mlop is live · free plan; paid plans on request · last verified 24 Oct 2026 · viability healthy
mlop
Open-source experiment tracking that stays 100% compatible with the Weights & Biases API
mlop earns a look if you're already on Weights & Biases and want the same logging calls without the bill — the API compatibility is the pitch and it's a real one. The free tier (1 seat, unlimited logging hours, 10 GB storage, no credit card) is genuinely usable for solo work, and self-hosting on Enterprise answers the data-residency question. Just don't buy it expecting a complete MLOps stack; this is experiment tracking, done well and narrowly, and Compute Instances is still in private beta while Inference is not yet available.
Verdict generatedVerified 6d ago · liveness 71/100 · cite: rightaichoice.com/tools/mlop
- ML researchers logging many runs who want unlimited logging hours without metered billing
- Small teams migrating off Weights & Biases using the same API calls
- Organizations that need self-hosted experiment tracking for data residency
- Open-source-first engineering teams that want to read and modify the tracking stack
- Teams needing a full MLOps suite with model registry, feature store, and CI/CD
- Groups that need Compute Instances or Inference in production today — both are experimental
- Teams whose tracking workflow depends on connectors well beyond Git and GitHub
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Skip mlop if you need a model registry, feature store or CI/CD orchestration in the same platform, rather than an experiment tracker that logs runs, media and Git state.
The Free plan's 10 GB storage cap is a hard ceiling — media-heavy runs with logged images fill it faster than metric-only logging, and the next size up is Pro at 100 GB.
Free at $0/month covers 1 seat, unlimited logging hours and 10 GB storage, which is the cheapest workable entry point for a solo researcher. Pro (up to 10 seats, 100 GB) is quoted on contact, and Enterprise is custom — so mlop is priced for individuals and small-to-mid teams, and large organizations comparing against a fully hosted MLOps suite should price Enterprise directly rather than assume a list rate.
In short
mlop — Open-source experiment tracking that stays 100% compatible with the Weights & Biases API. Best for ML researchers logging many runs who want unlimited logging hours without metered billing, Small teams migrating off Weights & Biases using the same API calls, Organizations that need self-hosted experiment tracking for data residency. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
What is mlop?
mlop — Open-source experiment tracking that stays 100% compatible with the Weights & Biases API. Best for ML researchers logging many runs who want unlimited logging hours without metered billing, Small teams migrating off Weights & Biases using the same API calls, Organizations that need self-hosted experiment tracking for data residency. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
mlop is an open-source experiment tracking platform for machine learning teams, backed by Y Combinator (X25). It covers the core loop of an ML project: log parameters and gradients and watch them update in real time, track model accuracy over time, and attach images or other media to a run. A short Python SDK handles the wiring — call mlop.init() with a project and run name, log a dict of metrics, then run.finish(). The feature most teams will care about is API-level compatibility with Weights & Biases. Migrating is a matter of pointing existing logging calls at mlop rather than rewriting instrumentation code — mlop's own homepage advertises it as "100% compatible with Weights & Biases API for easy migration." Behind that sits reproducibility tooling: Git status tracking that records the commit and flags uncommitted files (the homepage shows runs labelled with commits such as git: a2dg3t plus an "Uncommitted files" warning), so a run can be traced back to an exact code state. Email notifications and critical issue alerts cover the monitoring side when a training job goes sideways overnight. Pricing spans a free tier for individuals and small teams, a Pro plan for businesses scaling with AI, and an Enterprise tier for large organizations. Compute Instances (private beta) and Inference (coming soon) are listed alongside experiment tracking as the product roadmap, both still experimental. Where mlop sits relative to alternatives: it competes on data ownership and cost against proprietary tracking services, and trades breadth for focus. If you need a model registry, feature store, or CI/CD orchestration, this is one piece of that picture rather than the whole thing.
Key features
- Track parameters and gradients in real time during training
- Log images and multi-media artifacts alongside metrics
- Monitor model accuracy and performance metrics over time
- Git status tracking that flags uncommitted files per run
- Email notifications and critical issue alerts
- 100% compatible with the Weights & Biases API for migration
- Python SDK: mlop.init(), run.log(), run.finish()
- Open-source with community-driven development
- Self-hosted deployment available on Enterprise
- Unlimited logging hours on every tier
- Compute Instances (private beta)
- Inference (coming soon)
- Backed by Y Combinator (X25)
Behind the verdict
mlop's bet is narrow and legible: be the open-source drop-in for experiment tracking, and win the teams that already write Weights & Biases logging calls. The homepage states this plainly — "100% compatible with Weights & Biases API for easy migration" — and the demo code shows the shape of it: `run = mlop.init(project="vision-language-model", name="gpt4o-vision")`, then `run.log({"loss": loss, "accuracy": accuracy, "img": mlop.Image(gen_model)})`, then `run.finish()`. If your training scripts already look like that with a different import, the migration cost is close to zero. That is the strongest thing mlop has going for it. The second real strength is reproducibility plumbing. mlop's Git status tracking records the commit a run came from and flags uncommitted files next to it — the product page renders this as run cards like "BERT Fine-tuning / git: a2dg3t" and "XGBoost Tuning / git: c9j4l0 / Uncommitted files." Anyone who has spent an afternoon trying to reconstruct which working-tree state produced a checkpoint knows why that warning matters. Pair it with parameter and gradient tracking that updates in real time and you have the debugging loop most ML researchers actually run day to day. Monitoring is covered but modest: email notifications and critical issue alerts on model performance. There is no incident-management layer, no on-call routing, no Slack alerting documented in what we scraped. The honest limits are on the roadmap and the integration surface. The homepage lists Compute Instances as "Private Beta" and Inference as "Coming Soon" — do not plan production work around either. Integrations documented on the pages we reached are Git and GitHub; mlop's homepage talks about connecting to "your code base and development workflow" and links a Docs page, but we did not reach the integrations or docs pages this run, so treat the breadth of connectors as an open question rather than a confirmed gap. And this is a tracking tool, not an MLOps suite: no model registry, no feature store, no CI/CD orchestration is described anywhere in the vendor's own material. Teams that need those will run mlop alongside something else, or pick a broader platform instead. One more practical point on pricing. The free tier is a real tier, not a trial: 1 seat, unlimited logging hours, 10 GB of storage, no credit card required to start. Pro is listed as "Let's Talk" with up to 10 seats, unlimited logging hours and 100 GB of storage plus email support, and Enterprise is custom with unlimited seats, unlimited storage, a self-hosted option, a security audit, a private Slack channel and 24/7 support from founders. Only the Free tier has a published number; the paid tiers are quoted on contact, so budget planning for Pro or Enterprise starts with a conversation, not a checkout page.
Limitations
- Free plan storage is capped at 10 GB and limited to 1 seat.
- Pro supports up to 10 seats with 100 GB of storage, while Enterprise adds unlimited seats, unlimited storage and a self-hosted option — Pro and Enterprise are both quoted on contact rather than published as a list price.
- Compute Instances is in private beta and Inference is listed as coming soon, so neither is generally available.
- The vendor's own material describes experiment tracking, alerts and Git-status reproducibility; a model registry, feature store or CI/CD orchestration is not part of the product.
as of 2026-10-03
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Is mlop still active?
mlop is live · free plan; paid plans on request · last verified 24 Oct 2026 · viability healthy.
Viability score
How well maintained and how widely used is mlop? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about mlop?
What people actually say about mlop — is it worth it?
Our community sentiment research written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
In users’ own words
“Hey guys, just launched a fully open source alternative to wandb called [mlop.ai](http://mlop.ai/), that is performant and secure (yes our backend is in rust). Its fully compatible with the wandb API so migration is just a one line change. WandB has pretty bad performance, they block on `.log` calls. [**This video** ](https://github.com/mlop-ai/mlop)shows a comparison of what non-blocking logging+upload actually…”
Real posts from independent users, linked to the source — not testimonials we collected.
How we verified this
Every listed fact about mlop— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 24 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified mlop 13 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed hidden costs, who should skip it, limitations, switching out, switching in, our verdict, description, setup time, use cases, pricing fit, SSL certificate
- — changed who it suits, description, our verdict, features, integrations, who should skip it, pricing tiers, summary
- — changed domain expiry
- — changed limitations, hidden costs, description, pricing fit, setup time, who should skip it, switching in, switching out, pricing tiers, features, our verdict
- — changed SSL certificate
- — changed pricing page
Showing the 6 most recent of 13 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does mlop cost in 2026?
mlop: Free to start; it also has paid plans whose price we could not confirm — see the pricing table. Pricing last verified 24 Oct 2026.
Plans compared
Every published mlop tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Free | Free | — | Solo researchers and small teams who want real experiment tracking without a card on file — 1 seat, unlimited logging hours, 10 GB of storage.Adds: Starting tier: 1 seat, unlimited logging hours, 10 GB storage, no credit card required to start, priced at $0/month. |
| Pro | Contact Us | quote | A small lab or startup team of up to ten people logging media-heavy runs that outgrow 10 GB of storage.Adds: Up from Free: seat count rises to 10, storage to 100 GB and email support is included; priced on contact rather than published. |
| Enterprise | Custom | quote | Large organizations that need self-hosting for data residency or compliance, plus unlimited seats and storage.Adds: Up from Pro: unlimited seats and storage, self-hosted option, security audit, private Slack channel and 24/7 support from founders; custom pricing. |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where mlop's pricing actually pencils out — and where peers do it cheaper.
Free at $0/month covers 1 seat, unlimited logging hours and 10 GB storage, which is the cheapest workable entry point for a solo researcher. Pro (up to 10 seats, 100 GB) is quoted on contact, and Enterprise is custom — so mlop is priced for individuals and small-to-mid teams, and large organizations comparing against a fully hosted MLOps suite should price Enterprise directly rather than assume a list rate.
Has mlop's pricing changed?
No plan price for mlop has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick mlop?
Pick mlop if you are ML researchers logging many runs who want unlimited logging hours without metered billing, Small teams migrating off Weights & Biases using the same API calls, Organizations that need self-hosted experiment tracking for data residency, Open-source-first engineering teams that want to read and modify the tracking stack.
Look elsewhere if you are Teams needing a full MLOps suite with model registry, feature store, and CI/CD, Groups that need Compute Instances or Inference in production today — both are experimental, Teams whose tracking workflow depends on connectors well beyond Git and GitHub.
Skip it if Skip mlop if you need a model registry, feature store or CI/CD orchestration in the same platform, rather than an experiment tracker that logs runs, media and Git state.
GeneratedReal-world workflow fit
Concrete scenarios for the personas mlop actually fits — and what changes day-one when you adopt it.
You want run tracking without a metered logging bill. You install the mlop SDK, call mlop.init(project="vision-language-model", name="gpt4o-vision"), log loss, accuracy and generated images each epoch with run.log(), and close with run.finish().
Outcome: Runs land in the Free tier's 10 GB with unlimited logging hours and no credit card, and each run carries the Git commit it came from.
Your training scripts already call the W&B logging API. You keep the instrumentation and repoint the calls at mlop, which advertises 100% API compatibility, then set email alerts so a degraded run page reaches you instead of waiting until morning.
Outcome: You keep the same experiment dashboards and alerting flow while moving off a proprietary tracking bill.
Your data cannot leave your infrastructure. You take the Enterprise tier for the self-hosted option, unlimited seats, storage and a security audit, and run mlop next to your existing model registry and CI rather than replacing them.
Outcome: Experiment tracking and Git-state reproducibility stay inside your network, with 24/7 support from the founders and a private Slack channel for escalations.
Use cases
- Track hyperparameter experiments for deep learning models in real time.
- Monitor model training metrics and receive alerts on performance degradation.
- Collaborate with team members on shared experiment logs and results.
- Migrate existing Weights & Biases projects to an open source alternative.
- Reproduce historical experiments with automatic Git state and parameter logging.
- Self-host experiment tracking to meet data residency or compliance needs.
Setup time & first value
How long it actually takes to get something useful out of mlop — broken out by persona, not the marketing-page minute.
Solo researcher: minutes — pip install, mlop.init(), run.log(), and the demo flow on the homepage is the whole tutorial. Small team off W&B: an afternoon, since the API is compatible and you're repointing existing calls rather than rewriting instrumentation. Enterprise self-hosted: the self-hosted option and security audit are scoped with the vendor, so plan days of internal infra and review work
Switching to or from mlop
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Weights & Biases: keep your existing logging calls and point them at mlop, which advertises 100% compatibility with the Weights & Biases API.
- →From spreadsheets or ad-hoc logging: replace manual metric capture with mlop.init() and run.log() calls per training run.
- →From no run tracking at all: add mlop to an existing training script so each run records its metrics, media and Git commit.
- ↗To Weights & Biases: no rewrite needed if you stay on the compatible logging calls, which is the same surface mlop implements.
- ↗To a full MLOps platform: export your run history and reattach it to a suite that also carries a model registry, feature store and CI/CD orchestration.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “mlop”, and we withheld 6: 6 could not be judged, because “mlop” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about mlop.
Official links
What are the best mlop alternatives?
The closest alternatives to mlop we list are Weights & Biases, Langfuse and Phoenix. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Weights & Biases | Freemium | Pricing not published | — |
| Langfuse | Freemium | Pricing not published | — |
| Phoenix | Freemium | Pricing not published | — |
Alternatives to mlop
View allWeights & Biases
Weights & Biases tracks ML experiments, versions models and datasets, and traces LLM apps — now inside CoreWeave Forge
Tools that pair well with mlop
Common stack mates teams adopt alongside mlop, with the specific reason each pairing earns its keep.
Weights & Biases
Weights & Biases tracks ML experiments, versions models and datasets, and traces LLM apps — now inside CoreWeave Forge
Langfuse
Open-source LLM observability: trace agents, manage prompts, and run evals on one MIT-licensed platform.
Phoenix
Open-source LLM observability and agent evaluation you can trace, self-host, and eval end to end.
mlop vs its closest rival
mlop's closest head-to-head is with Spider Cloud — If you need fast, reliable web scraping for AI agents and RAG pipelines, Spider Cloud is the clear choice with its Rust-powered API, AI Studio, and competitive per-page pricing. For ML experiment tracking, especially if you want to migrate from Weights & Biases to an open-source alternative, mlop is a strong option with API compatibility and self-hosted enterprise support. The products serve completely different domains, so your decision depends on whether your primary need is data extraction or experiment management. Verdict generated
Head-to-head comparisons
Mlop vs Spider Cloud
If you need fast, reliable web scraping for AI agents and RAG pipelines, Spider Cloud is the clear choice with its Rust-powered API, AI Studio, and competitive per-page pricing. For ML experiment tracking, especially if you want to migrate from Weights & Biases to an open-source alternative, mlop is a strong option with API compatibility and self-hosted enterprise support. The products serve completely different domains, so your decision depends on whether your primary need is data extraction or experiment management.
GeneratedMlop vs Screenplayiq
Buyers should choose ScreenplayIQ if they need AI-powered script analysis with box office predictions and structural feedback for feature films. Choose mlop if they are ML teams seeking open source, self-hosted experiment tracking with seamless migration from Weights & Biases. These tools serve entirely different domains.
GeneratedMlop vs Temporal Ai
Choose Temporal if you need reliable, crash-proof orchestration for AI agents or multi-step microservices; mlop is the better fit if your primary need is lightweight, open-source ML experiment tracking with easy W&B migration. They serve fundamentally different domains, so the decision depends on whether your bottleneck is workflow reliability or experiment visibility.
GeneratedFrequently asked questions
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