Opik (Comet)

Editor’s note: This is an independent assessment of documented capabilities and pricing, not a hands-on app test.

Quick verdict: I recommend Opik for developers who want LLM observability and evaluation in the same workflow without a large starting subscription. Its strongest buying argument is the connection between production traces and repeatable tests, but self-hosting and evaluation quality still need engineering ownership.

My Opik review focuses on that connection. A trace can explain what an agent did; an evaluation can help decide whether a proposed change deserves to ship. Having both matters more to me than collecting another dashboard full of model calls.

Opik is developed by Comet, with managed cloud and open-source deployment options. It belongs in the AI data stack around your application, rather than replacing the application itself. I would shortlist it for a team building a support assistant or tool-using agent, especially when debugging has become a matter of piecing together logs.

Below, I weigh the subscription against the work left to your team, then examine the documented setup, evaluation workflow, and alternatives.

Key Takeaways

• Trace-to-test workflows are Opik’s strongest reason to make a shortlist.
• Low-cost cloud entry makes it approachable for smaller engineering teams.
• Prompt versioning and optimization extend its usefulness beyond incident investigation.
• Self-hosting needs operational ownership, and open-source deployment excludes user management.
• Scores are not guarantees: references need review, and online evaluation does not block responses.

Pros and Cons

I like Opik’s breadth, but the distinction between having evaluation tools and having trustworthy evaluations is central to my recommendation.

Opik Pricing: What Will You Actually Pay?

Opik has a free cloud tier and a paid plan starting at $19/month. I would budget for telemetry volume and evaluation calls before choosing a tier.

• Open Source: $0 software license, for teams operating their own infrastructure.
• Free Cloud: $0, for an initial instrumented workflow.
• Pro Cloud: $19/month flat, for teams needing more capacity.
• Enterprise: custom pricing, for organizational controls and negotiated requirements.

Plan	Monthly price	Included spans/month	Members	Retention
Open Source	$0 license	Self-managed	User management excluded	Self-managed
Free Cloud	$0	25,000	Up to 10	60 days
Pro Cloud	$19	100,000	Up to 50	60 days
Enterprise	Custom	Custom	Contract-specific	Contract-specific

Pro lists $5 per additional 100,000 spans. Extending retention from 60 to 400 days is listed at $29 per 100,000 spans; the public pricing does not fully explain the add-on’s billing mechanics.

I would settle that retention detail before committing to a long-lived archive. Keeping telemetry for debugging last week’s release is a different purchasing decision from preserving evidence across several product cycles.

A span is not necessarily a customer request. As a hypothetical example, if each application request generates five billable spans, the included allowances translate to 5,000 requests on Free or 20,000 on Pro, before other telemetry. That is arithmetic, not a measured capacity result.

An agent that retries or calls several tools could generate more spans per interaction than a single-call assistant. I would budget around a representative execution path, not just monthly active users. Cloud API and ingestion limits also exist separately from the monthly allowance.

Is Opik Good Value for Money?

Yes, for a team that will use evaluation alongside tracing. The subscription buys access to a workflow, but your engineers still need to decide what a good answer means and maintain examples that represent it.

Model-provider spending is another budget line. LLM judges and optimizer trials can generate additional calls, so the platform fee is not the entire evaluation bill. A cheap subscription does not make an unnecessarily broad judging policy cheap.

• Start with Free when instrumenting one application workflow.
• Choose Pro for capacity once the workload justifies it.
• Discuss Enterprise for governance, rather than expecting a usage upgrade to solve access requirements.

Enterprise lists SSO, service accounts, view-only users, dedicated support, and SLAs. If those are prerequisites, I would resolve them before integrating deeply.

Also, Opik and Comet MLOps are separate product families. Do not apply MLOps per-user pricing to an Opik budget.

Reviewer’s Notes

For a small engineering team without special governance requirements, I recommend starting on Free and paying for more capacity once the trace-to-test routine is useful.

Getting Started With Opik

I would start with one failure-prone workflow, not instrument an entire application at once. That makes the first decision manageable: can the captured context explain a problem well enough to create a useful regression case?

The documented Python quickstart installs opik, runs opik configure, and uses @track around application functions. Cloud setup requires an account; a self-hosted endpoint is the alternative. TypeScript SDK and OpenTelemetry routes also exist, so Python is not the only entry point.

1. Choose the destination: Configure the deployment and project that should receive telemetry.
2. Instrument the workflow: Track the outer operation and the relevant nested functions or supported clients.
3. Review the captured context: Examine the input and output alongside timing and metadata.

A trace represents the overall operation, while nested spans describe steps inside it. For a hypothetical support assistant, the outer trace could cover answering a question, with separate spans for retrieval and model generation. That distinction makes it possible to ask whether the delay came from searching for context or composing the answer.

I would choose a workflow where someone already knows what failure looks like. A support lead who can explain why a refund answer is wrong gives an engineer a much stronger starting point than a dashboard of unexplained low scores.

Project organization matters, too. Opik supports environment labels and filters, so staging traffic can be distinguished from production. Short-lived scripts also need attention to buffered trace flushing; finishing an application process is not, by itself, proof that every buffered event has been sent.

The next step is the one I consider most valuable: preserve a troublesome trace as a dataset case. Opik supports that transition, but saving a failure does not fix its reference answer. When a trace is added, its output becomes expected_output, so a failed response needs correction before it can represent the desired result.

Imagine an assistant inventing a refund deadline. The useful regression case contains the original question and relevant policy context, paired with an approved answer or clearly defined expected behavior. Leaving the invented deadline as the reference could reward precisely the mistake you intended to eliminate.

From there, an experiment can compare the existing application with a candidate change. I would start with one controlled change, such as a revised instruction, rather than changing retrieval and model selection simultaneously. That makes the comparison easier to interpret.

This is an engineering-led onboarding path, not a no-code promise. The value comes from connecting application behavior to a decision about what to change.

Tracing Agents and Finding Expensive Steps

Opik’s tracing is most useful when a single answer hides several operations. A final response alone cannot explain the path that produced it.

Nested instrumentation can expose the relationship between an outer application call and its internal steps. For an agent, that creates a basis for investigating repeated tool calls or an unnecessary generation step. Thread-level feedback also gives conversation-oriented applications a way to attach judgments beyond an isolated call.

I would use tracing to separate two questions: why was this interaction expensive, and was the expense justified? A longer operation might be appropriate for a complex request. Conversely, a fast response can still be completely wrong.

Opik’s cost tracking provides estimated USD values at span, trace, and project level. Automatic calculation depends on supported provider, model, and usage information; unsupported models can return None, and custom span costs can be supplied.

That makes the cost view useful for locating expensive steps, not reconciling invoices. If one branch repeatedly invokes a costly model, it becomes a candidate for investigation. Whether a cheaper alternative is acceptable still belongs in an evaluation, not in the cost chart.

Instrumentation coverage is the catch. If a tool call is missing from the trace, the visible path is incomplete. I would be especially careful with custom wrappers and mixed integration stacks rather than assuming every integration exposes identical detail.

Large payloads deserve separate attention. Opik documents size limits and truncation behavior in recent SDKs, with attachments as the route for large content. For a multimodal application, that affects whether the retained trace contains enough evidence to understand a failure.

I would also shortlist LangSmith for teams already invested in LangChain, while recognizing that LangSmith supports framework-agnostic instrumentation too.

Evaluation: Turn Failures Into Repeatable Tests

Evaluation is my main reason to choose Opik over a tracing-only setup. A trace explains one event; a well-designed regression set helps judge whether a fix survives beyond that event.

Opik’s offline evaluation combines an application task with a dataset and selected metrics, producing an experiment. Its datasets are project-scoped, and versioning provides immutable snapshots. That gives comparisons a defined set of examples instead of a moving collection whose contents may have changed between runs.

I would separate the work into different kinds of checks:

• Deterministic checks: Use custom logic for requirements with explicit pass conditions.
• LLM-judge checks: Assess qualities that require interpretation, with a suitable rubric.
• Human review: Resolve ambiguous or domain-sensitive judgments.

For example, an agent may select the correct tool but provide a poor final explanation. Opik documents agent evaluation using expected tool usage and a custom selection metric. I like that approach because it avoids compressing distinct failures into one vague “quality” score.

For retrieval-augmented generation, the Hallucination metric can consider input and output with optional context, using a configurable judge model. That is useful for assessing whether an answer is supported by supplied material. It is not proof that every unsupported statement will be detected.

My main caution is metric quality. A rubric that rewards confident, detailed answers could favor a fluent mistake over a short, correct refusal. I would calibrate judge results against human decisions on difficult examples before relying heavily on an aggregate score.

Annotation queues support that human layer by collecting traces or threads with review instructions and feedback definitions. Subject matter experts can review them, and feedback-score conditions can populate queues automatically. I see the practical benefit in directing limited expert attention toward questionable cases rather than asking people to inspect every interaction.

Dataset composition remains your responsibility. If most examples are straightforward questions, a better average score may tell you little about contradictory policy documents or missing context. My preference is to compare item-level failures as well as the summary, especially when a candidate changes refusal behavior.

Online evaluation extends scoring to logged production data with configurable sampling and judge settings. It does not block responses from the application. New rules apply to subsequently logged data by default, while historical traces or threads can be selected for evaluation.

That makes online scoring a monitoring and triage mechanism, not a substitute for synchronous application controls. I would use it to identify patterns worth investigating, then bring those patterns back into the regression set.

Braintrust is also worth comparing if evaluation is your primary buying reason. I would make that decision around the dataset-to-experiment workflow and billing model, not the number of named metrics.

Prompt Management and Optimization

Prompt management is less eye-catching than automated optimization, but version control is the feature I would establish first. A quality comparison is hard to trust when the instruction text can change without a clear reference.

Opik creates a new immutable sequential version when a prompt template changes. It supports side-by-side comparison, and SDKs can retrieve a specified version rather than always taking the latest. Identical templates do not create another version.

I like that foundation for teams where prompt changes move faster than application releases. An engineer can evaluate a particular instruction version instead of relying on an informal understanding of which text was active. It also helps separate a prompt change from a model change when investigating a regression.

The more ambitious option is Agent Optimizer. It uses the separate opik-optimizer package and requires Python 3.10 or later, with a dataset, metric, prompt, and model credentials. Trials and results are logged to Opik, and sample and trial controls let you bound the search.

The optimizer distinguishes the model executing the prompt from the model proposing improvements. In practical terms, it generates candidates and evaluates them against your chosen objective. Improvement is not assured, and an improved metric does not automatically mean an improved product.

Consider a hypothetical assistant scored mainly on brevity. An optimizer could favor a shorter answer that omits an important exception. The optimization process would be following the objective; the objective would be incomplete.

Opik’s optimizer documentation explicitly addresses overfitting and supports a separate validation dataset. I would use that separation, reserve unseen examples for an independent check, and set a model-call budget before starting a run.

My recommendation is to treat optimization as a later-stage experiment, not the onboarding attraction. First establish a baseline that the team understands. Once examples and scoring criteria are credible, automated candidate generation becomes more meaningful because someone can explain what a higher score actually buys.

Self-Hosting, Access Controls, and Data Handling

Self-hosting changes who owns the work. It can give a team control over the deployment, but it does not turn observability into a maintenance-free internal service.

Opik’s server, web app, and core observability and evaluation software are available under Apache 2.0. The open-source deployment includes tracing and evaluation, but explicitly excludes user management. I would not assume it includes the cloud edition’s administration features.

The local Docker Compose setup is not intended for production. Comet recommends Kubernetes deployment through its Helm chart for that purpose, with operational considerations including ingress and TLS, storage, and database backups.

For a platform team already running Kubernetes, that may be a reasonable addition to an existing operating model. For two application developers who mainly want to diagnose their assistant, it could be a poor trade. Avoiding a subscription is not compelling if it creates a service nobody has time to maintain.

I would make the ownership question concrete: who handles a failed upgrade, and who restores the data after a storage problem? If there is no clear answer, managed hosting is probably the better starting point, even when the software license is free.

Cloud region choice also matters. Free and Pro use the US; Enterprise offers custom regions. Langfuse is the alternative I would examine first if a self-serve EU or Japan cloud region is a hard requirement.

Data handling needs its own decision before instrumentation expands. A trace can contain the material your application processes, so I would minimize unnecessary sensitive content rather than assuming self-hosting alone makes collection appropriate. Shorter retention cannot undo sending information to the wrong destination.

Opik also documents export and import commands covering project traces and spans, alongside development artifacts such as datasets and experiments. That is useful for extracting work, but I would not mistake export availability for guaranteed lossless migration to another platform.

I would self-host Opik when deployment control fits the team’s architecture and staffing. Governance requirements should drive the choice as much as model-call volume.

How Does Opik Compare With Alternatives?

I would compare these tools using the same hypothetical application workload, not their headline allowances. Their billing units differ, so larger included numbers do not automatically represent better value.

• Langfuse: For self-serve region flexibility. Its cloud options include the US, EU, and Japan, and Core offers longer data access than Opik’s base cloud retention. In an Opik vs Langfuse decision, I would prioritize location and retention before price. Langfuse also supports self-hosting, although some add-ons require a license.
• LangSmith: For LangChain teams and broader SDK coverage. Its documented SDK coverage includes Go and Java as well as Python and TypeScript. It is not restricted to LangChain, but per-seat Plus pricing changes the team-cost calculation, and self-hosted or hybrid deployment sits under Enterprise.
• Braintrust: For an evaluation-first purchase. Its allowances explicitly meter processed data and scores, which may fit how an evaluation-heavy team budgets work. I would examine the Starter overages and retention carefully rather than dismissing it because its paid entry tier is higher.

Alternative	Free allowance	Paid entry	Main comparison caveat
Langfuse	50,000 units/month; 30-day access; 2 users	Core: $29/month; 100,000 units; 90-day access	Units are not equivalent to Opik spans
LangSmith	5,000 base traces/month; 1 seat	Plus: $39/seat/month; 10,000 base traces, then usage charges	Traces and seats change the calculation
Braintrust	1 GB processed/month; 10,000 scores; 14-day retention	Pro: $249/month; 5 GB; 50,000 scores; 30-day retention	Data, score overages, and model usage affect total cost

I would keep Opik on the shortlist when its tracing-to-evaluation connection fits the application and its deployment options fit the organization. None of these plan tables establishes which interface your engineers will prefer or which integration captures your workflow most completely.

How I Reviewed Opik

I assessed Opik’s documented capabilities, deployment requirements, and plan specifications against common engineering buying decisions. The workflow discussion follows its documented tracing, dataset, evaluation, and prompt-management behavior; the recommendations are my judgments about those capabilities.

I did not create an account, run an application, or measure overhead, usability, or answer quality. This review therefore assesses product fit rather than reporting hands-on performance.

Prices are current as of October 2026.

Should You Use Opik?

I would shortlist Opik for a small engineering team that wants repeatable quality checks alongside debugging. Its appeal is not merely seeing model calls. It is being able to take a problematic interaction, establish the correct expectation, and compare a proposed fix against other cases.

That recommendation is strongest when someone owns the evaluation set. Without that ownership, the team may accumulate traces and scores without gaining a dependable basis for release decisions. The software can organize evidence; it cannot decide your product’s standards.

I would be more cautious if your immediate requirement is centrally managed access or a specific self-serve cloud region. Resolve those constraints first. Likewise, I would skip self-hosting unless the team can maintain the service alongside application work.

My recommended starting point is Free Cloud for one meaningful workflow, where the region and governance fit. Build a small, reviewed regression set around the failures that matter, then decide whether more telemetry capacity is worth paying for.

Opik FAQ

Is Opik free?

Yes. Opik offers a free cloud plan and open-source software that you can self-host. The cloud option has usage and retention limits, while self-hosting leaves infrastructure and maintenance costs with your team.

I would also budget for model calls used by evaluation or optimization. Free platform access does not make those calls free.

What is the difference between Opik and Comet?

Comet develops Opik, which focuses on LLM application observability and evaluation, with prompt-management and optimization capabilities. Comet MLOps is a separate product family for machine learning work.

If your main question is why an assistant produced a particular answer, Opik is the relevant starting point. Keep the two products’ plans separate when budgeting.

Do I need LangChain to use Opik?

No. Opik provides Python and TypeScript SDKs, a REST API, and OpenTelemetry support, alongside framework integrations.

I would choose the route that fits your application and required telemetry. Integration availability does not mean every route provides identical coverage or behavior.

Does Opik online evaluation block bad responses?

No. Online evaluation scores logged data rather than acting as a blocking check before the response reaches the user.

Use it for monitoring and investigation. If your application must stop a response before delivery, that requires a separate synchronous control in the response path.