Alternatives to TruLens
Compare TruLens alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to TruLens in 2026. Compare features, ratings, user reviews, pricing, and more from TruLens competitors and alternatives in order to make an informed decision for your business.
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1
Gloo AI Gateway
Solo.io
Gloo AI Gateway by Solo.io is a cloud-native solution designed to manage AI applications with enhanced security, control, and observability. Built on the Envoy Proxy and Kubernetes Gateway API, Gloo AI Gateway enables seamless integration of large language models (LLMs) and AI-driven services across cloud environments. It offers features like prompt management, fine-grained access control, and real-time analytics to monitor and optimize AI consumption. The platform also includes safeguards to protect against abuse and ensure model security, improving both model performance and operational efficiency in AI-powered applications. -
2
Selene 1
atla
Atla's Selene 1 API offers state-of-the-art AI evaluation models, enabling developers to define custom evaluation criteria and obtain precise judgments on their AI applications' performance. Selene outperforms frontier models on commonly used evaluation benchmarks, ensuring accurate and reliable assessments. Users can customize evaluations to their specific use cases through the Alignment Platform, allowing for fine-grained analysis and tailored scoring formats. The API provides actionable critiques alongside accurate evaluation scores, facilitating seamless integration into existing workflows. Pre-built metrics, such as relevance, correctness, helpfulness, faithfulness, logical coherence, and conciseness, are available to address common evaluation scenarios, including detecting hallucinations in retrieval-augmented generation applications or comparing outputs to ground truth data. -
3
doteval
doteval
doteval is an AI-assisted evaluation workspace that simplifies the creation of high-signal evaluations, alignment of LLM judges, and definition of rewards for reinforcement learning, all within a single platform. It offers a Cursor-like experience to edit evaluations-as-code against a YAML schema, enabling users to version evaluations across checkpoints, replace manual effort with AI-generated diffs, and compare evaluation runs on tight execution loops to align them with proprietary data. doteval supports the specification of fine-grained rubrics and aligned graders, facilitating rapid iteration and high-quality evaluation datasets. Users can confidently determine model upgrades or prompt improvements and export specifications for reinforcement learning training. It is designed to accelerate the evaluation and reward creation process by 10 to 100 times, making it a valuable tool for frontier AI teams benchmarking complex model tasks. -
4
Athina AI
Athina AI
Athina is a collaborative AI development platform that enables teams to build, test, and monitor AI applications efficiently. It offers features such as prompt management, evaluation tools, dataset handling, and observability, all designed to streamline the development of reliable AI systems. Athina supports integration with various models and services, including custom models, and ensures data privacy through fine-grained access controls and self-hosted deployment options. The platform is SOC-2 Type 2 compliant, providing a secure environment for AI development. Athina's user-friendly interface allows both technical and non-technical team members to collaborate effectively, accelerating the deployment of AI features.Starting Price: Free -
5
Prompt flow
Microsoft
Prompt Flow is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, and evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality. With Prompt Flow, you can create flows that link LLMs, prompts, Python code, and other tools together in an executable workflow. It allows for debugging and iteration of flows, especially tracing interactions with LLMs with ease. You can evaluate your flows, calculate quality and performance metrics with larger datasets, and integrate the testing and evaluation into your CI/CD system to ensure quality. Deployment of flows to the serving platform of your choice or integration into your app’s code base is made easy. Additionally, collaboration with your team is facilitated by leveraging the cloud version of Prompt Flow in Azure AI. -
6
Literal AI
Literal AI
Literal AI is a collaborative platform designed to assist engineering and product teams in developing production-grade Large Language Model (LLM) applications. It offers a suite of tools for observability, evaluation, and analytics, enabling efficient tracking, optimization, and integration of prompt versions. Key features include multimodal logging, encompassing vision, audio, and video, prompt management with versioning and AB testing capabilities, and a prompt playground for testing multiple LLM providers and configurations. Literal AI integrates seamlessly with various LLM providers and AI frameworks, such as OpenAI, LangChain, and LlamaIndex, and provides SDKs in Python and TypeScript for easy instrumentation of code. The platform also supports the creation of experiments against datasets, facilitating continuous improvement and preventing regressions in LLM applications. -
7
Weights & Biases
Weights & Biases
Experiment tracking, hyperparameter optimization, model and dataset versioning with Weights & Biases (WandB). Track, compare, and visualize ML experiments with 5 lines of code. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Optimize models with our massively scalable hyperparameter search tool. Sweeps are lightweight, fast to set up, and plug in to your existing infrastructure for running models. Save every detail of your end-to-end machine learning pipeline — data preparation, data versioning, training, and evaluation. It's never been easier to share project updates. Quickly and easily implement experiment logging by adding just a few lines to your script and start logging results. Our lightweight integration works with any Python script. W&B Weave is here to help developers build and iterate on their AI applications with confidence. -
8
HoneyHive
HoneyHive
AI engineering doesn't have to be a black box. Get full visibility with tools for tracing, evaluation, prompt management, and more. HoneyHive is an AI observability and evaluation platform designed to assist teams in building reliable generative AI applications. It offers tools for evaluating, testing, and monitoring AI models, enabling engineers, product managers, and domain experts to collaborate effectively. Measure quality over large test suites to identify improvements and regressions with each iteration. Track usage, feedback, and quality at scale, facilitating the identification of issues and driving continuous improvements. HoneyHive supports integration with various model providers and frameworks, offering flexibility and scalability to meet diverse organizational needs. It is suitable for teams aiming to ensure the quality and performance of their AI agents, providing a unified platform for evaluation, monitoring, and prompt management. -
9
Langfuse
Langfuse
Langfuse is an open source LLM engineering platform to help teams collaboratively debug, analyze and iterate on their LLM Applications. Observability: Instrument your app and start ingesting traces to Langfuse Langfuse UI: Inspect and debug complex logs and user sessions Prompts: Manage, version and deploy prompts from within Langfuse Analytics: Track metrics (LLM cost, latency, quality) and gain insights from dashboards & data exports Evals: Collect and calculate scores for your LLM completions Experiments: Track and test app behavior before deploying a new version Why Langfuse? - Open source - Model and framework agnostic - Built for production - Incrementally adoptable - start with a single LLM call or integration, then expand to full tracing of complex chains/agents - Use GET API to build downstream use cases and export dataStarting Price: $29/month -
10
Pinecone Rerank v0
Pinecone
Pinecone Rerank V0 is a cross-encoder model optimized for precision in reranking tasks, enhancing enterprise search and retrieval-augmented generation (RAG) systems. It processes queries and documents together to capture fine-grained relevance, assigning a relevance score from 0 to 1 for each query-document pair. The model's maximum context length is set to 512 tokens to preserve ranking quality. Evaluations on the BEIR benchmark demonstrated that Pinecone Rerank V0 achieved the highest average NDCG@10, outperforming other models on 6 out of 12 datasets. For instance, it showed up to a 60% boost on the Fever dataset compared to Google Semantic Ranker and over 40% on the Climate-Fever dataset relative to cohere-v3-multilingual or voyageai-rerank-2. The model is accessible through Pinecone Inference and is available to all users in public preview.Starting Price: $25 per month -
11
Arize Phoenix
Arize AI
Phoenix is an open-source observability library designed for experimentation, evaluation, and troubleshooting. It allows AI engineers and data scientists to quickly visualize their data, evaluate performance, track down issues, and export data to improve. Phoenix is built by Arize AI, the company behind the industry-leading AI observability platform, and a set of core contributors. Phoenix works with OpenTelemetry and OpenInference instrumentation. The main Phoenix package is arize-phoenix. We offer several helper packages for specific use cases. Our semantic layer is to add LLM telemetry to OpenTelemetry. Automatically instrumenting popular packages. Phoenix's open-source library supports tracing for AI applications, via manual instrumentation or through integrations with LlamaIndex, Langchain, OpenAI, and others. LLM tracing records the paths taken by requests as they propagate through multiple steps or components of an LLM application.Starting Price: Free -
12
Maxim
Maxim
Maxim is an agent simulation, evaluation, and observability platform that empowers modern AI teams to deploy agents with quality, reliability, and speed. Maxim's end-to-end evaluation and data management stack covers every stage of the AI lifecycle, from prompt engineering to pre & post release testing and observability, data-set creation & management, and fine-tuning. Use Maxim to simulate and test your multi-turn workflows on a wide variety of scenarios and across different user personas before taking your application to production. Features: Agent Simulation Agent Evaluation Prompt Playground Logging/Tracing Workflows Custom Evaluators- AI, Programmatic and Statistical Dataset Curation Human-in-the-loop Use Case: Simulate and test AI agents Evals for agentic workflows: pre and post-release Tracing and debugging multi-agent workflows Real-time alerts on performance and quality Creating robust datasets for evals and fine-tuning Human-in-the-loop workflowsStarting Price: $29/seat/month -
13
Aserto
Aserto
Aserto helps developers build secure applications. It makes it easy to add fine-grained, policy-based, real-time access control to your applications and APIs. Aserto handles all the heavy lifting required to achieve secure, scalable, high-performance access management. It offers blazing-fast authorization of a local library coupled with a centralized control plane for managing policies, user attributes, relationship data, and decision logs. And it comes with everything you need to implement RBAC or fine-grained authorization models, such as ABAC, and ReBAC. Take a look at our open-source projects: - Topaz.sh: a standalone authorizer you can deploy in your environment to add fine-grained access control to your applications. Topaz lets you combine OPA policies with Zanzibar’s data model for complete flexibility. - OpenPolicyContainers.com (OPCR) secures OPA policies across the lifecycle by adding the ability to tag, verStarting Price: $0 -
14
Sunlight
Sunlight
The Sunlight Dashboard, a component of NexVisor HCI, provides a graphical management interface onto any Sunlight cluster - including resource-limited Edge clusters. It provides Highly Available local resource management on a single pain of glass. Manage all of your VMs on a local Sunlight cluster, with fine-grained control of your resources. Resource groups provide control to meet VM requirements. Ultra fine-grained control of performance when needed or simplicity when not needed. Maximum utilisation of constrained Edge resources. Dashboard automatically failsover in the event of server failure. Sunlight is designed with security at its core. All components of the Sunlight stack are hardened. Thanks to Sunlight’s fine grained CPU and memory allocation it is possible to physically guard against CPU memory exploits. Control of IO interfaces allows you to separate content and network traffic so there is no sharing of physical drives or network physical interfaces.Starting Price: $100 per node per month -
15
Cedar
Amazon
Cedar is an open source policy language and evaluation engine developed by AWS to facilitate fine-grained access control in applications. It enables developers to define clear and concise authorization policies, decoupling access control from application logic. Cedar supports common authorization models, including role-based access control and attribute-based access control, allowing for expressive and analyzable policy definitions. Its design emphasizes readability and performance, ensuring that policies are both easy to understand and efficient to enforce. By integrating Cedar, applications can make precise authorization decisions, enhancing security and maintainability. The policy structure is designed to be indexed for quick retrieval and to support fast and scalable real-time evaluation, with bounded latency. It enables analyzer tools capable of optimizing your policies and proving that your security model is what you believe it is.Starting Price: Free -
16
Symflower
Symflower
Symflower enhances software development by integrating static, dynamic, and symbolic analyses with Large Language Models (LLMs). This combination leverages the precision of deterministic analyses and the creativity of LLMs, resulting in higher quality and faster software development. Symflower assists in identifying the most suitable LLM for specific projects by evaluating various models against real-world scenarios, ensuring alignment with specific environments, workflows, and requirements. The platform addresses common LLM challenges by implementing automatic pre-and post-processing, which improves code quality and functionality. By providing the appropriate context through Retrieval-Augmented Generation (RAG), Symflower reduces hallucinations and enhances LLM performance. Continuous benchmarking ensures that use cases remain effective and compatible with the latest models. Additionally, Symflower accelerates fine-tuning and training data curation, offering detailed reports. -
17
Opik
Comet
Confidently evaluate, test, and ship LLM applications with a suite of observability tools to calibrate language model outputs across your dev and production lifecycle. Log traces and spans, define and compute evaluation metrics, score LLM outputs, compare performance across app versions, and more. Record, sort, search, and understand each step your LLM app takes to generate a response. Manually annotate, view, and compare LLM responses in a user-friendly table. Log traces during development and in production. Run experiments with different prompts and evaluate against a test set. Choose and run pre-configured evaluation metrics or define your own with our convenient SDK library. Consult built-in LLM judges for complex issues like hallucination detection, factuality, and moderation. Establish reliable performance baselines with Opik's LLM unit tests, built on PyTest. Build comprehensive test suites to evaluate your entire LLM pipeline on every deployment.Starting Price: $39 per month -
18
OpenPipe
OpenPipe
OpenPipe provides fine-tuning for developers. Keep your datasets, models, and evaluations all in one place. Train new models with the click of a button. Automatically record LLM requests and responses. Create datasets from your captured data. Train multiple base models on the same dataset. We serve your model on our managed endpoints that scale to millions of requests. Write evaluations and compare model outputs side by side. Change a couple of lines of code, and you're good to go. Simply replace your Python or Javascript OpenAI SDK and add an OpenPipe API key. Make your data searchable with custom tags. Small specialized models cost much less to run than large multipurpose LLMs. Replace prompts with models in minutes, not weeks. Fine-tuned Mistral and Llama 2 models consistently outperform GPT-4-1106-Turbo, at a fraction of the cost. We're open-source, and so are many of the base models we use. Own your own weights when you fine-tune Mistral and Llama 2, and download them at any time.Starting Price: $1.20 per 1M tokens -
19
Seed-Music
ByteDance
Seed-Music is a unified framework for high-quality and controlled music generation and editing, capable of producing vocal and instrumental works from multimodal inputs such as lyrics, style descriptions, sheet music, audio references, or voice prompts, and of supporting post-production editing of existing tracks by allowing direct modification of melodies, timbres, lyrics, or instruments. It combines autoregressive language modeling with diffusion approaches and a three-stage pipeline comprising representation learning (which encodes raw audio into intermediate representations, including audio tokens, symbolic music tokens, and vocoder latents), generation (which transforms these multimodal inputs into music representations), and rendering (which converts those representations into high-fidelity audio). The system supports lead-sheet to song conversion, singing synthesis, voice conversion, audio continuation, style transfer, and fine-grained control over music structure. -
20
AgentBench
AgentBench
AgentBench is an evaluation framework specifically designed to assess the capabilities and performance of autonomous AI agents. It provides a standardized set of benchmarks that test various aspects of an agent's behavior, such as task-solving ability, decision-making, adaptability, and interaction with simulated environments. By evaluating agents on tasks across different domains, AgentBench helps developers identify strengths and weaknesses in the agents’ performance, such as their ability to plan, reason, and learn from feedback. The framework offers insights into how well an agent can handle complex, real-world-like scenarios, making it useful for both research and practical development. Overall, AgentBench supports the iterative improvement of autonomous agents, ensuring they meet reliability and efficiency standards before wider application. -
21
Latitude
Latitude
Latitude is an open-source prompt engineering platform designed to help product teams build, evaluate, and deploy AI models efficiently. It allows users to import and manage prompts at scale, refine them with real or synthetic data, and track the performance of AI models using LLM-as-judge or human-in-the-loop evaluations. With powerful tools for dataset management and automatic logging, Latitude simplifies the process of fine-tuning models and improving AI performance, making it an essential platform for businesses focused on deploying high-quality AI applications.Starting Price: $0 -
22
Epsilla
Epsilla
Manages the entire lifecycle of LLM application development, testing, deployment, and operation without the need to piece together multiple systems. Achieving the lowest total cost of ownership (TCO). Featuring the vector database and search engine that outperforms all other leading vendors with 10X lower query latency, 5X higher query throughput, and 3X lower cost. An innovative data and knowledge foundation that efficiently manages large-scale, multi-modality unstructured and structured data. Never have to worry about outdated information. Plug and play with state-of-the-art advanced, modular, agentic RAG and GraphRAG techniques without writing plumbing code. With CI/CD-style evaluations, you can confidently make configuration changes to your AI applications without worrying about regressions. Accelerate your iterations and move to production in days, not months. Fine-grained, role-based, and privilege-based access control.Starting Price: $29 per month -
23
EvalsOne
EvalsOne
An intuitive yet comprehensive evaluation platform to iteratively optimize your AI-driven products. Streamline LLMOps workflow, build confidence, and gain a competitive edge. EvalsOne is your all-in-one toolbox for optimizing your application evaluation process. Imagine a Swiss Army knife for AI, equipped to tackle any evaluation scenario you throw its way. Suitable for crafting LLM prompts, fine-tuning RAG processes, and evaluating AI agents. Choose from rule-based or LLM-based approaches to automate the evaluation process. Integrate human evaluation seamlessly, leveraging the power of expert judgment. Applicable to all LLMOps stages from development to production environments. EvalsOne provides an intuitive process and interface, that empowers teams across the AI lifecycle, from developers to researchers and domain experts. Easily create evaluation runs and organize them in levels. Quickly iterate and perform in-depth analysis through forked runs. -
24
DeepEval
Confident AI
DeepEval is a simple-to-use, open source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that run locally on your machine for evaluation. Whether your application is implemented via RAG or fine-tuning, LangChain, or LlamaIndex, DeepEval has you covered. With it, you can easily determine the optimal hyperparameters to improve your RAG pipeline, prevent prompt drifting, or even transition from OpenAI to hosting your own Llama2 with confidence. The framework supports synthetic dataset generation with advanced evolution techniques and integrates seamlessly with popular frameworks, allowing for efficient benchmarking and optimization of LLM systems.Starting Price: Free -
25
Perplexity Search API
Perplexity AI
Perplexity has launched the Perplexity Search API, giving developers access to the same global-scale indexing and retrieval infrastructure that powers Perplexity’s public answer engine. The API indexes hundreds of billions of webpages and is optimized for the unique demands of AI workflows; it breaks documents into fine-grained subunits so that responses return highly relevant snippets already ranked against the original query, reducing preprocessing and improving downstream performance. To maintain freshness, the index processes tens of thousands of updates every second using an AI-driven content understanding module that dynamically parses web content and iteratively self-improves via real-time query feedback. The API returns rich, structured responses suitable for both AI agents and traditional apps, rather than limited, document-level outputs. Alongside the API, Perplexity is releasing an SDK, an open source evaluation framework, and detailed research into their design. -
26
Ferret
Apple
An End-to-End MLLM that Accept Any-Form Referring and Ground Anything in Response. Ferret Model - Hybrid Region Representation + Spatial-aware Visual Sampler enable fine-grained and open-vocabulary referring and grounding in MLLM. GRIT Dataset (~1.1M) - A Large-scale, Hierarchical, Robust ground-and-refer instruction tuning dataset. Ferret-Bench - A multimodal evaluation benchmark that jointly requires Referring/Grounding, Semantics, Knowledge, and Reasoning.Starting Price: Free -
27
HumanSignal
HumanSignal
HumanSignal's Label Studio Enterprise is a comprehensive platform designed for creating high-quality labeled data and evaluating model outputs with human supervision. It supports labeling and evaluating multi-modal data, image, video, audio, text, and time series, all in one place. It offers customizable labeling interfaces with pre-built templates and powerful plugins, allowing users to tailor the UI and workflows to specific use cases. Label Studio Enterprise integrates seamlessly with popular cloud storage providers and ML/AI models, facilitating pre-annotation, AI-assisted labeling, and prediction generation for model evaluation. The Prompts feature enables users to leverage LLMs to swiftly generate accurate predictions, enabling instant labeling of thousands of tasks. It supports various labeling use cases, including text classification, named entity recognition, sentiment analysis, summarization, and image captioning.Starting Price: $99 per month -
28
Scale Evaluation
Scale
Scale Evaluation offers a comprehensive evaluation platform tailored for developers of large language models. This platform addresses current challenges in AI model assessment, such as the scarcity of high-quality, trustworthy evaluation datasets and the lack of consistent model comparisons. By providing proprietary evaluation sets across various domains and capabilities, Scale ensures accurate model assessments without overfitting. The platform features a user-friendly interface for analyzing and reporting model performance, enabling standardized evaluations for true apples-to-apples comparisons. Additionally, Scale's network of expert human raters delivers reliable evaluations, supported by transparent metrics and quality assurance mechanisms. The platform also offers targeted evaluations with custom sets focusing on specific model concerns, facilitating precise improvements through new training data. -
29
BigLake
Google
BigLake is a storage engine that unifies data warehouses and lakes by enabling BigQuery and open-source frameworks like Spark to access data with fine-grained access control. BigLake provides accelerated query performance across multi-cloud storage and open formats such as Apache Iceberg. Store a single copy of data with uniform features across data warehouses & lakes. Fine-grained access control and multi-cloud governance over distributed data. Seamless integration with open-source analytics tools and open data formats. Unlock analytics on distributed data regardless of where and how it’s stored, while choosing the best analytics tools, open source or cloud-native over a single copy of data. Fine-grained access control across open source engines like Apache Spark, Presto, and Trino, and open formats such as Parquet. Performant queries over data lakes powered by BigQuery. Integrates with Dataplex to provide management at scale, including logical data organization.Starting Price: $5 per TB -
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Ragas
Ragas
Ragas is an open-source framework designed to test and evaluate Large Language Model (LLM) applications. It offers automatic metrics to assess performance and robustness, synthetic test data generation tailored to specific requirements, and workflows to ensure quality during development and production monitoring. Ragas integrates seamlessly with existing stacks, providing insights to enhance LLM applications. The platform is maintained by a team of passionate individuals leveraging cutting-edge research and pragmatic engineering practices to empower visionaries redefining LLM possibilities. Synthetically generate high-quality and diverse evaluation data customized for your requirements. Evaluate and ensure the quality of your LLM application in production. Use insights to improve your application. Automatic metrics that helps you understand the performance and robustness of your LLM application.Starting Price: Free -
31
Agenta
Agenta
Agenta is an open-source LLMOps platform designed to help teams build reliable AI applications with integrated prompt management, evaluation workflows, and system observability. It centralizes all prompts, experiments, traces, and evaluations into one structured hub, eliminating scattered workflows across Slack, spreadsheets, and emails. With Agenta, teams can iterate on prompts collaboratively, compare models side-by-side, and maintain full version history for every change. Its evaluation tools replace guesswork with automated testing, LLM-as-a-judge, human annotation, and intermediate-step analysis. Observability features allow developers to trace failures, annotate logs, convert traces into tests, and monitor performance regressions in real time. Agenta helps AI teams transition from siloed experimentation to a unified, efficient LLMOps workflow for shipping more reliable agents and AI products.Starting Price: Free -
32
Acquven LMS
Acquven Business Solutions
SpriteLMS™ is an easy-to-use application that helps create, manage, deliver and track training. It is scalable and compatible across desktops, mobiles and tablets. Undergo training assigned. Search and register for available training. Approvals and e-signatures of training and supporting documentation. System configuration & maintenance, user management & fine-grained access control. Search & self-registration of available training. -
33
SecuPi
SecuPi
SecuPi provides an overarching data-centric security platform, delivering fine-grained access control (ABAC), Database Activity Monitoring (DAM) and de-identification using FPE encryption, physical and dynamic masking and deletion (RTBF). SecuPi offers wide coverage across packaged and home-grown applications, direct access tools, big data, and cloud environments. One data security platform for monitoring, controlling, encrypting, and classifying data across all cloud & on-prem platforms seamlessly with no code changes. Agile and efficient configurable platform to meet current & future regulatory and audit requirements. No source-code changes with fast & cost-efficient implementation. SecuPi’s fine-grain data access controls protect sensitive data so users get access only to data they are entitled to view, and no more. Seamlessly integrate with Starburst/Trino for automated enforcement of data access policies and data protection operations. -
34
Klu
Klu
Klu.ai is a Generative AI platform that simplifies the process of designing, deploying, and optimizing AI applications. Klu integrates with your preferred Large Language Models, incorporating data from varied sources, giving your applications unique context. Klu accelerates building applications using language models like Anthropic Claude, Azure OpenAI, GPT-4, and over 15 other models, allowing rapid prompt/model experimentation, data gathering and user feedback, and model fine-tuning while cost-effectively optimizing performance. Ship prompt generations, chat experiences, workflows, and autonomous workers in minutes. Klu provides SDKs and an API-first approach for all capabilities to enable developer productivity. Klu automatically provides abstractions for common LLM/GenAI use cases, including: LLM connectors, vector storage and retrieval, prompt templates, observability, and evaluation/testing tooling.Starting Price: $97 -
35
ColBERT
Future Data Systems
ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. It relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings. At search time, it embeds every query into another matrix and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators. These rich interactions allow ColBERT to surpass the quality of single-vector representation models while scaling efficiently to large corpora. The toolkit includes components for retrieval, reranking, evaluation, and response analysis, facilitating end-to-end workflows. ColBERT integrates with Pyserini for retrieval and provides integrated evaluation for multi-stage pipelines. It also includes a module for detailed analysis of input prompts and LLM responses, addressing reliability concerns with LLM APIs and non-deterministic behavior in Mixture-of-Experts.Starting Price: Free -
36
ReByte
RealChar.ai
Action-based orchestration to build complex backend agents with multiple steps. Working for all LLMs, build fully customized UI for your agent without writing a single line of code, serving on your domain. Track every step of your agent, literally every step, to deal with the nondeterministic nature of LLMs. Build fine-grain access control over your application, data, and agent. Specialized fine-tuned model for accelerating software development. Automatically handle concurrency, rate limiting, and more.Starting Price: $10 per month -
37
OpenFGA
The Linux Foundation
OpenFGA is an open source authorization solution that enables developers to implement fine-grained access control using a user-friendly modeling language and APIs. Inspired by Google's Zanzibar paper, it supports various access control models, including Relationship-Based Access Control (ReBAC), Role-Based Access Control (RBAC), and Attribute-Based Access Control (ABAC). OpenFGA offers SDKs for multiple programming languages, such as Java, .NET, JavaScript, Go, and Python, facilitating seamless integration into diverse applications. The platform is designed for high performance, capable of processing authorization checks in milliseconds, making it suitable for projects ranging from small startups to large enterprises. Operating under the Cloud Native Computing Foundation (CNCF) as a sandbox project, OpenFGA emphasizes transparency and community collaboration, inviting contributions to its development and governance.Starting Price: Free -
38
AuthZed
AuthZed
Unblock your business with an authorization system inspired by Google's Zanzibar white paper. As the creators of SpiceDB, the AuthZed team delivers enterprise-ready permissions systems built for scale and security. The most mature open source Zanzibar implementation designed for both consistency and performance at scale. Define fine-grained access for any object in your application or across your product suite and manage permissions using a centralized schema. Specify consistency requirements per authorization check; tunable consistency features balance performance and correctness according to your use case. SpiceDB returns lists of authorized subjects and accessible resources, helpful when pre-filtering permission-based results. Instrumented with observability tooling, a powerful Kubernetes operator, and load-testing capabilities, SpiceDB prioritizes both developer and platform engineering experiences. -
39
Eventarc
Google
Google Cloud's Eventarc is a fully managed platform that enables developers to build event-driven architectures by routing events from various sources to supported destinations. It allows for the collection of events occurring within a system and publishes them to a specified destination, facilitating the creation of loosely coupled services that react to state changes. Eventarc supports events from Google Cloud services, custom applications, and third-party SaaS providers, providing flexibility in event-driven application design. Developers can create triggers to route events to various destinations, such as Cloud Run services, allowing for responsive and scalable application architectures. Eventarc ensures secure event delivery by integrating with Identity and Access Management (IAM), enabling fine-grained access control over event ingestion and processing. -
40
Kubestone
Kubestone
Welcome to Kubestone, the benchmarking operator for Kubernetes. Kubestone is a benchmarking operator that can evaluate the performance of Kubernetes installations. Supports a common set of benchmarks to measure, CPU, disk, network and application performance. Fine-grained control over Kubernetes scheduling primitives, affinity, anti-affinity, tolerations, storage classes, and node selection. New benchmarks can easily be added by implementing a new controller. Benchmarks runs are defined as custom resources and executed in the cluster using Kubernetes resources, pods, jobs, deployments, and services. Follow the quickstart guide to see how Kubestone can be deployed and how benchmarks can be run. Benchmarks can be executed via Kubestone by creating custom resources in your cluster. After the namespace is created you can use it to post a benchmark request to the cluster. The resulting benchmark executions will reside in this namespace. -
41
Tetrate
Tetrate
Connect and manage applications across clusters, clouds, and data centers. Coordinate app connectivity across heterogeneous infrastructure from a single management plane. Integrate traditional workloads into your cloud-native application infrastructure. Create tenants within your business to define fine-grained access control and editing rights for teams on shared infrastructure. Audit the history of changes to services and shared resources from day zero. Automate traffic shifting across failure domains before your customers notice. TSB sits at the application edge, at cluster ingress, and between workloads in your Kubernetes and traditional compute clusters. Edge and ingress gateways route and load balance application traffic across clusters and clouds while the mesh controls connectivity between services. A single management plane configures connectivity, security, and observability for your entire application network. -
42
Gemini 2.5 Flash-Lite
Google
Gemini 2.5 is Google DeepMind’s latest generation AI model family, designed to deliver advanced reasoning and native multimodality with a long context window. It improves performance and accuracy by reasoning through its thoughts before responding. The model offers different versions tailored for complex coding tasks, fast everyday performance, and cost-efficient high-volume workloads. Gemini 2.5 supports multiple data types including text, images, video, audio, and PDFs, enabling versatile AI applications. It features adaptive thinking budgets and fine-grained control for developers to balance cost and output quality. Available via Google AI Studio and Gemini API, Gemini 2.5 powers next-generation AI experiences. -
43
promptfoo
promptfoo
Promptfoo discovers and eliminates major LLM risks before they are shipped to production. Its founders have experience launching and scaling AI to over 100 million users using automated red-teaming and testing to overcome security, legal, and compliance issues. Promptfoo's open source, developer-first approach has made it the most widely adopted tool in this space, with over 20,000 users. Custom probes for your application that identify failures you actually care about, not just generic jailbreaks and prompt injections. Move quickly with a command-line interface, live reloads, and caching. No SDKs, cloud dependencies, or logins. Used by teams serving millions of users and supported by an active open source community. Build reliable prompts, models, and RAGs with benchmarks specific to your use case. Secure your apps with automated red teaming and pentesting. Speed up evaluations with caching, concurrency, and live reloading.Starting Price: Free -
44
HashiCorp Waypoint
HashiCorp
Waypoint uses a single configuration file and common workflow to manage and observe deployments across platforms such as Kubernetes, Nomad, EC2, Google Cloud Run, and more. Waypoint builds applications for any language or framework. You can use Buildpacks for automatically building common frameworks or custom Dockerfiles or other build tools for more fine-grained control. The build step is where your application and assets are compiled, validated, and an artifact is created. This artifact can be published to a remote registry or simply passed to the deploy step. Waypoint deploys artifacts created by the build step to a variety of platforms, from Kubernetes to EC2 to static site hosts. It configures your target platform and prepares the new application version to be publicly accessible. Deployments are accessible via a preview URL prior to release. Waypoint releases your staged deployments and makes them accessible to the public. -
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Benchable
Benchable
Benchable is a dynamic AI tool designed for businesses and tech enthusiasts to effectively compare the performance, cost, and quality of various AI models. It allows users to benchmark leading models like GPT-4, Claude, and Gemini through custom tests, providing real-time results to help make informed decisions. With its user-friendly interface and robust analytics, Benchable streamlines the evaluation process, ensuring you find the most suitable AI solution for your needs.Starting Price: $0 -
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Manages users, groups and roles. Authentication, delegation, authorization and auditing. Role-based access control, entitlements and time-based access rules. Manages access control policies for Web, Java and CORBA® resources. Manages access control policies for fine-grain application data and/or features. Central administration with flexible deployment options. Features specifically designed to aid in meeting privacy legislation. Supports integration with existing security infrastructure. Provides foundation for orb2 for Java Security Services.
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VMware Cloud Director
Broadcom
VMware Cloud Director is a leading cloud service-delivery platform used by some of the world’s most popular cloud providers to operate and manage successful cloud-service businesses. Using VMware Cloud Director, cloud providers deliver secure, efficient, and elastic cloud resources to thousands of enterprises and IT teams across the world. Use VMware in the cloud through one of our Cloud Provider Partners and build with VMware Cloud Director. A policy-driven approach helps ensure enterprises have isolated virtual resources, independent role-based authentication, and fine-grained control. A policy-driven approach to compute, storage, networking and security ensures tenants have securely isolated virtual resources, independent role-based authentication, and fine-grained control of their public cloud services. Stretch data centers across sites and geographies; monitor resources from an intuitive single-pane of glass with multi-site aggregate views. -
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Permify
Permify
Permify is an authorization service designed to help developers build and manage fine-grained, scalable access control systems within their applications. Inspired by Google's Zanzibar, Permify enables the structuring of authorization models, storage of authorization data in preferred databases, and interaction with its API to handle authorization queries across various applications and services. It supports multiple access control models, including Role-Based Access Control (RBAC), and Attribute-Based Access Control (ABAC), allowing for the creation of granular permissions and policies. Permify centralized authorization logic, abstracting it from the codebase to facilitate easier reasoning, testing, and debugging. It offers flexible policy storage options and provides a role manager to handle RBAC role hierarchies. The platform also supports filtered policy management for efficient enforcement in large, multi-tenant environments.Starting Price: Free -
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P0 Security
P0 Security
Find and fix vulnerabilities, request and grant privileged access. You should never need to trade off infrastructure security against developer velocity. Process access escalations in minutes. No more tickets, better-scoped permissions, and automatic expiration. P0 Security enables engineers to request just-in-time, fine-grained access to any cloud resource, without becoming an expert in the language of cloud IAM. DevOps teams can automate provisioning and expiry of access, without needing to constantly update static IDP groups. Provide developers just-in-time, short-lived, and fine-grained access to a production stack (AWS, GCP, Kubernetes) for deploying or troubleshooting services. Automate periodic access reviews of your cloud environment, and accelerate compliance for SOC2 and ISO 27001, without overburdening your teams. Provide engineers and customer success teams just-in-time and short-lived access to customer data in a cloud environment, or in a data warehouse.Starting Price: $25 per month -
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AWS Network Firewall
Amazon
With AWS Network Firewall, you can create firewall rules that provide fine-grained control over network traffic and easily deploy firewall security across your VPCs. Automatically scale your network firewall to protect your managed infrastructure. Protect your unique workloads with a flexible engine that can define thousands of custom rules. Centrally manage security policies across existing accounts and VPCs and automatically enforce mandatory policies on new accounts. With AWS Network Firewall, you can define firewall rules that provide fine-grained control over network traffic. Network Firewall works together with AWS Firewall Manager so you can build policies based on Network Firewall rules and then centrally apply those policies across your virtual private clouds (VPCs) and accounts. Inspect traffic flows using features such as inbound encrypted traffic inspection, stateful inspection, protocol detection, and more.
