Overview

Comet brings two product families onto one platform, but they serve distinct workflows. Opik focuses on generative AI applications and agents; MLOps supports the work of building, training, and operating machine-learning models. The breadth is useful for teams spanning both disciplines, though buyers should match the plan to the product they need rather than assume a single plan covers everything.

Founded in 2017, Comet is headquartered in New York City and has offices in Tel Aviv. API and SDK access and hybrid deployment options make it relevant to teams integrating model tooling into their environments.

Key features

Opik observability and evaluation

Opik traces application execution, including context retrieval, model responses, and tool calls. For teams debugging agent behavior, following these steps can help identify where a run diverged. Test suites, datasets, and experiments support structured evaluation, while built-in LLM-as-a-judge and heuristic metrics offer ways to assess changes. More than 40 integrations span AI frameworks, model providers, and gateways, including LangChain, OpenAI, Google ADK, LangGraph, and CrewAI. OpenTelemetry support extends tracking to applications written in Ruby, Java, and other languages.

The open-source Opik version can be self-hosted, and Enterprise offers flexible deployment options. That gives teams deployment choices, while the free cloud tier’s span and retention caps matter for sustained or high-volume use.

MLOps lifecycle tools

Comet MLOps combines experiment tracking, dataset management, a model registry, and production monitoring. That makes it a fit for teams that want to manage several stages of model work within one product family. The MLOps plans have their own user, training-hour, and data-usage terms, separate from Opik’s span limits.

Security and access

Comet’s Trust Center lists ISO 9001:2015, ISO/IEC 27001:2022, and SOC 2 Type 2 compliance. Enterprise adds single sign-on, service accounts, and view-only users, which are relevant for organizations with centralized access controls. Community support is available on all plans; paid cloud Pro and Enterprise include email support, while Enterprise adds dedicated support and SLAs.

Pricing

Comet uses a freemium model, with paid plans from $19/user/mo. The free options make it possible to start without a subscription, but each product family has separate limits and plan terms.

Open Source: 0.00 USD per free, billed Free. Includes the full AI observability and agent testing feature set, agent tracing and analysis, test suites and assertions, and an agent playground. Self-hosting suits teams that want to run Opik themselves.
Free Cloud: 0.00 USD per free, billed Free plan. Supports up to 10 team members, 25k spans per month, and 60-day data retention. It is a useful starting tier for smaller teams, but the monthly span cap constrains heavier tracing workloads.
MLOps Free: 0.00 USD per free, billed Free plan. Includes 1 platform user, fair usage, and 100GB data usage. Its single-user limit makes it better for an individual evaluation than a collaborative team workflow.
Pro Cloud: 19.00 USD per month, billed per month. Allows up to 50 team members and 100k spans per month, with 60-day data retention. The higher span ceiling suits larger or busier teams, but retention remains capped at 60 days.
MLOps Pro: 19.00 USD per month, billed per user/month. Includes up to 10 users, 1,500 training hours, and 500GB data usage. It adds room for team use and training compared with MLOps Free, but buyers should account for per-user billing.
Enterprise: custom pricing, with unlimited team members, custom usage plans, flexible deployments, dedicated support, and SLAs.
MLOps Enterprise: custom pricing, with unlimited users and training hours, flexible deployments, dedicated support, and SLAs.

A free trial is available. Enterprise is the better fit when fixed cloud quotas or standard plan limits do not suit a deployment, while its custom pricing means it is not a like-for-like low-cost step up.

Platforms

Comet supports API, Linux, macOS, self-hosted, web, and Windows. The combination of hosted and self-hosted options gives technical teams flexibility, while OpenTelemetry broadens Opik tracking across languages.

Who it's for

Choose Opik if your applications or agents make LLM calls and you need to trace runs and evaluate behavior. Choose MLOps if your team builds and trains machine-learning models and needs experiment tracking, dataset management, registry, and production monitoring. Comet is most compelling when both workflows matter to the same organization; teams needing only one should compare that product’s plan and limits rather than pay for breadth they will not use.

Pros and cons

Pro: Opik combines tracing with test suites, datasets, experiments, and evaluation metrics, supporting both debugging and repeatable assessment.
Pro: More than 40 integrations and OpenTelemetry support give teams multiple routes to instrument AI applications.
Pro: MLOps combines experiment tracking, dataset management, model registry, and production monitoring for teams managing model work beyond experimentation.
Con: Free Cloud tops out at 25k spans per month and 60-day retention, which can constrain teams with more extensive observability needs.
Con: MLOps Free is limited to one user, while MLOps Pro bills per user/month; collaborative use can therefore require a paid plan.
Con: Opik and MLOps have separate plans and quotas, so the platform’s breadth does not translate into a single shared subscription structure.

Alternatives

Weights & Biases is another freemium option with API, self-hosted, web, desktop, and iOS platforms; its free plan includes five model seats, 5 GB/month storage, and 1 GB/month Weave data ingestion. Consider it when those published free-tier allowances or platform options better match your needs.

GitLab Package Registry has a free tier for up to five users per top-level group, 400 compute minutes/month, and 10 GiB adjustable storage, with a Premium plan priced at 29.0. It is an alternative to consider for package-registry needs under those terms.

MLRun offers an open-source option with community support and a managed offering on Iguazio. It is worth considering if an open-source ML platform is your priority.

Amazon SageMaker Autopilot is a paid option with a pay-as-you-go plan and no minimum fees or upfront commitments. Consider it if that pricing approach suits your use.

DagsHub has a free Individual plan with unlimited public repositories and unlimited private repositories for non-commercial use. It may suit repository-centered work with those terms.

ClearML says its self-hosted version is 100% open source on GitHub. Consider it if self-hosted open-source software is the deciding factor.

Trackio is free, with its library and Hugging Face hosting provided at no charge. Its no-cost approach may fit users looking for a free option.

Hopsworks AI Lakehouse offers a free plan with one project, Feature Store, and Model Registry. Consider it if those are the capabilities you need from a free tier.

For focused comparisons, see ML Experiment Tracking Software and Model Registry Software.

Verdict

Comet is a strong choice for teams that need either LLM application observability and evaluation or a broader MLOps workflow, particularly when both belong in the same organization. Opik’s tracing and evaluation toolkit and MLOps’s lifecycle coverage are the core reasons to choose it. Look elsewhere if the free cloud caps are too restrictive, a single shared plan is essential, or custom-priced Enterprise is needed to meet your deployment and support requirements.

	Comet	Snowflake Feature Store
Free plan	Yes	No
Free trial	Yes	No
Paid from	$19/mo	—
Open source	No	No
Platforms	api, Linux, macOS, self-hosted, Web, Windows	api, Web
Free plan	Yes	—

Comet vs Snowflake Feature Store

Comet

Snowflake Feature Store