Tools
Comet vs MLflow
Comet or MLflow? Their plans monthly and yearly, the capabilities their makers state, security and latest updates, side by side — read from the makers' own pages.
In inglese
In short
- Both: API, Self-hosted
- Only Comet states: Ask in plain language, Charts and dashboards, Builds models, Real-time data, Single sign-on
- Only MLflow states: Command line, Official SDKs, Builds agents and workflows, Traces and evaluates
Comet
Comet is an MLOps platform for tracking experiments, managing datasets and models, and monitoring machine learning systems.
Plans
Open Source
Free
- Full AI observability & agent testing feature set
- True OSS: same codebase as the hosted versions
Free Cloud
Free
- Up to 10 team members
- 25k spans per month
- 60-day data retention
- Agent tracing & analysis
- Test Suites & assertions
- Agent Playground
Pro Cloud
- $19 Per month
- Up to 50 team members
- 100k spans per month
- 60-day data retention
- Customizable monthly span limits
- Customizable data retention periods
Enterprise
Price on request
- Unlimited team members
- Custom usage plans
- Flexible deployments
- Service accounts and view-only users
- Single sign-on
- Dedicated support and SLAs
Free
Free
- 1 platform user
- Generous free tier
- Track and compare machine learning training runs
- Dataset management and versioning
- Model Registry
- LLM evaluation included for free
Pro
- $19 Per user/month
- Up to 10 users
- 1500 training hours included
- Email support
- Generous storage limits
- LLM evaluation included for free
Enterprise
Price on request
- Unlimited users
- Unlimited training hours
- Flexible deployments
- Model production monitoring
- Service accounts and view-only users
- Dedicated support and SLAs
Prices checked 2026-09-25 on the maker’s page.
Capabilities
- Ask in plain language — “Ask natural language questions to help root-cause issues, identify performance bottlenecks, and get debugging recommendations for your traces” source
- Charts and dashboards — “Built-in Charts” source
- Builds models — “One Platform to Train, Optimize, and Version Your Models” source
- Real-time data — “Easily track training metrics in real time, compare performance, debug, and evaluate models faster with built-in code panels.” source
- API — “Opik’s REST API and complete Python client can be used with both the Open-Source platform and Opik Cloud” source
- Self-hosted — “Self-hosting the open-source version of Opik is easy!” source
- Single sign-on — “Enterprise SSO (OAuth 2.0, SAML, and LDAP protocols)” source
Security
- SOC 2 Type II — “SOC 2 Type 2” source
- SOC 2 — “SOC 2 Type 2” source
- ISO 27001 — “ISO/IEC 27001:2022” source
MLflow
MLflow is an open-source platform for tracking, evaluating, deploying, and monitoring machine learning models, LLM applications, and agents.
Plans
Not read from the maker’s page yet.
Prices checked 2026-09-25 on the maker’s page.
Capabilities
- Command line — “The easiest way to start MLflow server is to run the mlflow CLI command in your terminal.” source
- API — “This page hosts the API documentation for MLflow.” source
- Self-hosted — “Thousands of users and organizations run their own MLflow instances to meet their specific needs.” source
- Official SDKs — “Python API” source
- Builds agents and workflows — “Hands-on guides and code examples for building Agents and LLM applications with MLflow.” source
- Traces and evaluates — “Capture complete traces of your LLM applications and agents to get deep insights into their behavior.” source
Latest updates
- MLflow 3.16.0 Highlights: Build-Your-Own Trace Views, a Redesigned Trace Explorer, and Span Links (3.16.0)
Build trace views in plain English, use the trace explorer, and record links between spans.
- MLflow 3.15.0 Highlights: MCP Registry, a Smarter Assistant, and Multimodal Judges (3.15.0)
Register and share MCP servers, choose LLM providers in the Assistant, save and share Runs table views, transfer artifacts using presigned URLs, and evaluate image…
- MLflow 3.14.0 Highlights: One-Line Agent Onboarding, Review Queues, Pytest Integration, and the LLM Playground (3.14.0)
Onboard apps with mlflow agent setup, collect trace reviews, manage evaluation datasets in the UI, run pytest regression tests, and use an in-browser LLM Playground.