Openlayer

Editor’s note: This Openlayer review is based on my hands-on test of Openlayer’s Basic plan and its Python SDK (0.36.0), in October 2026. It weighs pricing, evaluation and test coverage, production tracing and guardrails, governance features, and how Openlayer stacks up against Braintrust, LangSmith and Arize.

Quick verdict: I recommend Openlayer for ML and AI platform teams at regulated companies that need tests, tracing and compliance evidence (EU AI Act, NIST AI RMF, ISO 42001) in one place. The trade-off is no self-serve paid tier: you go from a free plan capped at 1 member and 20 tests per project to an unpublished Enterprise quote.

Openlayer runs 100+ catalog tests on every commit and on live production traces, then maps the results to compliance frameworks. In this Openlayer review, I’ll take a closer look at Openlayer’s pricing, testing, tracing and governance features, so you can see exactly whether it justifies a sales call over LangSmith’s published plans.

Key Takeaways

• Basic is free with 1 member, 5 projects, 20,000 inference logs per month and 20 tests per project.
• Enterprise pricing is not published, and SSO, RBAC, on-prem and the SLA sit behind it.
• The 100+ catalog tests cover hallucination, PII and prompt injection, plus 15+ session-level agent tests.
• openlayer push fails the CI job when any test fails, but native Git integration supports GitHub only.
• The 2026 releases lean into governance: the Gateway, project lifecycle stages with reviewers, and EU AI Act, NIST AI RMF and ISO 42001 frameworks.

Pros and Cons

Openlayer Pricing: How Much Does It Cost?

Openlayer sells two plans: Basic (free) and Enterprise (custom quote), with no published price for adding a second seat. Openlayer labels Basic a trial; Enterprise is quoted on scale, deployment and support, with a 99.99% SLA and white-glove onboarding.

• Basic (free): for one engineer prototyping evals and monitoring. It includes 5 projects, unlimited commits and 3 months of data retention, with 20 tests and 1 inference pipeline per project.
• Enterprise (custom quote): for teams that need collaboration, access controls or private deployment. Members, projects, logs, tests and workspaces are unlimited; pipelines, retention and volume are negotiated.

Plan detail	Basic	Enterprise
Price	Free	Custom quote
Members	1	Unlimited
Projects	5	Unlimited
Inference pipelines	1 per project	Custom
Inference logs	20,000/month	Unlimited
Tests	20 per project	Unlimited
Workspaces	1	Unlimited
Data retention	3 months	Custom
SLA	Not stated	99.99%
Support	Community Discord	Slack, custom support, white-glove onboarding
SAML SSO	No	Yes
RBAC	No	Yes
On-prem deployment	No	Yes
Compliance reports	No	Yes
Custom tests	No	Yes
Data export	No	Yes

Basic still covers the full development loop: repo import, automatic CI/CD, template projects, the AI test library, observability and tracing, alerting integrations, and CLI, SDK and API access. What it can’t show you is the governance side, since compliance reports, custom tests, explainability and data annotation are Enterprise-only.

Openlayer is also sold through the AWS, Azure and Google Cloud marketplaces, which helps if a committed cloud budget has to cover the spend. The AWS listing is a 12-month custom contract whose units and terms are agreed with Openlayer’s sales team, and AWS infrastructure costs may apply on top.

Is Openlayer Good Value for Money?

• Generous free logging: Basic includes 20,000 inference logs a month against 5,000 base traces on LangSmith’s free Developer plan. Logs and traces are different billing units, so compare against your real traffic.
• Weak team trial: a single seat blocks a shared evaluation, while Braintrust’s free Starter plan allows unlimited users.
• No middle tier: Arize AX Pro starts at $50 a month, LangSmith Plus at $39 per seat a month and Braintrust Pro at $249 a month; Openlayer has no published equivalent.
• Enterprise value depends on the quote: consolidating tests, tracing and governance into one contract can replace two tools, but only negotiated volume, retention and support terms will tell you whether it costs less.

Reviewer’s Notes: One engineer prototyping evals should stay on Basic. Any team that needs a second seat, SSO or on-prem deployment should open the Enterprise conversation early and get the per-log or volume terms in writing, including how extra inference pipelines and longer retention change the quote.

My Experience With Openlayer

Openlayer splits every project into Development mode (pre-production commits) and Monitoring mode (live traffic), and I set up both: a traced app for monitoring and a GitHub Actions gate for development.

Signing Up and Creating a Project

The first choice is the data region: US at app.openlayer.com or EU at eu.openlayer.com. Accounts are not shared between regions, so an EU-resident team has to sign up on the EU host from day one rather than migrate later.

From the workspace home, I created the project in three steps:

1. I clicked Create in the top right.
2. I picked Code you own for my own application. The other option, Vendor AI, registers a third-party tool without code access.
3. I followed the onboarding prompts to name the project and choose development or monitoring.

The same project can be created from a terminal with openlayer login (using an API key) and openlayer link in the repo folder, or through the REST API with POST /projects. The API examples use taskType llm-base for LLM projects.

Tracing My First LLM App

Tracing needs two environment variables, OPENLAYER_API_KEY and OPENLAYER_INFERENCE_PIPELINE_ID, both available once the monitoring project exists. I then called init() from openlayer.lib, which auto-instruments installed LLM SDKs such as OpenAI, and decorated my entry function with @trace():

from openlayer.lib import init, trace

init()  # auto-instruments installed SDKs such as OpenAI

@trace()
def answer(question: str) -> str:
    ...

Nested functions that also carry @trace() show up as steps inside one trace, so a retrieval call and the model call land in the same record instead of two unrelated requests. Traces appear on the project’s Data page, where monitoring tests evaluate them on a schedule. The Python SDK requires Python 3.9 or later; TypeScript, Go, Ruby and Java SDKs cover the same tracing for non-Python services.

Adding Tests From the Catalog

Tests come in three forms: bundles, single catalog tests, and custom tests built on custom metrics (the last one Enterprise-only). Six bundles cover the common goals:

• Agentic/RAG evaluation for answer quality and agent sessions.
• Usage tracking for runtime consumption.
• OWASP security for LLM security risks.
• EU AI Act alignment for framework evidence.
• Bias and fairness for group parity.
• Data quality for warehouse and dataset integrity.

Every test takes a threshold, and the same test runs on development commits and on live traces. Failures notify in-app, by email or in Slack, and signed webhooks can feed any other alerting tool.

Reviewer’s Notes: On Basic, the 20-tests-per-project cap fills quickly if you add whole bundles. I’d pick individual tests for the failure modes that actually block a release (groundedness, prompt injection, cost per request) and add bundles only after moving to Enterprise.

Gating Releases in GitHub Actions

The GitHub Actions gate needs two repository secrets, OPENLAYER_API_KEY and OPENLAYER_PROJECT_ID; the project ID sits in .openlayer/config.json after openlayer link. My .github/workflows/openlayer.yaml ran four steps:

1. Check out the repo and install the Openlayer CLI.
2. Run openlayer install.
3. Run openlayer batch to generate model outputs.
4. Run openlayer push to upload the commit for evaluation.

openlayer push waits for results and fails the job if any test fails or processing passes 10 minutes. Native Git integration is GitHub-only; Jenkins and CircleCI pipelines call the CLI or REST API instead, and --output-mode ci keeps login and link non-interactive.

Openlayer Tests and Evals

Openlayer markets 175+ out-of-the-box tests; its docs catalog documents 100+. The catalog spans LLM answers, multi-turn agent sessions and classic ML, while Braintrust meters its plans on eval scores and LangSmith on LLM traces.

• LLM judges and custom metrics: LLM-as-a-judge tests score qualitative behavior; custom metrics cover app-specific scoring, though custom tests need Enterprise.
• Answer quality: hallucination, faithfulness and groundedness, plus answer relevancy and correctness.
• RAG retrieval: context recall, relevancy and utilization, which separate a bad retrieval from a bad answer.
• Safety and security: harmfulness, toxicity, bias, PII, prompt injection and unauthorized tool calls, plus a “recommends competitor” check for brand rules.
• Agent sessions: 15+ session-level tests, including goal achievement, role adherence, turn relevancy and task progression.
• Cost and performance: latency, cost and token tests turn runtime budgets into pass/fail thresholds.
• Classic ML and data: accuracy, F1, ROC AUC and RMSE for tabular predictions, drift against a reference dataset, and data-quality tests (nulls, duplicates, freshness, Great Expectations) on warehouse tables.

Bias and fairness tests shipped in October 2026, including group parity tests. They give you a measurement, not a legal finding, so you still define the groups and thresholds your policy requires. The EU AI Act alignment and OWASP security bundles work the same way: they package catalog tests, and the thresholds remain yours to set.

Confident AI’s DeepEval metrics are open source, while Openlayer’s platform is closed SaaS even though its SDKs are Apache-2.0. Openlayer does maintain jevals, an MIT-licensed project that runs agent evals and guardrail decisions locally, but it is not a self-hosted edition of the platform. If you want to read and modify the metric code itself, DeepEval is the better starting point; if you want one set of tests across CI and production, Openlayer is.

Best for: teams testing LLMs, agents and classic ML models under one set of thresholds. Skip if: you need an open-source eval platform you can run without an Enterprise contract.

Tracing, Guardrails and the Openlayer Gateway

Production Monitoring

Monitoring tests evaluate live traces on a set cadence, so they catch regressions after the fact; blocking a bad response at request time is the job of guardrails and the Gateway. Data reaches Openlayer three ways:

• SaaS: traces sent to Openlayer’s hosted platform.
• On-prem: the platform runs inside your infrastructure (Enterprise).
• Read in place: Openlayer queries BigQuery, Databricks or Snowflake directly.

Retention is set per project: Indefinite, 7, 30, 90, 180 or 365 days, with nightly cleanup of older data, while Basic stops at 3 months. The 2026 releases added a trace timeline, AI summaries, semantic search filters and customizable dashboards, which is the investigation tooling LangSmith users will compare first. Failed monitoring tests alert through the same in-app, email, Slack and webhook channels as development tests, and tabular models can publish predictions with a reference dataset for drift tests.

Guardrails

The openlayer-guardrails Python library ships four built-in checks:

• PIIGuardrail: personally identifiable information.
• PromptInjectionGuardrail: prompt injection attempts.
• ToxicityENGuardrail and ToxicityPTGuardrail: English and Portuguese toxicity.

Custom checks subclass BaseGuardrail. Guardrails attach to @trace() or run standalone through check_input, and they add latency without replacing tests. Install extras such as openlayer-guardrails[pii] per check. Fiddler markets sub-80 ms guardrails; Openlayer quotes no latency figure, so benchmark with your checks enabled before a latency-sensitive launch.

Gateway

The Gateway, launched in spring 2026, puts one endpoint between your apps and model providers and publishes every request as a trace. It translates between OpenAI and Anthropic formats and blocks requests with three HTTP responses:

Policy	Blocking response
Model access denied	Forbidden
Usage limit reached	429
Guardrail violation	422

Coverage is narrow: OpenAI Responses and Embeddings, plus Anthropic Messages. Providers are OpenAI, Anthropic, Azure OpenAI/Foundry or any OpenAI- or Anthropic-compatible API, streaming works only on text-generation endpoints, and heavy load returns 503 with a Retry-After header. Teams already running Portkey or LiteLLM can keep them, since both are documented Openlayer integrations; the Gateway is worth adopting when you need central model permissions and budgets tied to the same traces. It also supports shadow AI discovery across the apps routed through it.

Governance, Security and Deployment

Openlayer ships built-in EU AI Act, NIST AI RMF and ISO 42001 frameworks, plus custom frameworks for internal policy, and attaches evidence to each rule result. The October 2026 release added project lifecycle stages with named reviewers, which turns a model launch into an approval trail risk teams can sign off on. Framework mapping is evidence, not certification, and compliance reports are Enterprise-only, so Basic can show the frameworks but not export the report a risk committee would file.

Vendor AI projects register third-party tools such as Microsoft 365 Copilot without code access; Copilot can send telemetry later, while opaque tools stay registration-only. That inventory is shallower than the testing an instrumented app gets.

• Compliance: SOC 2 Type II, GDPR and HIPAA are listed.
• Encryption: TLS 1.2+ in transit and AES-256 at rest.
• Identity: SAML and OIDC SSO, directory sync and MFA, with SSO and RBAC on Enterprise only.
• Residency and isolation: US and EU regions, air-gapped deployment in your own VPC, and on-prem on Enterprise.
• Availability: 99.9% on the security page versus a 99.99% SLA on Enterprise, so get the contractual number in writing.

Integrations cover OpenAI, Anthropic, Bedrock, Gemini, LangChain, LangGraph, LiteLLM, the OpenAI Agents SDK, the Claude Agent SDK and OpenTelemetry, with SDKs in five languages. A remote MCP connector at mcp.openlayer.com/mcp gives coding assistants read-only or full workspace access; read-only is the safer default.

Fiddler sells into the same regulated buyers with SaaS, VPC and on-prem options. Openlayer is the stronger pick when framework evidence and lifecycle approvals matter as much as production monitoring; Fiddler is the stronger pick when guardrail latency is the deciding spec.

How Does Openlayer Compare to Competitors?

Openlayer wins on governance combined with testing, not on entry price. Logs, traces, spans and scores are different billing units, so compare plans against your own volume. Every rival in the table publishes a paid entry price between $39 and $249 a month; Openlayer is the only one that sends you straight from a free plan to a quote.

Tool	Free tier	Paid entry	Self-host	Best for
Openlayer	1 member; 20,000 logs/month	Enterprise quote	On-prem/air-gapped VPC on Enterprise	Regulated teams needing governance
Braintrust	1 GB; 10,000 scores; 14-day retention	Pro: $249/month	Enterprise	Eval-first development teams
LangSmith	1 seat; 5,000 base traces/month	Plus: $39/seat/month	Enterprise	LangChain/LangGraph applications
Arize AX/Phoenix	AX: 25,000 spans; 1 GB	AX Pro: $50/month	Phoenix open source; AX Enterprise	Budget or open-source tracing
Confident AI	2 seats; 5 test runs/week	Starter: $200/month	Enterprise	DeepEval users
Galileo/Splunk Agent Observability	Free edition	From $100/month per 1.2M spans, billed annually	Cloud or self-hosted	Splunk shops

• Braintrust: the pick for an eval-first dev team that wants several engineers collaborating before procurement, with unlimited projects. Starter allows unlimited users against Openlayer’s one, and Pro adds 5 GB of processed data, 50,000 scores and $100 in model credits, with metered overages.
• LangSmith: the pick for LangChain and LangGraph apps. Plus costs $39 per seat a month with 10,000 base traces, though base traces are kept only 14 days and usage beyond the included traces is pay-as-you-go.
• Arize AX/Phoenix: the budget pick. AX Pro costs $50 a month for 50,000 spans, 10 GB and 30-day retention, and Phoenix is open source for teams that want to self-host without an Enterprise contract. The catch is the small span cap on the paid entry tier.
• Confident AI: the pick if DeepEval already defines your evals. The free plan has 2 seats but only 5 test runs a week; Starter is a flat $200 a month per organization, and Team costs $2,000 a month.
• Galileo/Splunk Agent Observability: the pick for Splunk customers who want Luna small-model evaluators and runtime guardrails, from $100 a month per 1.2 million spans billed annually. Galileo’s old plan names no longer apply.
• Fiddler: the pick for fast runtime guardrails in regulated deployments, with free guardrails, a Developer plan at $0.002 per trace, and SaaS, VPC or on-prem Enterprise.

A small team gets a published paid plan from Braintrust or LangSmith today. A regulated platform team consolidating tests, tracing and governance should keep Openlayer on the shortlist and negotiate deployment, retention and support in one contract.

How I Reviewed Openlayer

I set up a Basic workspace with Python SDK 0.36.0 and followed Openlayer’s project-to-release path: region choice, project creation, @trace() instrumentation, catalog tests and a GitHub Actions gate. I then compared plan limits and entry prices against Braintrust, LangSmith, Arize, Confident AI and Galileo/Splunk Agent Observability, and weighed governance and deployment for a regulated buyer.

My criteria:

• Pricing and limits: seats, test caps, retention and the jump to Enterprise.
• Test coverage: LLM quality, agent sessions, security and classic ML.
• Production monitoring and guardrails: live-trace tests, alerts, guardrails and Gateway scope.
• Governance and security: frameworks, identity controls, residency and private deployment.
• Developer experience: SDK languages, instrumentation, repo integration and CI behavior.

Enterprise-only features (SSO, RBAC, on-prem, compliance reports) are scored on Openlayer’s published plan terms, since Basic doesn’t expose them.

Prices current as of October 2026.

Should You Use Openlayer for AI Testing and Governance?

Yes, if you run an AI platform team at a regulated company and want development tests, production tracing and governance evidence in one product. For a small team that wants a cheap paid upgrade, no.

Recommended if:

• You need framework evidence: EU AI Act, NIST AI RMF or ISO 42001 mapping tied to test results and lifecycle reviewers.
• You want one set of tests for CI and production: a GitHub Actions gate plus the same tests on live traces.
• Deployment is restricted: EU data residency, air-gapped VPC or on-prem is a procurement requirement.

Not recommended if:

• You want a published paid tier: LangSmith Plus ($39 per seat) and Braintrust Pro ($249 a month) skip the sales call.
• You want budget self-hosting: Arize Phoenix is open source; Openlayer’s platform is not.
• You need a multi-seat trial: Basic’s 1-member limit rules out a team evaluation without sales.

Before the sales call, use Basic to check whether 20 tests per project cover a meaningful release gate. Start on Openlayer’s Basic plan, wire one app with @trace() and a GitHub Actions gate, then ask sales to demo the governance features Basic can’t show.

FAQ

Is Openlayer free?

Yes. Openlayer Basic is free for 1 member, with 5 projects, 20,000 inference logs per month, 20 tests per project and 3 months of retention. It includes tracing, the test library and CI/CD. Collaboration, SSO, custom tests and compliance reports require Enterprise.

How much does Openlayer Enterprise cost?

Openlayer doesn’t publish Enterprise pricing. It is a custom quote based on scale, deployment and support, and there is no paid self-serve tier between Basic and Enterprise. Ask for volume, retention and deployment terms in writing, then compare against Braintrust Pro at $249 a month or LangSmith Plus at $39 per seat.

Can I self-host Openlayer?

Yes, on Enterprise: on-prem deployment and air-gapped deployment in your own VPC are both offered. The platform itself is not open source, although its SDKs are Apache-2.0. If you want to self-host without an Enterprise contract, Arize Phoenix is the open-source alternative.

Is Openlayer SOC 2 compliant?

Yes. Openlayer lists SOC 2 Type II, along with GDPR and HIPAA, and encrypts data with TLS 1.2+ in transit and AES-256 at rest. For a regulated purchase, request the SOC 2 report and confirm it covers your chosen region and deployment model.

What is Openlayer used for?

Openlayer is used for AI testing, evaluation, production monitoring and governance. It tests LLM answers, agent sessions and classic ML predictions in CI and on live traces, blocks risky requests through guardrails and the Gateway, and maps results to EU AI Act, NIST AI RMF and ISO 42001 frameworks.