Overview

Comet groups Opik and MLOps on one underlying platform, but they solve different problems. Opik focuses on generative AI applications and agentic systems, while MLOps covers the work of tracking experiments, managing datasets and models, and monitoring models in production. Teams spanning both areas may value that range, but should choose a plan for the workflow they need rather than assume one subscription covers both.

Founded in 2017 and headquartered in New York City, Comet supports API and SDK access, with hybrid deployment options. Its Trust Center lists ISO 9001:2015, ISO/IEC 27001:2022, and SOC 2 Type 2 compliance.

Key features

Opik tracing and evaluation

Opik traces execution paths through context retrieval, model responses, and tool calls. That detail is useful when teams need to follow how an agent reached an outcome, rather than inspect only the final response. Test suites, datasets, and experiments provide structure for evaluating changes, while built-in LLM-as-a-judge and heuristic metrics offer ways to assess runs. Integrations with more than 40 AI frameworks, model providers, and gateways include LangChain, OpenAI, Google ADK, LangGraph, and CrewAI. OpenTelemetry support extends tracking to AI applications written in Ruby, Java, and other languages.

The open-source Opik version can be self-hosted, and Enterprise offers flexible deployment options. This gives teams a route to self-managed use, while enterprise buyers can consider deployment arrangements suited to their organization.

MLOps workflow

Comet MLOps combines experiment tracking, dataset management, a model registry, and production monitoring. For teams building and training machine-learning models, those capabilities bring several parts of the workflow into one product family. The free plan's fair-usage policy and 100GB data allowance are useful boundaries to consider before relying on it for sustained team workloads.

Pricing

Comet is freemium, with paid plans starting at $19/user/mo. Opik and MLOps have separate tiers, so compare their limits against the work and team size you expect to support.

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 no-cost route for teams prepared to run the open-source version themselves.
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 practical starting point for a small Opik team, but the monthly span cap and retention period matter as usage grows.
MLOps Free: 0.00 USD per free, billed Free plan. It includes 1 platform user, fair usage, and 100GB data usage. This fits an individual exploring MLOps, but the single-user limit makes it a poor choice for a collaborative team.
Pro Cloud: 19.00 USD per month, billed Per month. It raises Opik's allowance to 50 team members and 100k spans per month, with 60-day data retention. Teams that outgrow Free Cloud gain capacity, though they still need to account for the span ceiling and retention window.
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. This is the paid MLOps tier for teams needing shared access and more capacity; the per-user term and included training hours should shape cost and usage planning.
Enterprise and MLOps Enterprise: custom pricing. Enterprise includes unlimited team members, custom usage plans, flexible deployments, dedicated support, and SLAs; it also lists single sign-on, service accounts, and view-only users. MLOps Enterprise includes unlimited users and training hours, flexible deployments, dedicated support, and SLAs. These tiers suit organizations needing larger or tailored deployments, but require a custom quote.

Community support is included across plans. Email support is listed for paid cloud Pro and Enterprise, while Enterprise adds dedicated support and SLAs. A free trial is available.

Platforms

Comet supports API, Linux, macOS, self-hosted, web, and Windows. That mix accommodates teams working through APIs or web interfaces as well as those seeking self-hosted deployment.

Who it's for

Choose Opik if you build applications or agents that make LLM calls and need tracing and evaluation tools around their execution. Choose MLOps if your work centers on training machine-learning models, tracking experiments, managing datasets and registries, or monitoring production models. Comet is less compelling for buyers who need a single undifferentiated plan across both workflows, since the product families have separate pricing and limits.

Pros and cons

Pro: Opik combines execution tracing with test suites, datasets, experiments, and evaluation metrics, giving teams tools to investigate and assess agent behavior.
Pro: More than 40 integrations and OpenTelemetry support, including Ruby and Java, broaden its fit across frameworks and languages.
Pro: MLOps brings experiment tracking, dataset management, model registry, and production monitoring into one product family.
Con: Free Cloud is capped at 25k spans per month and 60-day retention, which may constrain teams with higher tracing volumes or longer review needs.
Con: MLOps Free is limited to one platform user; collaborative teams need a paid tier or another arrangement.
Con: Pro Cloud and MLOps Pro have different billing terms and limits, so comparing costs requires matching the right product family and usage pattern.

Alternatives

Consider Weights & Biases if its free tier's five model seats, 5 GB/mo storage, and 1 GB/mo Weave data ingestion fit your needs. DagsHub is another freemium option, with an Individual plan that supports unlimited public repositories and unlimited private repositories for non-commercial use. For a browser-based notebook option, Databricks Notebooks offers a free edition with one serverless workspace and limited compute size and usage. GitLab Package Registry has a free tier for up to five users per top-level group, 400 compute minutes per month, and 10 GiB adjustable storage. Amazon SageMaker Autopilot is a paid option with pay-as-you-go pricing and no minimum fees or upfront commitments. ClearML offers a free Community plan and a self-hosted version described as 100% open source. DVCLive is a free open-source Python library for Python 3.9 and later. Hopsworks AI Lakehouse offers a free plan with one project, Feature Store and Model Registry, community support, and no credit card required.

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

Verdict

Comet is a strong fit for teams that need either LLM application observability and evaluation or a connected MLOps workflow, with a free entry point for both. Its breadth and integration support are the main reasons to choose it; look elsewhere if the span, retention, user, or training-hour limits do not match your workload, or if you need one plan spanning both product families.

	Comet	Jozu Hub
Free plan	Yes	Yes
Free trial	Yes	Yes
Paid from	$19/mo	Free
Open source	No	No
Platforms	api, Linux, macOS, self-hosted, Web, Windows	api, self-hosted, Web
Free plan	Yes	Yes
Model versioning	—	Yes
Model lineage	—	Yes
Deployment tracking	—	Yes
Artifact storage	—	Yes

Comet vs Jozu Hub

Comet

Jozu Hub