Comet review

Comet is developer infrastructure for building and operating AI applications—not the AI browser with the same name. Its Opik platform focuses on tracing, evaluating, debugging, and monitoring LLM applications and agents. Comet also offers MLOps tools for teams that need to track and compare model-training runs.

Opik traces can show activity such as context retrieval, tool selection, and user feedback. Teams can use test suites to check whether traces meet goals, or evaluate datasets with LLM-as-a-judge metrics. For diagnosis, Comet describes tools that group recurring issues and suggest root causes, plus Ollie, which reviews traces and recommends changes to tool calls, retrieval, or system prompts.

The product spans self-hosted open source and cloud plans. According to the pricing page, the free cloud plan includes up to 10 team members, 25,000 spans per month, and 60-day data retention. The MLOps platform is an optional add-on.

Comet covers much of the evaluation and observability workflow in one product: capture traces, inspect what an agent did, test behavior, then monitor performance and costs in production. That is useful when a team needs more than a prompt playground and wants a record of retrieval, tool calls, and feedback to investigate failures.

The evaluation options are also practical. Teams can write pass/fail test suites for specific goals or score datasets using LLM-as-a-judge metrics. The product description says Opik’s MCP server can group recurring problems, help identify causes, and recommend fixes; Ollie offers another route from trace review to suggested changes.

The pricing page lists a self-hosted Open Source plan at $0 per month, with AI observability, agent testing, tracing, test suites, and an Agent Playground. Free Cloud is also $0 and allows up to 10 members and 25,000 spans monthly. Pro is $19 per month, with up to 50 members and 100,000 spans monthly. For teams comparing tools, those free and paid entry points make it possible to assess the product’s fit before pricing an enterprise deployment.

The main caveat is that the advertised entry price does not tell the whole cost story. Pro starts at $19 per month, but that plan includes 100,000 spans monthly; additional Pro spans cost $5 per 100,000. The Free Cloud cap is 25,000 spans monthly. Teams with high-volume tracing should estimate usage before treating the starting price as their likely bill.

Retention is 60 days on both Free Cloud and Pro as listed. Pro offers customizable span limits and retention, but the pricing page does not publish the resulting cost. Enterprise pricing is custom, with unlimited members and spans listed alongside flexible deployments, dedicated support, and SLAs; there is no public price to compare against alternatives.

There are other unknowns to budget for: Ollie moves from a free trial to token purchases, but token prices are not published. The optional MLOps add-on also has no published price. Annual pricing is not published either. Comet’s feature set is broad, but teams should get a usage-based quote before committing if they expect to exceed plan caps or need the add-ons.

Comet lists two free options. Open Source costs $0 and is for teams self-hosting Opik; Free Cloud costs $0 and includes up to 10 team members, 25,000 spans per month, and 60-day data retention.

Pro Cloud is $19 per month, with up to 50 team members, 100,000 spans per month, and the same 60-day retention. That is the move from free to paid; the included span allowance is four times larger, and Comet says span limits and retention are customizable. Additional Pro spans cost $5 per 100,000.

Watch the add-ons. Ollie includes a coding harness trial, then requires purchased tokens; token pricing is not published. The optional MLOps platform add-on also has no published price. Enterprise pricing is custom. Comet shows monthly prices, but annual pricing is not published, so there is no listed annual discount to compare. Budget around your actual span volume and add-ons, not just the $19 headline.

The published materials don’t document a migration service, supported import paths, or data-export formats. Before switching, confirm how your current traces, evaluation datasets, prompts, and training-run history can move—and whether you need to keep a separate archive. Comet describes Opik as a tool for tracing, testing, and monitoring LLM applications, while its MLOps platform logs and compares training runs; moving those workflows may mean re-instrumenting applications and rebuilding evaluations. The exact effort depends on your current stack, and Comet’s materials don’t quantify it.

The same gap matters when leaving: data portability and retention after cancellation are not specified on the vendor's site. Ask Comet for export options and any limits before relying on it as your only record. The published materials include no Reddit migration reports or user accounts, so we can’t cite a recurring reason customers leave. Treat that as missing evidence, not proof that switching is painless—or difficult.