large. With the GPT improvement engine, you can easily A/B test models. You can only go so far with prompts. Fine-tuning your best data will produce better results. No coding or data science required. Integration in one line of code You can experiment with ChatGPT, Claude and other language model providers without having to touch it again. If you have the right tools to customize models for your customers, you can build innovative and defensible products on top APIs. Copy AI allows you to fine tune models based on the best data. This will allow you to save money and give you a competitive edge. This technology allows for magical product experiences that delight more than 2 million users.

ChainForge is a visual programming environment that is open-source and designed for large language model evaluation. It allows users to evaluate the robustness and accuracy of text-generation models and prompts beyond anecdotal data. Test prompt ideas and variations simultaneously across multiple LLMs in order to identify the most efficient combinations. Evaluate response quality for different prompts, models and settings to determine the optimal configuration. Set up evaluation metrics, and visualize results for prompts, parameters and models. This will facilitate data-driven decisions. Manage multiple conversations at once, template follow-ups, and inspect the outputs to refine interactions. ChainForge supports a variety of model providers including OpenAI HuggingFace Anthropic Google PaLM2, Azure OpenAI Endpoints and locally hosted models such as Alpaca and Llama. Users can modify model settings and use visualization nodes.

DagsHub, a collaborative platform for data scientists and machine-learning engineers, is designed to streamline and manage their projects. It integrates code and data, experiments and models in a unified environment to facilitate efficient project management and collaboration. The user-friendly interface includes features such as dataset management, experiment tracker, model registry, data and model lineage and model registry. DagsHub integrates seamlessly with popular MLOps software, allowing users the ability to leverage their existing workflows. DagsHub improves machine learning development efficiency, transparency, and reproducibility by providing a central hub for all project elements. DagsHub, a platform for AI/ML developers, allows you to manage and collaborate with your data, models and experiments alongside your code. DagsHub is designed to handle unstructured data, such as text, images, audio files, medical imaging and binary files.

Weights & Biases allows for experiment tracking, hyperparameter optimization and model and dataset versioning. With just 5 lines of code, you can track, compare, and visualise ML experiments. Add a few lines of code to your script and you'll be able to see live updates to your dashboard each time you train a different version of your model. Our hyperparameter search tool is scalable to a massive scale, allowing you to optimize models. Sweeps plug into your existing infrastructure and are lightweight. Save all the details of your machine learning pipeline, including data preparation, data versions, training and evaluation. It's easier than ever to share project updates. Add experiment logging to your script in a matter of minutes. Our lightweight integration is compatible with any Python script. W&B Weave helps developers build and iterate their AI applications with confidence.

Manage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders.

The most widely used observability platform, built on the ELK Stack, is the best choice. It converges silos and delivers unified visibility and actionable insight. All your observability data must be in one stack to effectively monitor and gain insight across distributed systems. Unify all data from the application, infrastructure, user, and other sources to reduce silos and improve alerting and observability. Unified solution that combines unlimited telemetry data collection with search-powered problem resolution for optimal operational and business outcomes. Converge data silos with the ingesting of all your telemetry data from any source, in an open, extensible and scalable platform. Automated anomaly detection powered with machine learning and rich data analysis can speed up problem resolution.

Telemetry that is portable, ubiquitous, and high-quality to allow effective observation. OpenTelemetry is an open-source collection of APIs and SDKs. It can be used to instrument, generate logs, logs, or traces telemetry data to analyze the performance and behavior of your software. OpenTelemetry can be used in many languages. You can create and collect telemetry data using your software and services, and then forward them to various analysis tools. OpenTelemetry can be integrated with popular frameworks and libraries like ASP.NET Core Express, Quarkus, Spring, and ASP.NET Core. Integration is as easy as writing a few lines. OpenTelemetry is 100% free and open source. It is supported by industry leaders in observability.

Create and version control your prompts. Retrieve prompts using an API generated automatically. Automate the end-to-end LLM test before updating your prompts in production. Let your engineers and industry specialists collaborate on the same platform. Let your industry experts and prompt engineers test, iterate and collaborate on the same platform, without any programming knowledge. You can run an unlimited number of test cases with our testing suite to ensure the quality and reliability of your prompt. Check for issues, edge-cases, and more. Our suite of prompts is the most complex. Use Git features to manage your prompts. Create a repository and multiple branches for each project to iterate your prompts. Commit changes and test them on a separate system. Revert to an earlier version with ease. Our real-time APIs allow you to update your prompt in real time with just one click.

Meet Ape, our first AI prompt engineer. Equipped with tracing and dataset curation. Batch testing, evals, and evalus. Ape achieved an impressive 93% in the GSM8K benchmark. This is higher than DSPy (86%), and base LLMs (70%) Continuously optimize prompts by using real-world data. Integrating CI/CD can prevent performance regression. Human-in-the loop with feedback and scoring. Ape uses the Weavel SDK in order to automatically log your dataset and add LLM generation as you use it. This allows for seamless integration and continuous improvements specific to your use cases. Ape automatically generates evaluation code and relies on LLMs to be impartial judges for complex tasks. This streamlines your assessment process while ensuring accurate and nuanced performance metrics. Ape is reliable because it works under your guidance and feedback. Ape will improve if you send in scores and tips. Equipped with logging and testing for LLM applications.

You can add logs, metrics, and traces to production or staging directly from your IDE/CLI, in real time and on-demand. Lightrun can help you increase productivity and ensure 100% code-level observability. Lightrun allows you to insert logs and metrics even when the service is in progress. You can debug monolith microservices like Kubernetes and Docker Swarm, ECS and Big Data workers, as well as serverless. Quickly add a logline, instrument a measurement, or place a snapshot that can be taken on-demand. There is no need to recreate the production environment or redeploy. Once instrumentation has been invoked, data is printed to your log analysis tool, your editor, or an APM of choice. To analyze code behavior and find bottlenecks or errors, you can stop the running process. You can easily add large numbers of logs and snapshots, counters or timers to your program. The system won't be stopped or broken. Spend less time debugging, and more time programming. Debugging is done without the need to restart, redeploying, or reproduce.

With confidence, collaborate on prompts, monitor and evaluate LLM apps. Agenta is an integrated platform that allows teams to build robust LLM applications quickly. Create a playground where your team can experiment together. Comparing different prompts, embeddings, and models in a systematic way before going into production is key. Share a link with the rest of your team to get human feedback. Agenta is compatible with all frameworks, including Langchain, Lama Index and others. Model providers (OpenAI, Cohere, Huggingface, self-hosted, etc.). You can see the costs, latency and chain of calls for your LLM app. You can create simple LLM applications directly from the UI. If you want to create customized applications, then you will need to use Python to write the code. Agenta is model-agnostic, and works with any model provider or framework. Our SDK is currently only available in Python.

Squash latencies and detect bottlenecks. Debug errors. Join the rapidly growing community of businesses around the globe that are achieving a 20X ROI and value by adopting KloudMate compared to other observability platforms. Monitor critical metrics and dependencies quickly, and detect anomalies using alarms and issue trackers. Locate 'breakpoints' within your application development lifecycle to fix issues proactively. View service maps of every component within your application and discover intricate dependencies and interconnections. Track every request and operation to gain detailed visibility into performance metrics and execution paths. Unified Infrastructure Monitoring capabilities can be used to monitor metrics, regardless of whether it is a multi-cloud, private, hybrid or hybrid architecture. A complete system view will help you debug faster and more precisely. Identify and solve issues faster.

Manage source code context, and generate optimized prompts. Ship with ChatGPT or Claude. 16x Prompt is a tool that helps developers manage the source code context, and provides prompts for complex coding tasks in existing codebases. Enter your own API Key to use APIs such as OpenAI, Anthropic Azure OpenAI OpenRouter or 3rd-party services that are compatible with OpenAI APIs, like Ollama and OxyAPI. APIs prevent your code from leaking to OpenAI and Anthropic training data. Compare the output code of different LLMs (for example, GPT-4o & Claude 3.5 Sonnet), side-by-side, to determine which is best for your application. Create and save your best prompts to be used across different tech stacks such as Next.js Python and SQL. To get the best results, fine-tune your prompt using various optimization settings. Workspaces allow you to manage multiple repositories, projects and workspaces in one place.

Open source continuous profiling. Find and debug the most painful performance issues in code, infrastructure, and CI/CD pipelines. You can tag your data according to the dimensions that are important to your organization. You can store large volumes of high-cardinality profiling information efficiently and cheaply. FlameQL allows you to create custom queries that select and aggregate profiles quickly for easy analysis. Our suite of profiling software allows you to analyze application performance profiles. Understand CPU and memory resource usage at any time to identify performance issues before your customers do. Store, analyze, and collect profiles from external profiling tools. Link to your OpenTelemetry trace data and get request specific or span specific profiles to enhance other observability information like traces and logs

Langtrace is a free observability tool which collects and analyses metrics and traces to help you improve LLM apps. Langtrace provides the highest level security. Our cloud platform is SOC 2 Type II-certified, ensuring the highest level of protection for your data. Supports popular LLMs and frameworks. Langtrace is self-hosted, and it supports OpenTelemetry traces that can be ingested into any observability tools of your choice. This means there is no vendor lock-in. With traces and logs that span the framework, vectorDB and LLM requests, you can gain visibility and insights in your entire ML pipeline. Use golden datasets to create and annotate traced LLM interactions and continuously test and improve your AI applications. Langtrace has built-in heuristics, statistical and model-based analyses to support this process.

Akita is designed for developers and SREs. It provides observability without the complexity. No code changes. Frameworks are not required. Simply deploy, observe, learn. You can solve problems faster and ship faster. Akita helps you to identify the root cause of problems by modeling API behavior, and mapping how services interact with each other. Akita creates models of API endpoints and their behavior to help you discover breaking changes quicker. Akita shows you what has changed in your service graph to help you debug latency errors and other errors. You can see what services are available in your system without having to add each service individually. Akita watches API traffic passively, making it possible for Akita to be run across your services without having to change code or use a proxy.

Prefix with OpenTelemetry is a great way to optimize app performance. OTel Prefix, the latest open-source observability standard, streamlines application development by allowing for universal telemetry data input, unmatched observability and extended language support. OTel Prefix gives developers the power of OpenTelemetry, supercharging the performance optimization of your entire DevOps Team. OTel Prefix's unmatched observability in new technologies, frameworks and architectures simplifies code development, app creation and ongoing performance optimization of your apps for you and your team. Summary Dashboards are available, as well as distributed tracing and smart suggestions. Prefix also offers developers the ability to jump between logs and traces.

Use tools to bring LLM-powered features into production, including tools for rapid engineering, semantic searching, version control, quantitative testing, and performance monitoring. Compatible with all major LLM providers. Develop an MVP quickly by experimenting with various prompts, parameters and even LLM providers. Vellum is a low-latency and highly reliable proxy for LLM providers. This allows you to make version controlled changes to your prompts without needing to change any code. Vellum collects inputs, outputs and user feedback. These data are used to build valuable testing datasets which can be used to verify future changes before going live. Include dynamically company-specific context to your prompts, without managing your own semantic searching infrastructure.

Our pre-built dashboards work and allow you to monitor your cluster's health and troubleshoot problems faster. Our clear pricing makes it easy for you to get started right away. ContainIQ deploys three agents inside your cluster. One replica deployment collects metrics and events using Kubernetes API. Two additional daemon sets collect logs from all your pods/containers. The second collects latency information for each pod on that node. Monitor latency by microservice or by path, including p95 and p99, average and RPS. It works instantly without the need for middleware or application packages. Set up alerts for significant changes. You can search functionality, filter by date range and view data over time. View all incoming and outgoing requests along with metadata. Graph P99,P95, average latency and error rate over time for each URL. For debugging problems when they arise, correlate logs are useful.

Promptfoo identifies and eliminates LLM risks prior to their being shipped into production. Its founders are experienced in launching and scaling AI for over 100M users, using automated red-teaming, testing, and compliance to overcome security, regulatory, and compliance issues. Promptfoo is the most widely used tool in this area, with more than 20,000 users, thanks to its open source, developer first approach. Custom probes that are tailored to your application and identify the failures you care about. Not just generic jailbreaks or prompt injections. With a command-line, live reloads and caching, you can move quickly. No SDKs or cloud dependencies. Open-source software used by teams that serve millions of users, and supported by a vibrant community. Build RAGs, models and prompts that are reliable, based on benchmarks that are specific to your use-case. Automated red teaming and pentesting will help you secure your apps. Accelerate evaluations by using caching, concurrency and live reloading.

SaaS-delivered, observability that extends visibility across cloud native, on-prem and hybrid technology stacks. SolarWinds Observability provides unified and comprehensive visibility of cloud-native, hybrid, custom and commercial applications, as well as on-premises and hybrid custom and business applications, to help ensure optimal service level and user satisfaction. For commercial and internally written applications. Unified code-level problem-solving with transaction tracing and code profiling, combined with end-user experience insights from synthetic and real-user monitoring. Deep database performance monitoring. Full visibility of open-source databases such as MySQL®, PostgreSQL®, MongoDB®, Azure SQL®, Amazon Aurora®, Redis®, and Azure SQL® can increase system performance and team efficiency while reducing infrastructure costs.

Release high-quality LLM applications quickly without compromising testing. Never let the subjective and complex nature of LLM interactions hold you back. Generative AI produces subjective results. A subject matter expert must manually check a generated text to determine its quality. You probably know if you're developing an LLM application that you cannot release it without addressing numerous constraints and edge cases. Hallucinations and other issues, such as incorrect answers, bias and deviations from policy, harmful material, and others, need to be identified, investigated, and mitigated both before and after the app is released. Deepchecks allows you to automate your evaluation process. You will receive "estimated annotations", which you can only override if necessary. Our LLM product has been extensively tested and is robust. It is used by more than 1000 companies and integrated into over 300 open source projects. Validate machine-learning models and data in the research and production phases with minimal effort.

OpenPipe provides fine-tuning for developers. Keep all your models, datasets, and evaluations in one place. New models can be trained with a click of a mouse. Automatically record LLM responses and requests. Create datasets using your captured data. Train multiple base models using the same dataset. We can scale your model to millions of requests on our managed endpoints. Write evaluations and compare outputs of models side by side. You only need to change a few lines of code. OpenPipe API Key can be added to your Python or Javascript OpenAI SDK. Custom tags make your data searchable. Small, specialized models are much cheaper to run than large, multipurpose LLMs. Replace prompts in minutes instead of weeks. Mistral and Llama 2 models that are fine-tuned consistently outperform GPT-4-1106 Turbo, at a fraction the cost. Many of the base models that we use are open-source. You can download your own weights at any time when you fine-tune Mistral or Llama 2.

PromptDrive helps teams adopt AI by bringing together all their prompts, chats and teammates in one workspace. Our web app allows you to create prompts quickly. Add context by adding notes, selecting a platform and selecting a folder. You can leave comments for your team to use and improve prompts. PromptDrive allows you to run and collaborate on ChatGPT Claude and Gemini without leaving the app. Add your API keys, select your model, then start prompting. Iterate until you get the desired response. Organize them however you like. Our prompt management tool includes a built-in search, so you can easily find, copy and execute prompts. Add variables to your workflow when you are dealing with repetitive prompts. We make it simple to share your prompts. Each folder and prompt contains a unique URL, which allows you to share it with anyone publicly or privately. Use our extension to find and use prompts quickly when you need them.

Prompt Refine helps you run better prompt experiments. Even small changes to a prompt may produce very different results. Prompt Refine allows you to run and iterate prompts. Each time you run a command, it is added to your history. You can see the differences between previous runs and all of their details. Organize them into groups and share with colleagues and friends. Export your prompt runs to a CSV file for further analysis when you're finished testing. Prompt Refine allows you to create generative prompts which guide users to formulate concise and specific prompts. This allows for more meaningful interactions between AI models. Prompt refine will enhance your prompt interactions, allowing you to unlock the full potential of AI.

Vidura will elevate your AI experience. All your AI prompts can be composed and run in one place. Not only generate AI responses but also organize, share and export them. Vidura is an AI prompt manager that simplifies the process of creating and managing prompts in generative AI platforms such as text to text, text to image, text to speech, and text music. The output of generative AI isn't always perfect. It's usually experimental and requires multiple attempts to achieve the desired result. Even then, the response must be edited & improved before it is used. Vidura is the one-stop solution to manage your prompts and responses. Vidura was designed as a productivity tool to help users of generative AI. We believe that you need a platform where you can incrementally learn to communicate with AI. Vidura is a platform that is community-driven, where you can share with others your best prompts without leaving Vidura. You can also learn about prompting from others and discover new prompts.

Narrow AI: Remove the Engineer from Prompt Engineering Narrow AI automatically writes, monitors and optimizes prompts on any model. This allows you to ship AI features at a fractional cost. Maximize quality and minimize costs Reduce AI costs by 95% using cheaper models Automated Prompt Optimizer: Improve accuracy - Achieve faster response times with lower latency models Test new models within minutes, not weeks - Compare the performance of LLMs quickly - Benchmarks on cost and latency for each model - Deploy the optimal model for your usage case Ship LLM features up to 10x faster - Automatically generate expert level prompts - Adapt prompts as new models are released - Optimize prompts in terms of quality, cost and time

Turn your imagination into art by being creative with your prompts. Use promptoMANIA to create unique AI art by adding details to your prompts. Use the Generic Prompt Builder for DALL-E2, Disco Diffusion NightCafe wombo.art Craiyon or any other diffusion-model-based AI art generator. promptoMANIA can be downloaded for free. Check out CF Spark if you want to get started with AI. promptoMANIA does not have any affiliation with Midjourney or Stability.ai. You can learn to prompt today by using our interactive tutorials. Create detailed prompts instantly for AI art.

CometLLM allows you to visualize and log your LLM chains and prompts. CometLLM can be used to identify effective prompting strategies, streamline troubleshooting and ensure reproducible workflows. Log your prompts, responses, variables, timestamps, duration, and metadata. Visualize your responses and prompts in the UI. Log your chain execution to the level you require. Visualize your chain in the UI. OpenAI chat models automatically track your prompts. Track and analyze feedback from users. Compare your prompts in the UI. Comet LLM Projects are designed to help you perform smart analysis of logged prompt engineering workflows. Each column header corresponds with a metadata attribute that was logged in the LLM Project, so the exact list can vary between projects.

Learn how to harness the power of AI to help you and your team achieve more in less time with less effort. Easy, fast access to the best commercial AIs and open source LLMs. The most intuitive way to manage and use your favorite prompts for your daily work in Expanse or any other piece of software installed on your OS. Create your own team of AI workers and specialists to gain access to deep knowledge and assistance on demand. Actions are reusable instructions that can be used for everyday work and tedious tasks. They simplify the use of AI. Create and refine roles, actions and snippets easily. Expande watches context to suggest the best prompt for the task. Share your prompts to your team or the entire world. Elegantly designed, this software makes AI work simple, fast, and secure. There are shortcuts for everything. Integrate the most powerful models including open source AI.