Alternatives to Plurai
Compare Plurai alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Plurai in 2026. Compare features, ratings, user reviews, pricing, and more from Plurai competitors and alternatives in order to make an informed decision for your business.
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1
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 -
2
Future AGI
Future AGI
Future AGI is an open-source, end-to-end AI agent engineering platform that covers the full lifecycle: simulate, evaluate, optimize, monitor, protect, gateway, and guardrail - all from one place. It helps teams ship self-improving AI agents by collapsing fragmented tooling into one platform and one feedback loop: simulate edge cases before launch, evaluate what happens in production, protect users in real time, and turn every trace into signal for the next version. Key capabilities include 70+ built-in evaluation templates covering quality, safety, factuality, RAG retrieval, bias, audio, and image evaluation, OpenTelemetry-native tracing, agent optimization, and real-time guardrails (PII detection, prompt injection blocking). SDKs are available in Python, TypeScript, Java, and C#, with integrations for OpenAI, LangChain, LlamaIndex, and 30+ frameworks. Apache 2.0 licensed, self-hostable or cloud-managed. -
3
Openlayer
Openlayer
Openlayer is the AI governance and observability platform that accelerates the evaluation and observability of agentic systems through 100+ automated tests and real-time guardrails that prevent prompt injections, PII leakage, bias, toxicity, and hallucinations, powering secure enterprise innovation. Designed to support both traditional ML and GenAI systems, Openlayer helps teams seamlessly handle everything from data-quality detection to automating comprehensive model evaluations, with full traceability across RAG, agents, and complex multi-step workflows. Trusted by Fortune 500 companies from early experimentation through production deployment and automated governance capabilities (NIST, EU AI Act, etc.)., Openlayer enables safe, reliable, and responsible AI operations. -
4
Arato.ai
Arato.ai
Arato.ai is an end-to-end platform for structured, reliable, and production-ready LLM development, built to help teams build, evaluate, and scale GenAI apps with confidence. Designed for complex systems but made simple, Arato works with any LLM stack and connects to AI applications as they are, with no rewrites, no heavy setup, and no deep integrations required. It helps teams simulate multi-modal user journeys across text, voice, data, or image, test AI behavior before it reaches customers, and align development with AI compliance requirements such as the EU AI Act and ISO/IEC 42001. Arato Simulate is a black-box simulation platform that runs realistic user traffic against AI applications to test for accuracy, security, compliance, cost, and UX, scored by business impact. It catches what traditional testing misses, including multi-turn conversations, edge cases, adversarial scenarios, persona-specific failures, and large-scale issues. -
5
Laminar
Laminar
Laminar is an open source all-in-one platform for engineering best-in-class LLM products. Data governs the quality of your LLM application. Laminar helps you collect it, understand it, and use it. When you trace your LLM application, you get a clear picture of every step of execution and simultaneously collect invaluable data. You can use it to set up better evaluations, as dynamic few-shot examples, and for fine-tuning. All traces are sent in the background via gRPC with minimal overhead. Tracing of text and image models is supported, audio models are coming soon. You can set up LLM-as-a-judge or Python script evaluators to run on each received span. Evaluators label spans, which is more scalable than human labeling, and especially helpful for smaller teams. Laminar lets you go beyond a single prompt. You can build and host complex chains, including mixtures of agents or self-reflecting LLM pipelines.Starting Price: $25 per month -
6
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 -
7
Respan
Respan
Respan is a self-driving observability and evaluation platform built specifically for AI agents. It enables teams to trace full execution flows, including messages, tool calls, routing decisions, memory usage, and outcomes. The platform connects observability, evaluations, and optimization into a continuous improvement loop. Metric-first evaluations allow teams to define performance standards such as accuracy, cost, reliability, and safety. Respan also includes capability and regression testing to protect stable behaviors while improving new ones. An AI-powered evaluation agent analyzes failures, identifies root causes, and recommends next steps automatically. With compliance certifications including ISO 27001, SOC 2, GDPR, and HIPAA, Respan supports secure, large-scale AI deployments across industries.Starting Price: $0/month -
8
Orq.ai
Orq.ai
Orq.ai is the #1 platform for software teams to operate agentic AI systems at scale. Optimize prompts, deploy use cases, and monitor performance, no blind spots, no vibe checks. Experiment with prompts and LLM configurations before moving to production. Evaluate agentic AI systems in offline environments. Roll out GenAI features to specific user groups with guardrails, data privacy safeguards, and advanced RAG pipelines. Visualize all events triggered by agents for fast debugging. Get granular control on cost, latency, and performance. Connect to your favorite AI models, or bring your own. Speed up your workflow with out-of-the-box components built for agentic AI systems. Manage core stages of the LLM app lifecycle in one central platform. Self-hosted or hybrid deployment with SOC 2 and GDPR compliance for enterprise security. -
9
Dynamiq
Dynamiq
Dynamiq is a platform built for engineers and data scientists to build, deploy, test, monitor and fine-tune Large Language Models for any use case the enterprise wants to tackle. Key features: 🛠️ Workflows: Build GenAI workflows in a low-code interface to automate tasks at scale 🧠 Knowledge & RAG: Create custom RAG knowledge bases and deploy vector DBs in minutes 🤖 Agents Ops: Create custom LLM agents to solve complex task and connect them to your internal APIs 📈 Observability: Log all interactions, use large-scale LLM quality evaluations 🦺 Guardrails: Precise and reliable LLM outputs with pre-built validators, detection of sensitive content, and data leak prevention 📻 Fine-tuning: Fine-tune proprietary LLM models to make them your ownStarting Price: $125/month -
10
Netra
Netra
AI agents fail silently in production. Wrong answers, broken loops, cost spikes, behavior drift after a prompt change, and no stack trace to explain why. Netra gives engineering teams full visibility into every agent decision. Trace every LLM call, evaluate quality automatically, simulate edge cases before launch, and manage prompts with complete version history. Built on OpenTelemetry so setup takes minutes, not days. SOC2 Type II certified. GDPR and HIPAA compliant. US and EU data residency. Integrates with: LangChain, LangGraph, CrewAI, LlamaIndex, OpenAI, Anthropic, Gemini, AWS Bedrock, and 30+ more.Starting Price: $39/month -
11
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 -
12
Lucidic AI
Lucidic AI
Lucidic AI is a specialized analytics and simulation platform built for AI agent development that brings much-needed transparency, interpretability, and efficiency to often opaque workflows. It provides developers with visual, interactive insights, including searchable workflow replays, step-by-step video, and graph-based replays of agent decisions, decision tree visualizations, and side‑by‑side simulation comparisons, that enable you to observe exactly how your agent reasons and why it succeeds or fails. The tool dramatically reduces iteration time from weeks or days to mere minutes by streamlining debugging and optimization through instant feedback loops, real‑time “time‑travel” editing, mass simulations, trajectory clustering, customizable evaluation rubrics, and prompt versioning. Lucidic AI integrates seamlessly with major LLMs and frameworks and offers advanced QA/QC mechanisms like alerts, workflow sandboxing, and more. -
13
AgentOps
AgentOps
Industry-leading developer platform to test and debug AI agents. We built the tools so you don't have to. Visually track events such as LLM calls, tools, and multi-agent interactions. Rewind and replay agent runs with point-in-time precision. Keep a full data trail of logs, errors, and prompt injection attacks from prototype to production. Native integrations with the top agent frameworks. Track, save, and monitor every token your agent sees. Manage and visualize agent spending with up-to-date price monitoring. Fine-tune specialized LLMs up to 25x cheaper on saved completions. Build your next agent with evals, observability, and replays. With just two lines of code, you can free yourself from the chains of the terminal and instead visualize your agents’ behavior in your AgentOps dashboard. After setting up AgentOps, each execution of your program is recorded as a session and the data is automatically recorded for you.Starting Price: $40 per month -
14
Vivgrid
Vivgrid
Vivgrid is a development platform for AI agents that emphasizes observability, debugging, safety, and global deployment infrastructure. It gives you full visibility into agent behavior, logging prompts, memory fetches, tool usage, and reasoning chains, letting developers trace where things break or deviate. You can test, evaluate, and enforce safety policies (like refusal rules or filters), and incorporate human-in-the-loop checks before going live. Vivgrid supports the orchestration of multi-agent systems with stateful memory, routing tasks dynamically across agent workflows. On the deployment side, it operates a globally distributed inference network to ensure low-latency (sub-50 ms) execution and exposes metrics like latency, cost, and usage in real time. It aims to simplify shipping resilient AI systems by combining debugging, evaluation, safety, and deployment into one stack, so you're not stitching together observability, infrastructure, and orchestration.Starting Price: $25 per month -
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Kayba
Kayba
Kayba makes AI agents self-improve from experience. It learns from an agent’s execution traces to detect failures, fix them, and measure whether the fix actually worked. Instead of relying on generic evals that cannot explain why an agent failed, Kayba derives failure modes from the agent’s own traces and builds custom benchmarks for the user’s domain, so teams can measure improvement against real production failure patterns. Kayba wires tracing into an agent with one line of setup, watches it around the clock, and flags the moment a step stops being recorded. Even good tracing rots as teams ship changes, and steps can quietly stop being captured; Kayba checks the tracing users already have, shows exactly what is broken, points to the file that needs attention, and sends the gap to a coding agent through MCP. The coding agent patches the issue, and Kayba verifies that the trace is actually closed.Starting Price: Free -
16
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 -
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Lunary
Lunary
Lunary is an AI developer platform designed to help AI teams manage, improve, and protect Large Language Model (LLM) chatbots. It offers features such as conversation and feedback tracking, analytics on costs and performance, debugging tools, and a prompt directory for versioning and team collaboration. Lunary supports integration with various LLMs and frameworks, including OpenAI and LangChain, and provides SDKs for Python and JavaScript. Guardrails to deflect malicious prompts and sensitive data leaks. Deploy in your VPC with Kubernetes or Docker. Allow your team to judge responses from your LLMs. Understand what languages your users are speaking. Experiment with prompts and LLM models. Search and filter anything in milliseconds. Receive notifications when agents are not performing as expected. Lunary's core platform is 100% open-source. Self-host or in the cloud, get started in minutes.Starting Price: $20 per month -
18
Braintrust
Braintrust Data
Braintrust is an AI observability and evaluation platform designed to help teams build, monitor, and improve AI systems in production. It enables users to capture and inspect real-time traces of AI interactions, including prompts, responses, and tool usage. The platform allows teams to measure performance using automated and human evaluations to ensure output quality. Braintrust helps identify issues such as hallucinations, regressions, and performance drops before they impact users. It supports prompt and model comparisons, making it easier to optimize AI workflows over time. With scalable trace ingestion and real-time monitoring, teams gain full visibility into how their AI systems behave. The platform integrates with multiple programming languages and tools, allowing developers to work within their existing tech stack. Overall, Braintrust provides a comprehensive solution for maintaining and improving AI quality at scale. -
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LayerLens
LayerLens
LayerLens is an independent AI model evaluation platform for understanding how models perform through verified results across benchmarks, prompt-level results, agentic benchmarks, and audit-ready comparisons across vendors. It helps teams compare more than 200 AI models side by side, with transparent benchmarks, model comparison tools, and consistent evaluation methods for accuracy, latency, behavior, and real-world applicability. LayerLens is built for deep model analysis through Spaces, where teams can group benchmarks and evaluations, explore task strengths, and track performance patterns in context. It supports continuous evaluation by running ongoing evals across model versions, prompt changes, judge updates, and live traces, helping teams detect quality regressions, drift, silent failures, contamination, and policy issues before they affect production. -
20
Weavel
Weavel
Meet Ape, the first AI prompt engineer. Equipped with tracing, dataset curation, batch testing, and evals. Ape achieves an impressive 93% on the GSM8K benchmark, surpassing both DSPy (86%) and base LLMs (70%). Continuously optimize prompts using real-world data. Prevent performance regression with CI/CD integration. Human-in-the-loop with scoring and feedback. Ape works with the Weavel SDK to automatically log and add LLM generations to your dataset as you use your application. This enables seamless integration and continuous improvement specific to your use case. Ape auto-generates evaluation code and uses LLMs as impartial judges for complex tasks, streamlining your assessment process and ensuring accurate, nuanced performance metrics. Ape is reliable, as it works with your guidance and feedback. Feed in scores and tips to help Ape improve. Equipped with logging, testing, and evaluation for LLM applications.Starting Price: Free -
21
AvonAI
AvonAI
AvonAI keeps your AI agents aligned with your business by monitoring every customer conversation, controlling every interaction, and helping teams trust every outcome at scale. Your agents are live, handling real conversations with real customers, but agents do not manage themselves: they go off-script, drift from policies, and cannot keep up with business changes on their own. AvonAI reads every interaction and surfaces only the ones that matter, including policy violations, hallucinations, missing disclaimers, and other behavioral drift, so teams can find and fix risks in hours instead of weeks. It lets operations teams update agent knowledge and steer behavior in plain language, with no code and no developer ticket, while showing exactly what will change and allowing validation before anything goes live. AvonAI continuously tests agents against business directives, so the moment a model, prompt, or knowledge source changes, teams know whether the agent still behaves as intended. -
22
Atla
Atla
Atla is the agent observability and evaluation platform that dives deeper to help you find and fix AI agent failures. It provides real‑time visibility into every thought, tool call, and interaction so you can trace each agent run, understand step‑level errors, and identify root causes of failures. Atla automatically surfaces recurring issues across thousands of traces, stops you from manually combing through logs, and delivers specific, actionable suggestions for improvement based on detected error patterns. You can experiment with models and prompts side by side to compare performance, implement recommended fixes, and measure how changes affect completion rates. Individual traces are summarized into clean, readable narratives for granular inspection, while aggregated patterns give you clarity on systemic problems rather than isolated bugs. Designed to integrate with tools you already use, OpenAI, LangChain, Autogen AI, Pydantic AI, and more. -
23
LangSmith
LangChain
Unexpected results happen all the time. With full visibility into the entire chain sequence of calls, you can spot the source of errors and surprises in real time with surgical precision. Software engineering relies on unit testing to build performant, production-ready applications. LangSmith provides that same functionality for LLM applications. Spin up test datasets, run your applications over them, and inspect results without having to leave LangSmith. LangSmith enables mission-critical observability with only a few lines of code. LangSmith is designed to help developers harness the power–and wrangle the complexity–of LLMs. We’re not only building tools. We’re establishing best practices you can rely on. Build and deploy LLM applications with confidence. Application-level usage stats. Feedback collection. Filter traces, cost and performance measurement. Dataset curation, compare chain performance, AI-assisted evaluation, and embrace best practices. -
24
Taam Cloud
Taam Cloud
Taam Cloud is a powerful AI API platform designed to help businesses and developers seamlessly integrate AI into their applications. With enterprise-grade security, high-performance infrastructure, and a developer-friendly approach, Taam Cloud simplifies AI adoption and scalability. Taam Cloud is an AI API platform that provides seamless integration of over 200 powerful AI models into applications, offering scalable solutions for both startups and enterprises. With products like the AI Gateway, Observability tools, and AI Agents, Taam Cloud enables users to log, trace, and monitor key AI metrics while routing requests to various models with one fast API. The platform also features an AI Playground for testing models in a sandbox environment, making it easier for developers to experiment and deploy AI-powered solutions. Taam Cloud is designed to offer enterprise-grade security and compliance, ensuring businesses can trust it for secure AI operations.Starting Price: $10/month -
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AgentHub
AgentHub
AgentHub is a staging environment to simulate, trace, and evaluate AI agents in a private, sandboxed space that lets you ship with confidence, speed, and precision. With easy setup, you can onboard agents in minutes; a robust evaluation infrastructure provides multi-step trace logging, LLM graders, and fully customizable evaluations. Realistic user simulation employs configurable personas to model diverse behaviors and stress scenarios, and dataset enhancement synthetically expands test sets for comprehensive coverage. Prompt experimentation enables dynamic multi-prompt testing at scale, while side-by-side trace analysis lets you compare decisions, tool invocations, and outcomes across runs. A built-in AI Copilot analyzes traces, interprets results, and answers questions grounded in your own code and data, turning agent runs into clear, actionable insights. Combined human-in-the-loop and automated feedback options, along with white-glove onboarding and best-practice guidance. -
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Noteweave
Noteweave
Noteweave is an Intelligent Research Machines platform that helps teams go from research to executable production plans. It is built to stress-test scientific research, translate papers into validated experiments, and run R&D faster from one research-first workspace. Deep Analysis pressure-tests methods, evaluations, and robustness so failure modes surface before they reach production, helping teams detect production faults in academic papers pre-emptively, find missing evals, set up discrepancies, or misleading robustness trends, and identify technical faults faster. Explore searches across millions of papers, datasets, and code repositories, then synthesizes them into runnable production plans with traceable evidence. Noteweave helps users discover relevant research signals across 3 million+ AI/ML publications, optimize plans against constraints such as GPU utilization, translate academic methods into reproducible steps, and validate evaluation strategies more reliably.Starting Price: $18.99 per month -
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AgentScope
AgentScope
AgentScope is an AI-driven agent observability and operations platform that provides visibility, control, and performance analytics for autonomous AI agents across production workloads. It enables engineering and DevOps teams to monitor, diagnose, and optimize complex multi-agent applications in real time by capturing detailed telemetry on agent actions, decisions, resource usage, and outcome quality. With rich dashboards and timelines, AgentScope helps teams trace execution flows, identify bottlenecks, and understand how agents interact with external systems, APIs, and data sources, improving debugging and reliability for autonomous workflows. It supports customizable alerting, log aggregation, and structured event views so teams can quickly surface anomalous behavior or errors across distributed agent fleets. In addition to real-time monitoring, AgentScope provides historical analysis and reporting that help teams measure performance trends, model drift, etc.Starting Price: Free -
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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 -
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Teammately
Teammately
Teammately is an autonomous AI agent designed to revolutionize AI development by self-iterating AI products, models, and agents to meet your objectives beyond human capabilities. It employs a scientific approach, refining and selecting optimal combinations of prompts, foundation models, and knowledge chunking. To ensure reliability, Teammately synthesizes fair test datasets and constructs dynamic LLM-as-a-judge systems tailored to your project, quantifying AI capabilities and minimizing hallucinations. The platform aligns with your goals through Product Requirement Docs (PRD), enabling focused iteration towards desired outcomes. Key features include multi-step prompting, serverless vector search, and deep iteration processes that continuously refine AI until objectives are achieved. Teammately also emphasizes efficiency by identifying the smallest viable models, reducing costs, and enhancing performance.Starting Price: $25 per month -
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Voker
Voker
Voker is an Agent Analytics Platform for monitoring and improving AI agents in the wild, helping teams make sure their agents are helping, not just responding. It gives builders a way to track what AI agents are saying, identify knowledge gaps, detect abnormalities, and measure improvement over time without digging through logs or waiting for users to complain. Voker connects agent metrics to business outcomes by correlating conversational data with user data that teams are already collecting, making it easier to understand whether an agent is actually improving activation, retention, conversion, support quality, or other product goals. Its self-service analytics are designed for PMs, analysts, and business teams, giving them digestible insights without tickets, bottlenecks, or delays. Developers can install Voker through the SDK, including pip install voker, or use an AI coding tool to scaffold the SDK, add an API key, and instrument an agent in minutes.Starting Price: $80 per month -
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Convo
Convo
Kanvo provides a drop‑in JavaScript SDK that adds built‑in memory, observability, and resiliency to LangGraph‑based AI agents with zero infrastructure overhead. Without requiring databases or migrations, it lets you plug in a few lines of code to enable persistent memory (storing facts, preferences, and goals), threaded conversations for multi‑user interactions, and real‑time agent observability that logs every message, tool call, and LLM output. Its time‑travel debugging features let you checkpoint, rewind, and restore any agent run state instantly, making workflows reproducible and errors easy to trace. Designed for speed and simplicity, Convo’s lightweight interface and MIT‑licensed SDK deliver production‑ready, debuggable agents out of the box while keeping full control of your data.Starting Price: $29 per month -
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Coval
Coval
Coval is a simulation and evaluation platform designed to accelerate the development of reliable AI agents across chat, voice, and other modalities. By automating the testing process, Coval enables engineers to simulate thousands of scenarios from a few test cases, allowing for comprehensive assessments without manual intervention. Users can create test sets by adding customer transcripts or describing user intents in natural language, with Coval handling the formatting. The platform supports both text and voice simulations, facilitating the testing of AI agents against a set of scorecard metrics. Comprehensive evaluations of agent interactions are provided, enabling performance tracking over time and root cause analysis of specific runs. Coval also offers workflow metrics that provide observability into system processes, aiding in the optimization of AI agents.Starting Price: $300 per month -
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LangWatch
LangWatch
Guardrails are crucial in AI maintenance, LangWatch safeguards you and your business from exposing sensitive data, prompt injection and keeps your AI from going off the rails, avoiding unforeseen damage to your brand. Understanding the behaviour of both AI and users can be challenging for businesses with integrated AI. Ensure accurate and appropriate responses by constantly maintaining quality through oversight. LangWatch’s safety checks and guardrails prevent common AI issues including jailbreaking, exposing sensitive data, and off-topic conversations. Track conversion rates, output quality, user feedback and knowledge base gaps with real-time metrics — gain constant insights for continuous improvement. Powerful data evaluation allows you to evaluate new models and prompts, develop datasets for testing and run experimental simulations on tailored builds.Starting Price: €99 per month -
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Fluq
Fluq
Fluq is an AI agent observability and orchestration platform designed to give teams full visibility and control over how their AI agents operate in real time. It acts as a centralized “single pane of glass” where every agent action, LLM calls, tool usage, file operations, token consumption, and associated costs are tracked and visualized through detailed waterfall traces. By routing all agent requests through a lightweight proxy, Fluq requires minimal setup and works with any LLM provider or agent framework, allowing organizations to integrate it into existing systems without modifying code. It enables teams to inspect each decision an agent makes, drill into execution steps, and understand exactly how outcomes are generated, improving transparency and debuggability. It also includes governance features such as policy enforcement, spend limits, approval gates, and access controls, helping prevent issues like runaway costs, misuse of tools, or inaccurate outputs.Starting Price: $29 per month -
35
OpenAI Presence
OpenAI
OpenAI Presence is an enterprise product for deploying trusted AI agents across customer-facing and internal workflows. The platform helps organizations build voice and chat agents that can answer questions, resolve issues, use company systems, take approved actions, and escalate to human teams when needed. Presence combines model reasoning with policies, guardrails, standard operating procedures, simulations, evaluations, and escalation rules. Each deployment starts with a specific workflow, such as billing support, insurance claims, customer service, outbound sales, or employee IT requests. Codex-powered improvement workflows help teams review production sessions, identify gaps, test proposed updates, and approve controlled changes after launch. Built for eligible enterprise customers, OpenAI Presence helps companies put AI agents into production with reliability, governance, and deployment support from OpenAI teams and partners. -
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Adaline
Adaline
Iterate quickly and ship confidently. Confidently ship by evaluating your prompts with a suite of evals like context recall, llm-rubric (LLM as a judge), latency, and more. Let us handle intelligent caching and complex implementations to save you time and money. Quickly iterate on your prompts in a collaborative playground that supports all the major providers, variables, automatic versioning, and more. Easily build datasets from real data using Logs, upload your own as a CSV, or collaboratively build and edit within your Adaline workspace. Track usage, latency, and other metrics to monitor the health of your LLMs and the performance of your prompts using our APIs. Continuously evaluate your completions in production, see how your users are using your prompts, and create datasets by sending logs using our APIs. The single platform to iterate, evaluate, and monitor LLMs. Easily rollbacks if your performance regresses in production, and see how your team iterated the prompt. -
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LangChain
LangChain
LangChain is a powerful, composable framework designed for building, running, and managing applications powered by large language models (LLMs). It offers an array of tools for creating context-aware, reasoning applications, allowing businesses to leverage their own data and APIs to enhance functionality. LangChain’s suite includes LangGraph for orchestrating agent-driven workflows, and LangSmith for agent observability and performance management. Whether you're building prototypes or scaling full applications, LangChain offers the flexibility and tools needed to optimize the LLM lifecycle, with seamless integrations and fault-tolerant scalability. -
38
Hamming
Hamming
Prompt optimization, automated voice testing, monitoring, and more. Test your AI voice agent against 1000s of simulated users in minutes. AI voice agents are hard to get right. A small change in prompts, function call definitions or model providers can cause large changes in LLM outputs. We're the only end-to-end platform that supports you from development to production. You can store, manage, version, and keep your prompts synced with voice infra providers from Hamming. This is 1000x more efficient than testing your voice agents by hand. Use our prompt playground to test LLM outputs on a dataset of inputs. Our LLM judges the quality of generated outputs. Save 80% of manual prompt engineering effort. Go beyond passive monitoring. We actively track and score how users are using your AI app in production and flag cases that need your attention using LLM judges. Easily convert calls and traces into test cases and add them to your golden dataset. -
39
RagMetrics
RagMetrics
RagMetrics is a production-grade evaluation and trust platform for conversational GenAI, designed to assess AI chatbots, agents, and RAG systems before and after they go live. The platform continuously evaluates AI responses for accuracy, groundedness, hallucinations, reasoning quality, and tool-calling behavior across real conversations. RagMetrics integrates directly with existing AI stacks and monitors live interactions without disrupting user experience. It provides automated scoring, configurable metrics, and detailed diagnostics that explain when an AI response fails, why it failed, and how to fix it. Teams can run offline evaluations, A/B tests, and regression tests, as well as track performance trends in production through dashboards and alerts. The platform is model-agnostic and deployment-agnostic, supporting multiple LLMs, retrieval systems, and agent frameworks.Starting Price: $20/month -
40
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. -
41
Scorable
Scorable
Scorable is an AI evaluation and monitoring platform designed to help developers measure, control, and improve the behavior of applications built with large language models. It enables teams to create customized automated evaluators, sometimes referred to as AI “judges”, that assess how an AI system responds to users and whether its outputs meet defined quality standards such as accuracy, relevance, helpfulness, tone, and policy compliance. Developers can describe what they want to measure in plain language, and the platform generates a tailored evaluation stack that tests AI outputs against context-specific criteria rather than generic benchmarks. These evaluators can be embedded directly into application code, allowing AI systems such as chatbots, retrieval-augmented generation (RAG) systems, or autonomous agents to be continuously monitored in production environments.Starting Price: $19 per month -
42
Fiddler AI
Fiddler AI
Fiddler is a pioneer in Model Performance Management for responsible AI. The Fiddler platform’s unified environment provides a common language, centralized controls, and actionable insights to operationalize ML/AI with trust. Model monitoring, explainable AI, analytics, and fairness capabilities address the unique challenges of building in-house stable and secure MLOps systems at scale. Unlike observability solutions, Fiddler integrates deep XAI and analytics to help you grow into advanced capabilities over time and build a framework for responsible AI practices. Fortune 500 organizations use Fiddler across training and production models to accelerate AI time-to-value and scale, build trusted AI solutions, and increase revenue. -
43
Valid Eval
Valid Eval
Complex group deliberations don't have to be painful. Whether you're tasked with ranking hundreds of competing proposals, judging a dozen live pitches, or managing a multi-phase innovation program, there's an easier way. A better way. Valid Eval is an online evaluation system for organizations that make and defend tough decisions. It's a secure SaaS platform that works efficiently at virtually any scale so you can involve as many applicants, subjects, domain experts, and judges as it takes to do the job right. Combining best practices from the learning sciences and systems engineering, Valid Eval delivers defensible, data driven results and provides robust reporting tools that help you measure and monitor performance and demonstrate mission alignment. Best of all, it provides an unprecedented degree of transparency that promotes accountability and builds trust in the process. -
44
Helicone
Helicone
Track costs, usage, and latency for GPT applications with one line of code. Trusted by leading companies building with OpenAI. We will support Anthropic, Cohere, Google AI, and more coming soon. Stay on top of your costs, usage, and latency. Integrate models like GPT-4 with Helicone to track API requests and visualize results. Get an overview of your application with an in-built dashboard, tailor made for generative AI applications. View all of your requests in one place. Filter by time, users, and custom properties. Track spending on each model, user, or conversation. Use this data to optimize your API usage and reduce costs. Cache requests to save on latency and money, proactively track errors in your application, handle rate limits and reliability concerns with Helicone.Starting Price: $1 per 10,000 requests -
45
NEO
NEO
NEO is an autonomous machine learning engineer: a multi-agent system that automates the entire ML workflow so that teams can delegate data engineering, model development, evaluation, deployment, and monitoring to an intelligent pipeline without losing visibility or control. It layers advanced multi-step reasoning, memory orchestration, and adaptive inference to tackle complex problems end-to-end, validating and cleaning data, selecting and training models, handling edge-case failures, comparing candidate behaviors, and managing deployments, with human-in-the-loop breakpoints and configurable enablement controls. NEO continuously learns from outcomes, maintains context across experiments, and provides real-time status on readiness, performance, and issues, effectively creating a self-driving ML engineering stack that surfaces insights, resolves standard settlement-style friction (e.g., conflicting configurations or stale artifacts), and frees engineers from repetitive grunt work. -
46
OpenAI Agents SDK
OpenAI
The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm. The Agents SDK has a very small set of primitives, agents, which are LLMs equipped with instructions and tools; handoffs, which allow agents to delegate to other agents for specific tasks; and guardrails, which enable the inputs to agents to be validated. In combination with Python, these primitives are powerful enough to express complex relationships between tools and agents, and allow you to build real-world applications without a steep learning curve. In addition, the SDK comes with built-in tracing that lets you visualize and debug your agentic flows, evaluate them, and even fine-tune models for your application.Starting Price: Free -
47
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. -
48
Trismik
Trismik
Trismik is an AI model evaluation platform designed to help teams choose the right large language model for their specific use case using real data instead of assumptions or generic benchmarks. It focuses on turning model experimentation into clear, evidence-based decisions by allowing users to test and compare multiple models directly on their own datasets, rather than relying on public leaderboards or limited manual testing. It introduces tools such as QuickCompare, which enables side-by-side evaluation of 50+ models across key dimensions like quality, cost, and speed, making trade-offs visible and measurable in real-world conditions. Trismik also incorporates adaptive evaluation techniques inspired by psychometrics, dynamically selecting the most informative test cases and automatically scoring outputs across factors such as factual accuracy, bias, and reliability.Starting Price: $9.99 per month -
49
Traceloop
Traceloop
Traceloop is a comprehensive observability platform designed to monitor, debug, and test the quality of outputs from Large Language Models (LLMs). It offers real-time alerts for unexpected output quality changes, execution tracing for every request, and the ability to gradually roll out changes to models and prompts. Developers can debug and re-run issues from production directly in their Integrated Development Environment (IDE). Traceloop integrates seamlessly with the OpenLLMetry SDK, supporting multiple programming languages including Python, JavaScript/TypeScript, Go, and Ruby. The platform provides a range of semantic, syntactic, safety, and structural metrics to assess LLM outputs, such as QA relevancy, faithfulness, text quality, grammar correctness, redundancy detection, focus assessment, text length, word count, PII detection, secret detection, toxicity detection, regex validation, SQL validation, JSON schema validation, and code validation.Starting Price: $59 per month -
50
EasyMock
EasyMock
Most parts of a software system do not work in isolation, but collaborate with other parts to get their job done. In a lot of cases, we do not care about using real collaborators implementation in unit testing, as we trust these collaborators. Mock objects replace collaborators of the unit under test. To test a unit in isolation or mount a sufficient environment, we have to simulate the collaborators in the test. A Mock Object is a test-oriented replacement for a collaborator. It is configured to simulate the object that it replaces in a simple way. In contrast to a stub, a Mock Object also verifies whether it is used as expected. EasyMock has been the first dynamic Mock Object generator, relieving users of hand-writing Mock Objects, or generating code for them. EasyMock provides Mock Objects by generating them on the fly using Java proxy mechanism.
