Comet

Comet brings together Opik for LLM application observability and evaluation and MLOps for tracking and managing machine-learning work. It suits teams building LLM-powered applications as well as teams training and operating models, though the two workflows have different plans and usage limits. Its free tiers offer useful starting points; growing teams should weigh the monthly quotas and custom-priced enterprise options.

Overview

Comet’s two product families share an underlying platform but address distinct jobs. Opik follows how generative AI applications and agents run, while MLOps covers the lifecycle around machine-learning experiments, datasets, models, and production monitoring. That breadth is useful for organizations working across both areas, but buyers should choose based on their workflow rather than assume every plan covers both products.

The company launched in 2017 and is headquartered in New York City, with offices in Tel Aviv. Its platform supports API and SDK access, and deployment options include hybrid setups.

Key features

Opik observability and evaluation

Opik traces application execution, including retrieved context, model responses, and tool calls. For teams debugging agent behavior, that view can help connect an outcome to the steps that produced it. Test suites, datasets, and experiments provide a way to evaluate changes, while built-in LLM-as-a-judge and heuristic metrics support repeatable checks. These capabilities make Opik a more focused fit for applications and agents that make LLM calls than for general model-training operations.

Opik names more than 40 integrations across AI frameworks, model providers, and gateways, including LangChain, OpenAI, Google ADK, LangGraph, and CrewAI. OpenTelemetry support extends tracking to AI applications written in Ruby, Java, and other languages, which matters for teams whose applications span ecosystems beyond the named integrations.

MLOps tracking and governance

Comet MLOps combines experiment tracking with dataset management, a model registry, and production monitoring. Run comparison, artifact tracking, and dataset versioning support work across model development, while a registry gives teams a place to manage models. The breadth is a strength for teams building and training models; it is more than an LLM application team needs if its priority is only tracing and evaluating agent calls.

The Trust Center lists ISO 9001:2015, ISO/IEC 27001:2022, and SOC 2 Type 2 compliance. Enterprise access includes single sign-on, service accounts, and view-only users. The Opik open-source version can be self-hosted, and Enterprise has flexible deployment options.

Pricing

Comet is freemium, with paid plans starting at $19/user/mo. Opik and MLOps have separate tiers, so compare the relevant product’s limits before choosing.

• Open Source: free, billed Free. It includes the full AI observability and agent testing feature set, agent tracing and analysis, test suites and assertions, and an agent playground. Self-hosting makes this the option for teams that want control over deployment; it does not come with the cloud plan’s stated member and span quotas.
• Free Cloud: free, billed Free plan. It allows up to 10 team members, 25k spans per month, and 60-day data retention. This is a practical starting point for a small team, but the span ceiling and retention period may constrain sustained or high-volume tracing.
• Pro Cloud: $19 a month, billed per month. It raises capacity to up to 50 team members and 100k spans per month, while retaining 60-day data retention. It suits teams that have outgrown Free Cloud’s member or span limits; the monthly span cap remains a consideration.
• MLOps Free: free, billed Free plan. It includes one platform user, fair usage, and 100GB data usage. It is a no-cost individual starting point, but the single-user allowance is not a team tier.
• MLOps Pro: $19 a month, billed per user/month. It supports up to 10 users, includes 1,500 training hours, and allows 500GB data usage. Teams gain room to collaborate and run training, but should account for per-user billing and the included-hour and data limits.
• Enterprise: custom pricing, with unlimited team members, custom usage plans, flexible deployments, dedicated support, and SLAs. Enterprise also lists single sign-on, service accounts, and view-only users.
• MLOps Enterprise: custom pricing, with unlimited users, unlimited training hours, flexible deployments, dedicated support, and SLAs.

Community support applies to all plans. Paid cloud Pro and Enterprise add email support, while Enterprise includes dedicated support and SLAs.

Platforms

Comet supports API, Linux, macOS, self-hosted, web, and Windows. Opik’s OpenTelemetry support is also relevant to teams instrumenting applications written in Ruby, Java, and other languages.

Who it's for

Opik is aimed at teams building applications or agents that make LLM calls, particularly those that need execution traces and structured evaluation. MLOps is designed for teams building and training machine-learning models, with tracking, dataset management, registry, and production monitoring in one product family. Comet is a stronger fit when those workflows justify its product-specific quotas and pricing; it is less compelling for a single individual who only needs basic experiment logging.

Pros and cons

• Pro: Opik combines tracing with test suites, datasets, experiments, and evaluation metrics, supporting both investigation and repeatable assessment of LLM applications.
• Pro: MLOps covers experiment tracking, dataset versioning, model registry, and production monitoring, giving model-building teams a broader lifecycle toolkit.
• Pro: Opik offers both a self-hostable open-source version and cloud tiers, while Enterprise adds flexible deployment options.
• Con: Free Cloud caps teams at 10 members and 25k monthly spans, with 60-day retention; teams with greater activity need to plan for the Pro tier or a different deployment.
• Con: MLOps Free is limited to one user, and MLOps Pro charges per user/month while imposing training-hour and data caps.
• Con: Enterprise pricing is custom, so teams needing unlimited usage or dedicated support cannot budget from a fixed published price.

Alternatives

Comet is broad, but a narrower tool may suit a specific workflow or budget better. For a direct comparison across experiment tracking products, browse ML Experiment Tracking Software; for registry-focused choices, see Model Registry Software.

• Weights & Biases is another freemium option with API, mobile, desktop, web, and self-hosted platform support. Its free plan specifies five model seats, 5 GB/mo storage, and 1 GB/mo Weave data ingestion, useful to compare if those allowances fit your needs.
• ClearML is a freemium alternative with a self-hosted version it describes as 100% open source on GitHub.
• TensorBoard is a free alternative with no stated price or usage limits.
• ZenML offers an open-source tier with unlimited executions and projects plus self-hosting; its Scale plan is $999 a month.
• Trackio is free, with its library and Hugging Face hosting offered at no cost.
• GitLab Package Registry is a freemium alternative with a free tier capped at five users per top-level group, 400 compute minutes/month, and 10 GiB adjustable storage.
• Hopsworks AI Lakehouse offers a free tier with one project, Feature Store and Model Registry, and community support.

Verdict

Comet is a strong choice for teams that need either LLM application observability and evaluation through Opik or a fuller MLOps workflow, and especially for organizations working across both. Its open-source and free cloud entry points lower the barrier to starting, while its product-specific quotas make growth costs and fit important to review. Look elsewhere if you need only a narrowly scoped free tool, or if fixed enterprise pricing is essential before committing.