Buying snapshot
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| Decision point | What Comet states |
|---|---|
Products | Two product families on one platform: Opik for LLM and agent observability and evaluation, and MLOps for experiment tracking, model registry, dataset management and model production monitoring. Each comes with access to the free version of the other. |
Deployment | Opik: self-host the open-source version from GitHub, use Opik Cloud, or ask about custom deployment. MLOps: Self-Serve, Managed by Comet or On-Premise; the full platform can also be deployed locally as a simple single-user deployment. |
Free start | No credit card needed to sign up; the free tier has no time limit. |
Opik Cloud data region | US on Free and Pro Cloud; Custom on Enterprise. |
Academic use | Comet's pricing page is headed "Flexible Pricing Plans, Free for Academics"; confirm eligibility with Comet. |
Our take
If you build LLM applications or agents, start with Opik: the open-source and free cloud versions carry the full observability and evaluation feature set, so you can test it in real work before paying. If you train classical ML models and need drift detection in production, plan for an MLOps Enterprise conversation, because Comet reserves model production monitoring for that plan.
Evaluate Comet ML
Comparisons
Where it stands out
Open source on the hosted codebase. Opik Open Source is true OSS: it shares its codebase with the hosted versions. Opik Connect (the Ollie coding harness) and OpikAssist are not included in Open Source.
Evaluation and cost tracking beyond tracing. Opik includes token and cost tracking, prompt versioning, Test Suites with pass/fail assertions, 30+ built-in evaluation metrics, custom LLM-as-judge and code metrics, annotation queues for experts, and prompt optimization.
Broad instrumentation. Opik integrates with 40+ AI frameworks, model providers and AI gateways, such as LangChain, OpenAI and CrewAI, and supports OpenTelemetry for apps in Ruby, Java and other languages.
Framework logging for model training. Comet shows MLOps experiment logging for PyTorch, PyTorch Lightning, Hugging Face Transformers, Keras, scikit-learn and XGBoost, with experiment tracking, model registry, dataset versioning and hyperparameter search on the platform. Python visualisations let you build custom charts for your Comet dashboards.
Production monitoring for ML models. On MLOps Enterprise, model production monitoring adds data drift detection, feature distribution analysis, SQL-based custom metrics and notifications via Slack, email and other channels.
Product screenshot
Trade-offs to weigh
Two products, two billing units. Opik Pro is a monthly price that covers up to 50 team members, while MLOps Pro is charged per user. A team using both should budget them separately and confirm the Opik seat limit with Comet, because Comet's Opik FAQ says otherwise: "All plans include unlimited team members."
Free MLOps workspaces can disappear. Only logging in to the Comet UI resets the 90-day inactivity clock; SDK and API logging do not count. Comet sends an email 30 days before deletion, but a workspace fed only by scripts can still lose its data.
Opik Connect is metered separately on Free and Pro Cloud. On those plans, Opik Connect, which lets the Ollie agent read your codebase and propose fixes, is a free trial followed by token purchases, so its cost sits outside the plan price.
Check which plan includes guardrails. Comet says Opik AI guardrails (PII, topic and custom) are available for self-hosted deployment. Confirm with Comet which plan includes them before you count on them.
Pricing and plan conditions
Opik bills by spans: a span is one tracked operation, such as an LLM call, a tool call or any tracked function, and a single span can contain many tokens. MLOps bills by users, training hours and storage; a training hour is 60 minutes of active training time, measured between starting and stopping an Experiment.
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| Plan | Published price | What it adds |
|---|---|---|
Opik Open Source | $0 Free | Full AI observability and agent testing feature set; same codebase as the hosted versions. You run it yourself. |
Opik Free Cloud | Free plan | Up to 10 team members, 25k spans per month, 60-day data retention, Ollie coding harness trial. Confirm the seat limit with Comet (see trade-offs). |
Opik Pro Cloud | $19 Per month | Everything in the Free plan, plus up to 50 team members, 100k spans per month, and customizable monthly span limits and data retention periods. Confirm the seat limit with Comet (see trade-offs). |
Opik Pro Cloud usage add-ons | $5/100k spans; $29/100k spans | Pro Cloud only: additional spans at $5/100k spans; increasing span retention from 60 days to 400 days costs $29/100k spans. |
Opik Enterprise | Custom | Unlimited team members and custom usage plans. Everything in the Pro plan, plus flexible deployments, service accounts and view-only users, single sign-on, dedicated support and SLAs. |
MLOps Free | Free | 1 platform user, 100GB data storage, fair usage limits, community support. Includes tracking and comparing training runs, dataset management and versioning, and Model Registry. Subject to 90-day inactivity deletion (see trade-offs). |
MLOps Pro | $19 per user/month | Up to 10 users, 1,500 training hours, 500GB storage included, email support, no inactivity deletion. Extra usage: $1/training hours and $3/100GB/month. |
MLOps Enterprise | Custom | Unlimited users and training hours, model production monitoring, flexible deployments, single sign-on, service accounts and view-only users, dedicated support and SLAs, SOC 2, ISO 27001, ISO 9001, HIPAA and GDPR compliance. |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
81.7k PyPI weekly downloadsNot available Google Trends search interest0 vulnerabilities across 1 package
See all signals from 5 sources
Three-month score against stable baseline terms—not search volume or adoption.
PyPI · comet-ml@3.58.10
Alternatives to Comet ML
The reviewed substitutes for Comet ML among the experiment tracking, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Weights & Biases
- Weights & Biases and Comet ML both record runs, parameters, metrics and model versions so a team can reproduce and review results. Weights & Biases is known for rich visual dashboards and hyperparameter sweeps, while Comet ML adds LLM evaluation and production model monitoring to the same tracker.Applies to: Choosing the experiment tracker a machine learning team will standardise on.
- MLflow
- MLflow and Comet ML both track datasets, code changes, experiment history and models so results can be reproduced. MLflow is an open-source platform for the whole ML lifecycle, now extending to agents and LLM apps, while Comet ML is a commercial platform that stresses model evaluation, especially for LLMs, and production monitoring.Applies to: Choosing the experiment tracker a machine learning team will standardise on.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- ClearML
- ClearML and Comet ML both track experiments, datasets, code changes and models for ML teams. Comet ML adds evaluation for LLM applications and monitoring of production models, while ClearML is open source and wraps tracking into a wider platform with pipelines, model deployment and GPU compute management.Applies to: Choosing between these two for the experiment tracking decision.
- DVC
- Both answer where a model version and its inputs came from; Comet is a managed tracking service, DVC is Git-based versioning.Applies to: Tracing a model version back to the data and code that produced it.
Head to head
See detailed alternatives analysis
If you are evaluating Comet ML alternatives, you are likely running into one of three friction points: enterprise-only governance features, usage-based limits that tax evaluation at scale, or the operational burden of self-hosting for production workloads. Comet ML is a freemium MLOps platform that provides experiment tracking, model management, and -- through its open-source Opik product -- LLM observability, tracing, and evaluation. Its Pro Cloud plan starts at $19/month and its Free Cloud tier supports up to 10 team members with 25,000 spans per month. However, critical features like SSO, RBAC, audit logs, and compliance certifications are locked behind the Enterprise tier, pushing growing teams toward custom contracts before they are ready. We have tested the leading alternatives across deployment flexibility, pricing transparency, and evaluation capabilities to help you find the right fit.
Top Alternatives Overview
Weights & Biases is the most direct commercial competitor to Comet ML. It provides experiment tracking, model registry, hyperparameter sweeps, and LLM evaluation through its Weave product. W&B's Free tier includes up to 5 model seats and 5 GB of storage per month. The Pro plan costs $60/month and supports up to 10 model seats with 100 GB of storage. Enterprise pricing is custom and includes SSO, HIPAA compliance, custom roles, and audit logs. W&B has over 11,000 GitHub stars for its open-source client library. Where Comet ML gates governance to Enterprise, W&B makes team-based access controls available at the Pro tier, which matters for mid-sized teams that need basic collaboration security without an enterprise contract.
ClearML is an open-source MLOps platform under the Apache-2.0 license that covers the full ML lifecycle: experiment tracking, pipeline orchestration, dataset versioning, model serving, and hyperparameter optimization. Its Community tier is free for teams up to 3 users with 100 GB artifact storage. The Pro tier costs $15/user/month and adds cloud auto-scaling, hyperparameter optimization, and pipeline automations for up to 10 users. Scale and Enterprise tiers add Kubernetes integration, SSO, fractional GPUs, and multi-tenant infrastructure management. ClearML has over 6,500 GitHub stars and is one of the few platforms that genuinely delivers a full self-hosted MLOps stack at no cost through its open-source edition.
Neptune.ai was a dedicated experiment tracker known for handling large-scale training runs with fast metadata querying and visualization. OpenAI entered into a definitive agreement to acquire Neptune, with the stated goal of integrating Neptune's tools into OpenAI's training stack. Neptune's product had supported tracking thousands of runs, analyzing metrics across layers, and surfacing issues during model training. Given the acquisition, Neptune's future as a standalone product is uncertain, but its technology is likely to influence the next generation of training observability tools.
MLflow is the most widely adopted open-source experiment tracking platform, with over 27,000 GitHub stars and an Apache-2.0 license. Created by Databricks, it provides experiment tracking, model registry, model deployment, and an evaluation framework for both traditional ML and LLMs. MLflow is entirely free to self-host and has deep integrations with Databricks, Azure ML, and Amazon SageMaker. It lacks a managed cloud offering from the MLflow project itself (Databricks provides managed MLflow), which means self-hosting teams own the operational burden. For teams already invested in the Databricks ecosystem, MLflow is essentially free and fully integrated.
DVC (Data Version Control) is an open-source tool with over 15,000 GitHub stars and an Apache-2.0 license that brings Git-like version control to ML projects. It tracks datasets, models, and experiments alongside code using Git, and works with any storage backend including S3, GCS, Azure, and SSH. DataChain Studio (formerly DVC Studio) provides a web UI for experiment tracking and collaboration. DVC is the strongest choice when data and model versioning are your primary concern and you want everything managed through Git workflows rather than a separate tracking platform.
Kedro is an open-source Python framework developed by QuantumBlack (McKinsey) for building reproducible, maintainable data and ML pipelines. It enforces software engineering best practices with a standardized project template, data catalog abstraction, and pipeline visualization. Kedro has over 10,000 GitHub stars and is part of the Linux Foundation's LF AI & Data. It is not a direct replacement for Comet ML's experiment tracking but complements tools like MLflow or DVC by adding pipeline structure and reproducibility that Comet ML does not natively provide.
Architecture and Approach Comparison
Comet ML and its alternatives split into two architectural camps: managed platforms with proprietary backends and open-source tools you deploy yourself.
Comet ML runs a dual-product architecture. Comet MLOps handles traditional experiment management -- logging metrics, hyperparameters, code changes, and model artifacts through its Python SDK with integrations for PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, and Hugging Face. Opik, Comet's open-source LLM evaluation product, provides tracing, annotation, automated scoring with LLM-as-a-judge metrics, and agent optimization. Opik can be self-hosted or used via Comet's cloud. The two products share the Comet platform but have separate pricing structures and feature sets.
Weights & Biases follows a similar dual-track approach: its core platform handles experiment tracking and model management, while Weave handles LLM application tracing and evaluation. W&B's architecture is primarily SaaS-first with an enterprise self-hosted option. Its client library is open-source (MIT license), but the server is proprietary.
ClearML takes the broadest architectural approach among the alternatives. Its open-source platform includes experiment tracking, pipeline orchestration with dependency injection and result caching, dataset versioning, model serving with REST endpoints, and a remote execution agent system that supports GPU clusters and cloud VMs. The ClearML AI Infrastructure Platform adds an Infrastructure Control Plane for GPU resource management across on-premise, cloud, and hybrid environments.
MLflow's architecture is modular: Tracking, Models, Model Registry, and Projects are separate components that can be used independently. This modularity means you can adopt MLflow's experiment tracking without buying into its deployment model. MLflow stores data in a backend store (database) and an artifact store (object storage), making it straightforward to deploy on any infrastructure.
DVC operates entirely through the Git workflow. Experiments are tracked as Git commits with lightweight metafiles pointing to data and model artifacts stored in external storage. This architecture means there is no server to maintain -- your Git repository and cloud storage are the entire backend.
Pricing Comparison
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| Tool | Pricing Model | Starting Price | Free Tier | Enterprise |
|---|---|---|---|---|
| Comet ML | Freemium | $19/month (Pro Cloud) | Yes (10 users, 25k spans) | Custom |
| Weights & Biases | Freemium | $60/month (Pro) | Yes (5 model seats) | Custom |
| ClearML | Freemium | $15/user/month (Pro) | Yes (3 users, 100 GB) | Custom |
| Neptune.ai | Enterprise | Contact for pricing | Previously available | Acquisition by OpenAI |
| MLflow | Open Source | $0 (self-hosted) | Full platform free | Via Databricks |
| DVC | Open Source | $0 (self-hosted) | Full platform free | Via DataChain Studio |
| Kedro | Open Source | $0 | Full framework free | N/A |
Comet ML sits in the middle of the pricing spectrum. Its Pro Cloud plan at $19/month is significantly cheaper than Weights & Biases at $60/month for comparable managed experiment tracking. However, ClearML's Pro tier at $15/user/month includes pipeline orchestration, hyperparameter optimization, and cloud auto-scaling that Comet ML does not offer at any tier. The open-source alternatives -- MLflow, DVC, and Kedro -- cost nothing to run but shift operational responsibility to your team.
The real cost difference surfaces at the governance boundary. Comet ML gates SSO, RBAC, audit logs, and compliance certifications entirely to its Enterprise tier. W&B provides team-based access controls at the Pro tier. ClearML's Scale tier includes SSO and priority support. For teams that need governance controls without enterprise pricing, ClearML and W&B provide earlier access to those features.
When to Consider Switching
We recommend exploring Comet ML alternatives when your team's requirements have outgrown what the Free and Pro tiers offer, or when a different architectural approach better matches your workflow.
If governance and access control are blocking you, both Weights & Biases and ClearML provide role-based access and team management at their mid-tier plans rather than requiring an enterprise contract. ClearML's Scale tier adds SSO and Kubernetes integration for organizations that need infrastructure-level governance without enterprise-only pricing.
If you need a full MLOps platform rather than just experiment tracking, ClearML covers experiment management, pipeline orchestration, dataset versioning, model serving, and compute orchestration in a single open-source package. Comet ML's Opik covers LLM evaluation well, but the broader MLOps lifecycle -- pipelines, model serving, compute scheduling -- requires assembling additional tools around Comet.
If your team is deeply embedded in the Databricks ecosystem, MLflow provides native experiment tracking and model registry at no additional cost. Migrating from Comet ML to MLflow in a Databricks environment eliminates a separate vendor dependency entirely.
If data and model versioning through Git workflows is your priority, DVC offers a fundamentally different approach. Instead of logging experiments to a separate platform, DVC tracks everything alongside your code in Git, which appeals to teams that prefer infrastructure-minimal tooling.
If Comet ML's pricing works for your team and you actively use both Comet MLOps and Opik for experiment tracking and LLM evaluation respectively, staying makes sense. The combination of traditional ML experiment management and GenAI observability in one vendor is a genuine differentiator that few competitors match at Comet's price point.
Migration Considerations
Migrating from Comet ML requires planning around experiment history, SDK integrations, and team workflows.
Comet ML's Python SDK hooks into training frameworks through decorators and context managers. Moving to Weights & Biases involves replacing comet_ml.Experiment calls with wandb.init() and corresponding logging methods -- the migration surface is primarily at the instrumentation layer, not the training code itself. Moving to ClearML is similarly straightforward: ClearML's two-line integration auto-captures most framework outputs without explicit logging calls, which can actually reduce instrumentation code during migration.
Experiment history is the harder problem. Comet ML does not offer a bulk export API for migrating historical runs to a competing platform. We recommend keeping read-only access to your Comet ML account for historical reference while logging new experiments to your target platform. For teams with strict data retention requirements, export critical run metadata and artifacts via Comet's REST API before decommissioning.
If you use Opik for LLM tracing and evaluation, note that Opik is open source and can be self-hosted independently of Comet's commercial platform. Teams migrating away from Comet MLOps for experiment tracking can potentially continue using Opik for LLM evaluation if that component is working well.
For teams moving to MLflow, the Databricks community maintains migration utilities and documentation for common experiment tracking migrations. The MLflow Tracking API maps closely to Comet's concepts of experiments, runs, parameters, and metrics.
Plan for a one-to-two-week parallel logging period where both platforms receive experiment data simultaneously. This validates that your new tool captures everything your team relies on before fully cutting over. The most common migration surprise is not the SDK swap itself but discovering which custom dashboards, alert rules, and team workflows need recreation in the new platform.