Automated model testing
Run more than 100 built-in and customizable tests to evaluate hallucination, completeness, relevance, toxicity, bias, and other output qualities.
Openlayer
ClaimOpenlayer is an AI governance and observability platform for testing, monitoring, and controlling ML and LLM systems before release and in production.
AI governance and observability platform
Openlayer is an AI governance and observability platform for evaluating, monitoring, and controlling ML and LLM systems. The product combines offline evaluation, production tracing, live guardrails, and automated compliance workflows so teams can test systems before release and watch them in production.
The site positions Openlayer for builders working on GenAI apps, agents, copilots, retrieval-augmented generation, and traditional ML models. It supports structured testing, request tracing, data quality checks, and compliance alignment with standards such as NIST, ISO/IEC 42001, OWASP, and the EU AI Act.
Core capabilities
Version and change comparisons
Compare prompts, model providers, and system changes to spot regressions and track improvements over time.
Production observability
Trace prompts, tool calls, intermediate steps, responses, latency, and cost across LLM pipelines and agent workflows.
Real-time guardrails and alerts
Run live safety and performance checks and set alerts for issues such as prompt injection, data leaks, latency spikes, and inappropriate content.
CI/CD and workflow integration
Use SDKs, APIs, CLI workflows, GitHub Actions, and Git-based processes to trigger tests as part of development and deployment.
Collaborative evaluation
Support shared review and feedback across engineers, product managers, researchers, and other stakeholders in a single workspace.
Common ways teams use Openlayer
Pre-release model evaluation
Evaluate prompts, model outputs, and release candidates before shipping by using test suites, comparisons, and LLM-as-a-judge scoring to catch regressions early.
Production observability
Monitor live GenAI systems for prompt injection, toxic output, data leaks, latency, and cost issues, then investigate failures with full request traces.
Data quality monitoring
Run automated checks on incoming data to detect schema changes, drift, and anomalies before bad data reaches models.
Cross-functional review
Coordinate review among engineers, PMs, researchers, and other stakeholders with shared evaluation results, comments, and comparison workflows.
Governance and compliance workflows
Align AI systems with governance and compliance frameworks by using testing and monitoring workflows that support standards such as NIST, ISO/IEC 42001, OWASP, and the EU AI Act.
Pros and Cons
Pros
- Covers both evaluation and observability in one platform, reducing the need for separate tools.
- Supports both LLM and traditional ML workflows, including structured outputs and multimodal or hybrid models.
- Offers built-in testing, tracing, alerts, and guardrails for both development and production stages.
- Includes workflow integrations such as CLI, SDK, APIs, and Git-based automation.
- Shows enterprise-oriented options on the pricing page, including SAML SSO, on-prem deployment, and white-glove onboarding.
Cons
- The product pages give high-level capability descriptions, but they do not fully document implementation details or setup complexity.
- Pricing information is plan-level rather than per-seat or usage-based breakdowns beyond the Basic inference allowance shown on the pricing page.
FAQ
How do you evaluate models in Openlayer?
Openlayer supports structured testing for LLMs and ML models, including LLM-as-a-judge scoring, rubric-based evaluation, prompt test coverage, and comparisons across versions or prompts.
What tools and frameworks does Openlayer integrate with?
The source pages state support for OpenAI, Anthropic, Hugging Face, LangChain, OpenTelemetry, Snowflake, GitHub Actions, GitLab CI, and more, depending on the workflow and product area.
What can you monitor or trace in production?
Openlayer can trace prompts, tool calls, latency, cost, model responses, and failure modes across multi-step and retrieval-based workflows.
Does Openlayer offer both self-serve and enterprise plans?
The pricing page shows a Basic trial plan and an Enterprise plan. Basic includes 20,000 inferences per month, CLI/SDK/API access, and community support, while Enterprise adds options such as team access controls, on-prem deployment, explainability, SAML SSO, and white-glove onboarding.
Quick Facts
- Category
- AI governance and observability
- Primary users
- Builders working on GenAI apps, LLM systems, and ML models
- Deployment model
- Cloud platform with enterprise on-prem option
- Pricing
- Basic trial and Enterprise plans listed
- Source domain
- openlayer.com
- Workflow
- Offline evaluation, production monitoring, and compliance tracking