Comet Opik
Open-source LLM observability and evaluation for agentic AI, with real-time tracing, cost optimization, and auto-fix.
If your team is shipping agents and your LLM bill is climbing, Opik is one of the few open-source platforms that treats cost tracking and regression testing as core features rather than add-ons. Agent Diagnostics, Test Suites, and the Ollie auto-fix assistant give it real teeth beyond logging. Just don't buy it expecting enterprise governance or a no-code UI — that's still the gap, and LangSmith covers it better.
Verified 1d ago · liveness 81/100 · cite: rightaichoice.com/tools/comet-opik
- Developers evaluating LLM prompts with A/B testing and measurable metrics
- ML engineers monitoring LLM performance and token cost in production
- Teams building complex multi-step agentic workflows that need cross-trace debugging
- Open-source projects wanting transparent, self-hostable LLM testing
- Teams needing advanced content guardrails or safety filters
- Non-developers looking for a no-code observability solution
- Enterprises requiring multi-cloud experiment tracking beyond LLMs
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Skip Comet Opik if you need a fully managed, no-code LLM observability solution with advanced enterprise governance (SSO, audit logs) out of the box — those features are still catching up.
The free tier caps at 3 users and 1 GB storage; going beyond requires a paid Team plan at $49/user/mo (billed annually), which can add up for larger teams.
Opik's free tier is generous for small teams (up to 3 users, 1 GB storage), and the Team plan at $49/user/mo is competitive with LangSmith's pricing. For larger enterprises needing custom deployment, Opik is cheaper than LangSmith's Enterprise tier, but you'll trade off some turnkey governance.
In short
Comet Opik — Open-source LLM observability and evaluation for agentic AI, with real-time tracing, cost optimization, and auto-fix. Best for Developers evaluating LLM prompts with A/B testing and measurable metrics, ML engineers monitoring LLM performance and token cost in production, Teams building complex multi-step agentic workflows that need cross-trace debugging. Free to start; paid plans from $49/user/mo.
What's new in Comet Opik
Checked 17 days agoAcross the latest 4 updates: 2 feature updates and 2 news mentions.
I Built a RAG Pipeline for F1 Team Radio, Then Made It Grade Itself
Community tutorial on building a self-grading RAG pipeline for F1 radio, showcasing Opik's evaluation capabilities.
One Prompt, 24 Versions: How Digibee Builds Prompts with Opik
Digibee shares how they use Opik to manage prompt versions and power their AI-native integration platform.
Beyond the Single Trace: How We Built Agent Diagnostics for Opik
Opik introduces agent diagnostics for production AI agents, moving beyond single-trace analysis.
How We Optimized Opik's MCP Server for Cost & Performance
Comet engineers detail optimizations to Opik's MCP server to cut token spend and improve performance.
What people actually say about Comet Opik — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
11 mentions across 2 sources (Hacker News, YouTube) · researched Aug 23, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Real-time logging and tracing with native OpenTelemetry support
- +Open-source (Apache 2.0) transparency avoids vendor lock-in
- +Advanced cost tracking could cut token spend up to 30%
- +Agent Playground speeds up early-stage agent prototyping
- +Test Suites turn ad-hoc evals into repeatable regression tests
- −No-code support is still lagging behind LangSmith
- −Enterprise features (governance, compliance) are catching up
- −Limited real-world community feedback outside vendor YouTube
- −Lacks rich media support like URL preview images in traces
- −Documentation and eval startup definition can be confusing
- • Self-hosting requires infrastructure and maintenance effort
- • Higher-tier plans may have setup or onboarding fees
Viability Score
How well maintained and how widely used is Comet Opik? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Real-time LLM interaction logging and tracing
- Prompt A/B testing against evaluation metrics
- Evaluation metrics dashboards for LLM quality
- Multi-step agent tracing for production systems
- Agent Diagnostics for cross-trace production analysis
- Test Suites for automated dataset and metric evaluation
- Agent Playground for early-stage agent prototyping
- Ollie auto-fix assistant for patching agent codebases
- Advanced cost tracking with token-spend optimization
- Claude Code token spend reduction up to 30%
- Native OpenTelemetry support for existing pipelines
- Optimized MCP server for agent tool workflows
- Python SDK for instrumentation
- CI/CD integration for regression testing
- Open-source under Apache 2.0, self-hostable
About Comet Opik
Opik is an open-source framework for evaluating, testing, and monitoring LLM applications, aimed squarely at teams whose workloads have outgrown single-call debugging. It logs every LLM interaction in real time, traces multi-step chains, and runs prompt A/B tests against concrete evaluation metrics, so iteration cycles stay grounded in numbers instead of vibes. Apache 2.0 licensing means you can read the source, self-host it, and wire it into your own stack rather than renting a black box. The last few months pushed Opik well past simple logging. Agent Diagnostics (July 2026) looks beyond a single trace to surface what a production agent is actually doing across a run. Test Suites (June 2026) turn ad-hoc dataset and metric checks into repeatable regression tests you can run on a schedule. Ollie, the auto-fix assistant, patches agent codebases when something breaks, and the Agent Playground (April 2026) covers early-stage prototyping before a workflow hits production. Community write-ups show the patterns in the wild: Digibee manages 24 versions of one prompt, and a developer built a self-grading RAG pipeline for F1 team radio. Cost control is a first-class concern here. Opik's advanced cost tracking, documented in a June 2026 guide, can cut Claude Code token spend by up to 30% by surfacing waste and recommending fixes — and Comet engineers published their own MCP server optimizations in July 2026 to trim token usage further. That matters once agent loops start compounding your bill. Opik integrates with OpenAI, Anthropic, LangChain, Comet ML, and Oracle Open Agent, and supports OpenTelemetry natively, with an MCP server for tool-based workflows. A hosted cloud carries a free tier (up to 3 users, 1 GB storage) alongside Team and Enterprise plans. Against closed alternatives like LangSmith, the pitch is transparency and cost visibility — you trade some enterprise governance and no-code polish for a stack you can inspect and self-host.
Behind the Verdict
Reach for Opik when you've moved past prompt tinkering and need evidence. The moment you're tracing multi-step agents, managing competing prompt versions, or watching token spend compound across retries, the open-source model starts paying off — you can self-host, read the source, and wire it into an existing OpenTelemetry setup without asking permission. The July 2026 Agent Diagnostics release is the one to care about. Single-trace tools tell you what one call did; diagnostics tell you what the agent did across a run, which is the question that actually slows down production debugging. Pair that with Test Suites and you get regression coverage that catches a prompt change breaking a downstream step before your users do. Where it bites: governance, guardrails, and content safety are not the strong suit. Teams that need policy enforcement baked into the observability layer will end up bolting on a separate tool. Non-developers will find little to grab onto — this is a developer-first product through and through, and the hosted cloud's free tier caps at 3 users and 1 GB of storage, which is a prototype allowance, not a team one. Against LangSmith, the trade is straightforward. LangSmith offers a more managed, more governed experience with smoother onboarding for enterprises. Opik counters with Apache 2.0 transparency and a cost story that's harder to replicate — the Claude Code cost tracking alone has been shown to cut spend by up to 30%, and the MCP server optimizations show Comet is treating token efficiency as an engineering problem, not a marketing bullet. In practice, we'd pick Opik for engineering-led teams already comfortable running infrastructure, especially those in the Comet ML ecosystem who want LLM observability in the same neighborhood as their
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Real-world workflow fit
Concrete scenarios for the personas Comet Opik actually fits — and what changes day-one when you adopt it.
You're deploying a new agentic workflow and want to monitor performance and cost in production.
Outcome: With Opik, you can trace every LLM call, set up evaluation metrics, and get alerts on cost anomalies, helping you catch regressions early.
You need to A/B test different prompt versions to improve response quality.
Outcome: Use Opik's prompt A/B testing to compare outputs side-by-side against a golden dataset, then pick the winner with confidence.
You're building an AI product and want to iterate quickly without burning through your LLM budget.
Outcome: Opik's cost tracking helps you identify wasteful token usage, and the open-source nature lets you self-host to avoid per-seat fees.
Use Cases
- Trace and debug multi-step LLM chains in production
- A/B test different prompts and compare outputs side-by-side
- Create evaluation datasets to automatically score LLM responses
- Monitor latency and token usage across model versions
- Integrate LLM evaluations into CI/CD pipelines to prevent regressions
- Collaborate with team members on prompt improvement and versioning
- Rapidly prototype and test AI agents in the Agent Playground
- Run unit and regression tests on AI agents with Test Suites
Models Under the Hood
as of 2026-08-31
Limitations
- Opik is an open-source LLM evaluation and observability tool for agentic AI, providing real-time tracing and cost optimization.
- It integrates with Comet's platform for collaboration and offers self-hosted deployment options.
- The documentation mentions Python SDK and APIs, suggesting a technical user base.
- Enterprise features like advanced governance are still maturing, and no-code support is limited.
as of 2026-08-29
Verification history
We have re-verified Comet Opik 19 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
Showing the 6 most recent of 19 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Comet Opik tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Solo developers and small teams evaluating LLM observability with up to 3 users and 1 GB storage.
What this tier adds
Starting tier: core tracing and evaluation features with limited users and storage.
Team
$49/user/mo (billed annually)
Ideal for
Growing teams needing collaboration and advanced features beyond the free tier.
What this tier adds
Adds collaboration features and advanced capabilities for team workflows, billed annually at $49/user/mo.
Enterprise
Custom
Ideal for
Organizations requiring custom deployment options, such as on-premises or VPC.
What this tier adds
Custom pricing with custom deployment and likely additional governance features.
Where the pricing makes sense
The company stage and team size where Comet Opik's pricing actually pencils out — and where peers do it cheaper.
Opik's free tier is generous for small teams (up to 3 users, 1 GB storage), and the Team plan at $49/user/mo is competitive with LangSmith's pricing. For larger enterprises needing custom deployment, Opik is cheaper than LangSmith's Enterprise tier, but you'll trade off some turnkey governance.
Setup time & first value
How long it actually takes to get something useful out of Comet Opik — broken out by persona, not the marketing-page minute.
For a developer familiar with Python, you can get Opik running in minutes by installing the SDK and logging your first trace. Setting up evaluation datasets and CI/CD integration may take a few hours. For teams, the cloud version requires no infrastructure setup.
Switching to or from Comet Opik
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith: Export your traces and evaluation data, then use Opik's Python SDK to re-log them; the open-source format makes it straightforward.
- ↗To LangSmith: Export your traces from Opik and import them via LangSmith's API; expect to rebuild some custom dashboards.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Comet Opik”, and we withheld 6: 6 did not mention Comet Opik. We are showing none, because we could not prove any of them are about Comet Opik.
Official links
Tools that pair well with Comet Opik
Common stack mates teams adopt alongside Comet Opik, with the specific reason each pairing earns its keep.
OpenLIT
Open-source, OpenTelemetry-native LLM observability and AI engineering platform for teams.
Opik (Comet)
Open-source AI observability for agent tracing, LLM-as-a-judge evals, and coding agent cost tracking
Autoblocks AI
A platform for testing, deploying, and monitoring AI agents in regulated industries.
Alternatives to Comet Opik
View all
OpenLIT
Open-source, OpenTelemetry-native LLM observability and AI engineering platform for teams.
Opik (Comet)
Open-source AI observability for agent tracing, LLM-as-a-judge evals, and coding agent cost tracking
Autoblocks AI
A platform for testing, deploying, and monitoring AI agents in regulated industries.
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