Paid tool

Openlayer

AI evaluation and observability platform to test, monitor, and govern ML and LLM models.

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Intro

What is Openlayer?

Openlayer is an enterprise-grade AI evaluation and observability platform built for ML and software engineering teams. Unlike traditional software observability, AI applications observability requires specialized tools to handle the non-deterministic nature of model outputs. Openlayer serves as an advanced LLM evaluator, allowing teams to test, monitor, and govern AI systems from standard ML to complex LLMs. It functions as an automated AI testing framework, helping data scientists and developers catch model performance AI regressions, validate data quality, and set up robust LLM ops tools. By offering deeper insights into evaluation metrics, it helps teams understand how their AI models behave in both development and production environments.

Openlayer at a glance
Free Trial available, Enterprise plan requires contacting sales24K monthly visitsPaid access
Pricing

Openlayer Pricing Plans

Compare Openlayer free options, Openlayer paid pricing plans, and usage notes before you choose the best way to use this AI tool in 2026.

Free Trial available, Enterprise plan requires contacting sales

Free

Ready to start for everyone. Includes 1 member, 5 projects, 1 inference pipeline per project, 20,000 inferences/mo, unlimited commits, 20 tests per project, automatic CI/CD, templates, observability & tracing, and community support.

Custom

Tailored for larger businesses. Includes unlimited members, projects, and inferences, custom pipelines, team access controls, on-premise deployment, explainability, SAML SSO, 99.99% SLA, compliance reports, and advanced support.

Pricing updated:Jun 12, 2026

Features

Openlayer AI Features

Unified AI evaluation and observability for ML and LLM systemsReal-time production tracing, alerts, cost, and latency trackingExpansive, customizable library of tests (e.g., PII detection, harmfulness, fraud false-positive validation)Seamless workflow integrations via Git, CLI, SDKs, and REST APIsPre-configured project templates for RAG, simple chatbots, and tabular data classificationCollaborative workspace featuring human feedback annotations, comments, and role-based access control
Pros & Cons

Openlayer Pros and Cons

Pros

  • Smoothly bridges the gap from prototype to production with continuous testing
  • Robust out-of-the-box support for major LLM providers and frameworks like LangChain
  • Helps prevent regressions and drastically reduces debugging hours through real-time error analysis
  • Offers an expansive set of pre-configured templates to get started in seconds

Limitations

  • The Basic free trial is limited to 1 member and 5 projects
  • Advanced features like explainability, custom data retention, and SAML SSO require an Enterprise contract

Openlayer FAQ

Traditional observability focuses on system health metrics like uptime, CPU usage, and standard application logs. AI applications observability, however, requires monitoring complex LLM evaluation metrics, data drifts, semantic context precision, prompt safety, and model performance AI characteristics that are unique to machine learning workflows.