mlop
by mlop · Open-source experiment tracking for machine learning teams
BenchRank score
Screenshots of mlop
Homepage
Overview
mlop is an open-source platform for tracking machine learning experiments. A Python SDK logs metrics, parameters, gradients and images from a training run, alongside model versions, git commit and uncommitted files, and the web app charts them over time and sends email alerts on performance issues. The API is stated to be compatible with Weights & Biases.
- Best for
- ML teams wanting open-source experiment tracking that is API-compatible with Weights & Biases
- Pricing
- Free tier at $0/month for one seat with 10 GB storage; Pro and Enterprise prices are not published and require contacting the vendor.
- Runs on
- Web
Strengths and trade-offs
Strengths
- Open source, with a self-hosted option on the Enterprise tier
- States 100% compatibility with the Weights & Biases API
- Logs parameters, gradients, media and git state per run
- Free tier has unlimited logging hours and 10 GB storage
Trade-offs
- Pro and Enterprise prices are not published; you must contact sales
- Compute is in private beta and inference is listed as coming soon
- Free tier is a single seat with 10 GB storage
- Pro is capped at 10 seats and 100 GB storage
How mlop markets itself
A structured read of the promise, proof and page design on mlop’s captured homepage.
Homepage capture
“The MLOps platform”
- Angle: Open source / ownership
- Hero: Product screenshot
Pricing
Published plans and prices from mlop’s own pricing page.
How this score is made up
Each dimension is scored out of 100 and combined into the headline score using fixed weights.
MCP support
Whether an agent can drive the product through the Model Context Protocol, and how much setup that takes.
API quality
Public API surface: machine-readable spec, official SDKs, documented auth, errors, rate limits and versioning.
Documentation
Publicly reachable docs — coverage, freshness, code samples and machine readability.
Agent friendliness
How readable the site is to an automated client: llms.txt, structured data, server-rendered content, crawler access.
Pricing transparency
Whether real prices are published, self-serve signup exists, and usage costs are knowable without a sales call.
Changelog
A public, dated record of what shipped and when — the clearest signal that a product is still alive.
Marketing site structure
Whether the site answers a buyer's questions: clear positioning, the pages that matter, and accessibility.
Page speed
How fast the site loads for real visitors: Chrome UX Report 75th-percentile LCP, INP and CLS, with a Lighthouse mobile run standing in where a site has too little traffic for field data.
Operational trust
Status page and incident history, security disclosure, compliance and data-processing documentation.
Measured, but not part of the score
Useful to know, but not a mark for or against the product — so these do not affect the ranking.
Openness
Source availability, self-hosting, data export and open standards. Scored and shown, but not part of the composite — paid SaaS is not worse for being paid SaaS.
Maintenance
Release cadence and repository activity. Scored and shown, but not part of the composite — it is only measurable for open repositories.
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Where this comes from
The mlop pages BenchRank reads when it scores the product — its documentation, release notes, status and security pages, and its repository where there is one.
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