Comet

Comet is a platform for observing and evaluating LLM applications and managing machine-learning workflows. It suits teams building agents or training models, especially when both jobs matter; the trade-off is that Opik and MLOps have separate plans and quotas.

Overview

Comet brings two distinct product families onto one underlying platform. Opik focuses on generative AI applications and agentic systems; MLOps covers experiment tracking, datasets, model management, and production monitoring. That range is useful for organizations working across both disciplines, but it does not make the products interchangeable: choose the plan for the workflow you need.

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 tooling into their own environments.

Key features

Opik observability and evaluation

Opik traces execution paths through context retrieval, model responses, and tool calls. For teams diagnosing agent behavior, connecting these steps can make it easier to see where a run went wrong. Test suites, datasets, and experiments support structured evaluation, while built-in LLM-as-a-judge and heuristic metrics give teams ways to assess changes. 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 applications written in Ruby, Java, and other languages.

MLOps workflow

MLOps combines experiment tracking with dataset management, a model registry, and production monitoring. That is a broader lifecycle toolkit than a tracker alone, making it a fit for teams building and training models as well as managing them after training. Run comparison, artifact tracking, dataset versioning, and model registry are supported.

Deployment and security

Opik's 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, useful for organizations with access-control requirements.

Pricing

Comet has separate Opik and MLOps tiers, so compare the relevant product's limits rather than treating the entry price as one bundle.

• 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 is the trade-off for teams that want this route.
• Free Cloud: free, billed Free plan. It allows up to 10 team members, 25k spans per month, and 60-day data retention. It is a practical starting point for smaller teams, but the span ceiling may constrain busier workloads.
• Pro Cloud: $19 a month, billed Per month. The allowance rises to 50 team members and 100k spans per month, with the same 60-day retention. It suits teams that have outgrown Free Cloud's member or span caps.
• MLOps Free: free, billed Free plan. It covers 1 platform user, fair usage, and 100GB data usage, so it is best for an individual exploring the workflow.
• 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. The per-user billing matters for teams budgeting beyond one seat.
• Enterprise: custom pricing, billed Custom, with unlimited team members, custom usage plans, flexible deployments, dedicated support, and SLAs.
• MLOps Enterprise: custom pricing, billed Custom, 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; Enterprise also includes dedicated support and SLAs. A free trial is offered, but no trial length or renewal terms are stated.

Platforms

Comet supports API, Linux, macOS, web, and Windows, with self-hosted deployment also supported. This mix accommodates teams working across local environments and cloud services.

Who it's for

Opik is the stronger fit for teams whose applications or agents make LLM calls and need tracing plus repeatable evaluation. MLOps is aimed at teams building and training machine-learning models, particularly those needing dataset management, a registry, and production monitoring alongside experiment tracking. Teams that need only one of these workflows should focus on its product-specific limits and price rather than the platform's combined scope.

Pros and cons

• Pro: Opik combines traces, test suites, datasets, experiments, and evaluation metrics, giving agent teams a connected way to inspect and assess runs.
• Pro: More than 40 integrations and OpenTelemetry support cover a broad range of AI stacks and languages.
• Pro: MLOps brings tracking, dataset management, registry, and production monitoring together for model teams.
• Con: Free Cloud tops out at 10 team members and 25k spans per month, while Free MLOps is limited to one platform user and fair usage.
• Con: Pro Cloud's 100k monthly spans and 60-day retention may be limiting for teams with heavier or longer-running observability needs.
• Con: MLOps Pro is priced per user, and both Enterprise tiers use custom pricing, making larger deployments harder to budget from a fixed public rate.

Alternatives

Choose Weights & Biases instead if its free tier's 5 model seats, 5 GB/mo storage, and 1 GB/mo Weave data ingestion better fit your team's needs. ClearML is an alternative with a free Community plan and a self-hosted version it describes as 100% open source on GitHub. For a free option, MLRun offers an open-source plan with community support. TensorBoard is free. Trackio is also free, with its library and Hugging Face hosting included at no charge. ZenML offers an open-source tier with unlimited executions and projects and self-hosting. For a registry-focused alternative, GitLab Package Registry has a free plan for up to 5 users per top-level group, 400 compute minutes/month, and 10 GiB adjustable storage. Hopsworks AI Lakehouse has a free plan with one project, Feature Store, Model Registry, and community support. Readers comparing categories can also browse ML Experiment Tracking Software and Model Registry Software.

Verdict

Comet is a strong choice for teams that need either LLM observability and evaluation or a fuller MLOps workflow, with the added option to use both product families on one platform. Opik's tracing and evaluation depth is its clearest reason to choose it; teams should look elsewhere if its cloud quotas are too tight or if they need a fixed enterprise price before committing.