Comet

Overview

Comet combines Opik, for observing and evaluating generative AI applications and agents, with MLOps, for experiment tracking and the wider machine-learning lifecycle. That breadth can suit organizations spanning both disciplines; it is less compelling if you expect one subscription to cover both workflows, since each family has its own plans and quotas.

Founded in 2017, Comet is headquartered in New York City and has offices in Tel Aviv. API and SDK access and hybrid deployment options are relevant to teams integrating the platform into an existing stack.

Key features

Opik observability and evaluation

Opik traces execution paths through context retrieval, model responses, and tool calls. For teams debugging agent behavior, this makes individual steps visible rather than leaving a run as a single opaque result. Test suites, datasets, and experiments support repeatable evaluation; built-in LLM-as-a-judge and heuristic metrics provide ways to assess outputs. The combination is useful for teams iterating on LLM applications, though the Free Cloud quota of 25k spans per month will matter as instrumentation grows.

Opik integrates with more than 40 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 helps teams avoid tying instrumentation solely to a listed framework integration.

MLOps lifecycle

Comet MLOps combines experiment tracking, dataset management, a model registry, and production monitoring. Dataset versioning, artifact tracking, and run comparison support a workflow from model development through operational oversight. This is the more relevant family for teams building and training machine-learning models; it is not a substitute for Opik’s LLM application tracing and evaluation.

Deployment and access

The Opik open-source version can be self-hosted, while Enterprise offers flexible deployment options. 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, a useful set of controls for larger teams. Comet also lists API and SDK access and hybrid deployment options.

Pricing

Comet uses a freemium model, with paid plans from $19/user/mo. The plans are split between Opik and MLOps, so compare limits within the product family you intend to use rather than treating the entry price as a universal platform tier.

• Open Source: 0.00 USD per 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 clearest route for teams prioritizing control of deployment; it does not include cloud usage terms.
• Free Cloud: 0.00 USD per free, billed Free plan. It allows up to 10 team members, 25k spans per month, and 60-day data retention. That is a meaningful starting point for a small Opik team, but the span cap and retention window constrain heavier or longer-running evaluation work.
• Pro Cloud: 19.00 USD per month, billed Per month. It raises the ceiling to 50 team members and 100k spans per month while retaining 60-day data retention. It fits a growing Opik team that needs more throughput; the fixed retention period remains the same as Free Cloud.
• MLOps Free: 0.00 USD per free, billed Free plan. It covers 1 platform user under a fair usage policy, with 100GB data usage. The single-user allowance makes it an individual starting point, not a broadly shared team tier.
• MLOps Pro: 19.00 USD per month, billed Per user/month. It supports up to 10 users, includes 1,500 training hours, and allows 500GB data usage. Teams should account for per-user billing and the included training-hour allowance when estimating expansion.
• Enterprise: custom pricing, billed Custom. It offers unlimited team members, custom usage plans, flexible deployments, dedicated support, and SLAs.
• MLOps Enterprise: custom pricing, billed Custom. It provides unlimited users, unlimited training hours, flexible deployments, dedicated support, and SLAs.

Community support is available across plans; paid cloud Pro and Enterprise include email support, while Enterprise adds dedicated support and SLAs.

Platforms

Comet supports API, Linux, macOS, self-hosted, web, and Windows. This range suits teams mixing browser-based access with API-driven workflows or self-managed deployment, although the self-hosting option is specifically stated for Opik open source.

Who it's for

Choose Comet when your team needs structured evaluation and tracing for LLM applications, or experiment and model management for conventional ML work. It is particularly relevant when both disciplines exist in the same organization, provided you are comfortable selecting and budgeting for separate product-family plans. Teams seeking only a compact model registry or a narrowly scoped AI gateway may find a more focused alternative a better match.

Pros and cons

• Pros: Opik joins execution tracing with test suites, datasets, experiments, and evaluation metrics, supporting a more repeatable way to investigate agent behavior.
• Pros: More than 40 integrations and OpenTelemetry support span common frameworks and other languages, giving teams multiple routes to instrument applications.
• Pros: Opik open source can be self-hosted, while Enterprise adds flexible deployment, access controls, dedicated support, and SLAs for organizations with stricter operational needs.
• Cons: Separate Opik and MLOps plans make it necessary to evaluate two sets of limits and costs when a team needs both workflows.
• Cons: Free Cloud caps usage at 25k spans per month and 60-day retention; MLOps Free is limited to one user under fair usage, so neither is an unlimited shared production tier.
• Cons: MLOps Pro is billed per user/month and includes a finite 1,500 training hours, which teams must factor into growth and training workloads.

Alternatives

For a narrower model-registry search, compare the Model registries category; for experiment tracking options, use ML Experiment Tracking Software.

• GitLab Package Registry is worth considering when a freemium registry with a five-user free group allowance, 400 compute minutes per month, and adjustable 10 GiB storage better matches the need.
• Weights & Biases is an alternative with a free tier covering five model seats, 5 GB/month storage, and 1 GB/month Weave data ingestion.
• Amazon SageMaker Autopilot is a paid, pay-as-you-go alternative with no minimum fees or upfront commitments, suited to buyers who prefer usage-based costs.
• ClearML is an alternative if a 100% open-source self-hosted version is the priority.
• DagsHub is another freemium option for teams whose needs align with its Individual plan.
• Hopsworks AI Lakehouse offers a free tier with one project, a Feature Store, and a Model Registry, making it relevant when those components are the focus.
• MLflow Model Registry is a free, open-source Apache 2.0 option for teams seeking a model registry rather than Comet’s broader paired product families.
• TrueFoundry AI Gateway is an alternative to consider when the primary requirement is an AI gateway.

Verdict

Comet is a strong fit for teams that need either LLM application observability and evaluation or a fuller MLOps workflow, especially organizations working across both. Its strongest reason to choose it is the range from Opik tracing and evaluation to MLOps tracking on one underlying platform. Look elsewhere if you need one simple shared plan for both product families or your usage is likely to outgrow the free quotas before a paid tier fits your budget.