Openlayer pricing, free plan and limits

Last updated: 2026-10-25

Founded in 2021, Openlayer is an AI governance platform that keeps one inventory of every AI system, scores risk at intake, and runs 175+ evaluation tests before deployment. Live guardrails stop injected prompts and personal-data leaks, and pre-built mappings span eight regulatory frameworks, from the EU AI Act to ISO/IEC 42001.

About Openlayer

Openlayer is an AI governance and observability platform built by Openlayer Inc, a San Francisco startup founded in 2021 by Vikas Nair, Gabriel Bayomi Tinoco Kalejaiye, and Rishab Ramanathan. It gives engineering, risk, and compliance teams one place to test AI systems before release, watch them in production, and prove to auditors that oversight actually happened, a problem underscored by S&P Global's 2026 finding that 88% of AI agents built inside companies never reach production.

Before an AI system ships, the platform screens it against a built-in library covering hallucination, bias, toxicity, and adversarial-robustness checks, scored with an LLM-as-a-judge approach Openlayer reports correlates with human raters 81.3% of the time. In production, it watches LLM calls, retrieval pipelines, and agent runs end to end, with dedicated support for tracing the Anthropic Claude Agent SDK, and steps in live to stop an injected prompt or a PII leak from ever reaching a user. A governance layer keeps a central inventory of every AI system, built or bought, with an owner, a use case and a risk score set at intake, then routes each one through approval workflows and lifecycle stages with a time-stamped audit trail. Pre-built mappings to 8 frameworks, including the EU AI Act, ISO/IEC 42001, NIST AI RMF and OSFI E-23, turn manual audit prep into a standing record.

Openlayer is built for AI and ML platform engineers shipping agents to production, MLOps and LLMOps teams who need CI/CD-gated regression tests, and Chief Risk Officers or AI governance leads who have to prove oversight to regulators. It is used across regulated sectors including financial services, insurance, healthcare, and telecom; customers named on Openlayer's site include eBay, Comcast LIFT Labs, Sun Life, DIRECTV, and Telefonica, which announced a 2026 partnership to resell the platform across Europe and Latin America. Jericho Security reports 6x deployment frequency and 53% higher throughput after standardizing on Openlayer.

The Basic tier costs nothing and stays free indefinitely, covering a handful of projects and a working monthly inference allowance with a shorter data-retention window than the paid plan; official SDKs are available across the major languages engineering teams already use, alongside a CLI, REST API, and Git-based workflow. Enterprise is quoted per customer, lifts the member and project caps and adds on-premise or VPC deployment, single sign-on, and a stronger uptime SLA; Openlayer does not publish an Enterprise rate card, so budget planning requires a sales conversation.

Analyst attention has followed: Openlayer holds a place among Gartner's tracked vendors for AI evaluation and observability heading into 2026, and investors have backed that with $19.4M in total funding, including a $14.5M Series A led by Y Combinator, Quiet Capital, and Race Capital. On the trust side, the company has completed a SOC 2 Type II audit, with Vanta and Insight Partners as auditors, and lists GDPR and HIPAA on its trust center, alongside air-gapped deployment for customers who cannot use shared infrastructure.

Pricing

Basic: free, 1 member, 5 projects, 20 tests/project, 20,000 inferences/month, 3-month data retention, community support. Enterprise: custom pricing, unlimited members/projects/pipelines/tests, on-premise or VPC deployment, SAML SSO, 99.99% uptime SLA, white-glove onboarding.

Key Features

• 175+ prebuilt evaluation tests: The test library screens for hallucinations, bias, toxicity, and adversarial robustness before an AI system ships, plus custom metrics pushed via CLI.
• Real-time guardrails: Runtime guardrails block prompt injection and PII leakage before a response leaves the API boundary, enforcing policy in production without a separate manual review step.
• Full-lifecycle observability: Traces LLM calls, retrieval pipelines, and multi-step agent runs in production, including first-party tracing for the Anthropic Claude Agent SDK covering tool calls, MCP tools, and subagents.
• Automated compliance mapping: Eight pre-built mappings, among them NIST AI RMF, OSFI E-23 and state-level rules, generate audit-ready evidence instead of manual documentation.
• CI/CD-gated testing: Runs evaluation suites inside existing CI/CD pipelines via SDK, CLI, or Git workflow so a regression in an AI system blocks a merge the same way a failing unit test would.
• Multi-language SDK and MCP server: Ships official SDKs for Python, TypeScript, Go, Ruby, and Java plus an Openlayer MCP server that exposes evaluation and monitoring data directly to LLM agents.
• AI system inventory and risk scoring: Logs each AI system, whether made in-house or purchased, against an owner, use case and lifecycle stage, then scores inherent risk at intake across business, operational and technical dimensions.
• Approval workflows and audit trail: Structured intake and stage-gated lifecycle reviews log each decision with a time stamp, a named owner and linked evidence that an auditor can follow.

Pros

• Named a Representative Vendor in the 2026 Gartner Market Guide for AI Evaluation and Observability Platforms, an analyst-level validation few YC-backed observability startups have earned.
• The free Basic tier requires no credit card and stays free indefinitely, unlike Braintrust which jumps straight from a free tier to a $249/month plan with no mid-tier option in between.
• SOC 2 Type II audited by Vanta and Insight Partners with GDPR and HIPAA compliance stated on its trust center, plus air-gapped and VPC self-hosting for customers that cannot use shared infrastructure.

Cons

• No published pricing above the Basic tier; the Enterprise plan is quote-only, making budget forecasting harder than usage-metered rivals like Braintrust ($3/GB tracing) or Langfuse.
• Basic tier caps data retention at 3 months and 20 tests per project, tight for teams running continuous regression suites across many agents.
• No independently verifiable G2, Trustpilot, or Product Hunt review count was found at time of writing, so buyers must rely on named case studies such as eBay and Comcast LIFT Labs rather than aggregate third-party ratings.

Visit Openlayer Official Website