Best Free AI Observability Tools in 2026

Weights & Biases (W&B) is the ML platform for experiment tracking, model management, and collaboration. Track every aspect of your machine learning experiments - hyperparameters, metrics, code, and artifacts. Compare runs with interactive visualizations and share results with your team. W&B integrates with PyTorch, TensorFlow, and all major ML frameworks. Features include model registry, dataset versioning, and production monitoring.

MLflow manages the machine learning lifecycle. Experiment tracking, model registry, and deployment—MLOps platform that's open source and widely adopted. The experiment tracking is solid. The model registry helps management. The deployment options are flexible. ML teams use MLflow because it's the open-source MLOps standard.

Neptune.ai tracks machine learning experiments with collaboration focus. Log experiments, compare runs, share results—MLOps for teams that work together. The collaboration features help teams. The tracking is comprehensive. The comparison is visual. ML teams wanting collaborative experiment tracking use Neptune for team MLOps.

Helicone is an AI Gateway and LLM Observability platform designed to help companies build, debug, and analyze their AI applications. It provides tools to route requests, identify and fix issues, and gain insights into application performance. Helicone aims to make AI development more reliable and efficient for fast-growing AI companies. The platform offers features like request monitoring, usage-based billing, caching, rate limits, automatic fallbacks, and data retention. It also includes advanced capabilities for prompts and testing, such as a playground, scores, and datasets. Helicone is built to scale with teams of all sizes, from individual developers to large enterprises, offering various plans with increasing features and support. Helicone is ideal for developers, teams, and enterprises working with AI applications who need robust tools for observability, performance optimization, and compliance. It helps users understand AI performance bottlenecks, save time on debugging, and ensure their AI products are reliable and scalable.

ClearML tracks machine learning experiments and manages model lifecycle without lock-in. Log metrics, compare runs, manage datasets—MLOps infrastructure you can self-host or run in their cloud. Experiment tracking captures everything reproducibility requires. Pipeline orchestration handles training workflows. Model serving deploys to production. ML teams wanting open-source MLOps tools choose ClearML for experiment tracking and pipeline management they control.

Langfuse is an open-source LLM engineering platform designed to help developers debug, evaluate, and improve their large language model (LLM) applications. It provides comprehensive observability features, including traces, evaluations, prompt management, and metrics, allowing users to inspect failures and build evaluation datasets. The platform integrates with popular LLM/agent libraries and is based on OpenTelemetry. Langfuse is ideal for developers and teams building and deploying LLM-powered applications, from hobby projects to large-scale enterprise solutions. It offers tools for prompt versioning, experimentation, and caching, along with robust evaluation capabilities including LLM-as-judge evaluators and human annotation. Key benefits include faster debugging, data-driven improvement of LLM performance, and streamlined prompt management, ultimately leading to more reliable and effective AI applications.

Portkey provides a comprehensive production stack for AI teams building with Large Language Models (LLMs). It offers an AI Gateway for unified access to over 1600 LLMs, enabling teams to connect, manage, and secure AI interactions with features like smart routing, caching, and key management. This gateway helps optimize costs, ensure reliability, and simplify integration across various models and providers. Beyond the gateway, Portkey includes robust observability tools to monitor LLM behavior, detect anomalies, and manage usage proactively with real-time dashboards. It also features guardrails for keeping AI outputs in check, governance capabilities for security and access control, and prompt management for creating, testing, and versioning prompts. Portkey is designed for developers and AI teams looking to move their Gen AI applications from prototyping to production efficiently and reliably.

Lakera is an AI-native security platform designed to protect generative AI applications, agents, and multi-chain applications (MCPs) for enterprise teams. It offers real-time threat detection, prompt attack prevention, and data leakage protection, ensuring that AI applications can be deployed securely and at scale. The platform is built to address the unique security challenges of GenAI, which traditional security solutions are not equipped to handle. The product suite includes Lakera Guard for runtime protection of AI applications and Lakera Red for risk-based GenAI Red Teaming. Lakera Guard provides an AI Application Firewall, security center for monitoring, guardrails for content control, and integrates with existing SIEMs. Lakera Red focuses on vulnerability management and attack simulations. Lakera emphasizes continuous, evolving security, industry-leading precision, ultra-low latency, and central policy control, supporting multimodal and model-agnostic deployments.

LangSmith is an observability platform designed to help teams build and ship reliable AI agents and LLM applications. It provides robust tracing capabilities to quickly debug non-deterministic LLM app behavior, allowing developers to see step-by-step what their agent is doing and fix issues to improve latency and response quality. The platform also offers live dashboards for monitoring business-critical metrics such as costs, latency, and response quality, with alerting features to notify users of issues and enable drilling down to root causes. Furthermore, LangSmith automatically discovers usage patterns and issues by clustering similar conversations, helping users understand what their users want and identify systemic problems. It integrates seamlessly with various frameworks, including LangChain and LangGraph, and supports OpenTelemetry for unified observability.

Seldon Core is an open-source platform designed to deploy, monitor, explain, and continuously improve machine learning models in production. It helps organizations manage the complexities of real-time AI, ensuring efficient operations and cost optimization across various environments. The platform provides built-in standardization and observability, making it flexible enough to fit diverse system requirements. Seldon Core+ offers additional layers of expert support, accelerators, and tailored guidance to help teams unlock continuous value beyond initial deployment. This includes dedicated customer success managers, guaranteed response times through SLAs, and hands-on assistance for technical issues. It also provides specialized modules for GenAI deployment, model performance monitoring (MPM), model explainability (Alibi Explain), and outlier/drift detection (Alibi Detect). The product is aimed at MLOps professionals, data scientists, and AI engineers who need to build trust in their production ML systems, scale deployments with confidence, and maintain compliance. It supports both on-premise and cloud deployments, offering tools to manage real-time innovation and optimize AI costs.

WhyLabs, Inc. has discontinued its operations as a company. However, the complete WhyLabs platform has been open-sourced to support future iterations of AI observability research. This platform was designed to enable responsible AI adoption by providing tools for monitoring and securing AI systems. Key components include `whylogs`, an open standard for data logging that facilitates privacy-preserving logging and monitoring for AI, and `langkit`, an open-source toolkit specifically for monitoring and securing Large Language Models (LLMs) while maintaining privacy. These tools are aimed at helping teams and researchers advance the field of responsible AI operations.

Fiddler AI provides an AI Control Plane designed for enterprise agents, offering comprehensive visibility, understanding, and control over AI systems. It enables organizations to track agentic applications throughout their lifecycle, gain insights into agent behaviors, and pinpoint root causes with full execution context and decision lineage. The platform ensures continuous monitoring and course correction, moving beyond passive evaluation systems to deliver high performance and protect against risks. Fiddler AI also specializes in MLOps, providing an observability platform that helps operationalize the entire machine learning workflow. It enables teams to trust model outcomes, align AI solutions with dynamic business contexts, and ensure model quality through effective governance. The platform supports the full ML lifecycle from training to production, offering features like continuous monitoring, deep explainability, rich analytics, trust and fairness assessments, and robust model governance, making it crucial for organizations deploying and managing AI at scale.

Soda is a data quality platform that helps organizations prevent data incidents before they impact production. It offers a unified workflow for both engineers and business users, powered by advanced AI. The platform automatically detects, explains, and helps resolve data quality issues as they emerge, directly at the source within your environment. Soda leverages proprietary AI for faster and more accurate data quality monitoring, including metrics monitoring, record-level anomaly detection, and AI automations for generating data contracts and checks. It provides comprehensive data observability with interactive visualizations, smart thresholds, and continuous AI improvement through user feedback. This allows teams to scale monitoring efforts without manual scripting, discover unknown data issues, and automate data and pipeline testing.

Parea AI provides a comprehensive platform for developing and deploying Large Language Model (LLM) applications. It offers tools for experiment tracking, observability, and human annotation, enabling teams to ensure the quality and performance of their AI systems before and after deployment. The platform focuses on streamlining the LLM development lifecycle from prompt engineering to production monitoring. This product is designed for AI developers, MLOps engineers, and product teams working with LLMs who need to rigorously test, evaluate, and debug their applications. It helps answer critical questions about model performance, regression detection, and the impact of model upgrades, ultimately accelerating the confident shipment of LLM apps to users. Parea AI supports both Python and JavaScript/TypeScript environments with native SDKs and integrations with popular LLM providers and frameworks.

AgentOps is a developer observability platform for building, debugging, and monitoring AI agents and LLM applications. It tracks events like LLM calls, tool usage, and multi-agent interactions across 400+ models. Features include time-travel debugging to replay agent runs, complete audit trails for security and compliance, token cost tracking, and fine-tuning pipelines. Free tier covers 5,000 events/month; Pro starts at $40/month.