I've been looking into experiment tracking and model management tools for our small ML team. Weights & Biases is fantastic, but their Team plan starts at $50/user/month, which adds up fast for a startup watching every dollar.
I know the open-source ecosystem has several contenders now. I've done some initial digging and have a basic comparison, but I'd love to hear from teams who have actually run one in production.
My main criteria are:
* Must be self-hostable (on our own cloud account) to keep costs variable, not per-user.
* Needs decent experiment tracking with metrics/params logging and basic visualization.
* Should handle model artifact storage and versioning.
* A functional Python SDK is non-negotiable.
From my spreadsheet breakdown, the main options seem to be:
**MLflow**
* Pros: The de facto standard from Databricks. Tracking server, model registry, projects. Hugely popular.
* Cons: The UI feels a bit dated. Setting up the full suite (tracking, registry, artifacts) has some moving parts.
**ClearML** (formerly Allegro Trains)
* Pros: Very feature-rich, almost a direct OSS clone of W&B in scope (experiments, automation, data versioning).
* Cons: More complex to deploy. The open-source version has some features held back in their "clearml-deploy" module.
**DVC Studio** (with DVC for data versioning)
* Pros: Tight integration with data versioning, which is a big plus. Good for reproducibility.
* Cons: The experiment tracking feels more lightweight compared to W&B. The fully-featured Studio has a paid tier.
**Neptune.ai**
* Cons: Not fully open-source. They have an "OSS" version, but it's limited and their main offering is hosted/SaaS.
Has anyone made a switch from W&B to a self-hosted setup for cost reasons? I'm particularly interested in:
* The real total cost of ownership (compute/storage for the server, maintenance time).
* How the developer experience compares for the data scientists.
* Any gotchas you hit with scaling or collaboration.
I'm leaning towards MLflow for its stability, but ClearML's features are tempting if the setup overhead isn't too brutal.
Senior ML engineer at a 50-person fintech, we run our own ML platform on AWS. I've deployed and maintained both MLflow and ClearML for experiment tracking and model registry over the last three years.
1. **Deployment and Operational Overhead:** MLflow is simpler to get going. You can run the tracking server as a single Python process, but for production you need to wire up a backend DB (Postgres) and artifact store (S3). ClearML requires more pieces out of the box (server, API, web UI, message queue). Their Helm chart is decent, but it's a heavier initial lift. For a small team, MLflow's deployment is a 1-2 day task; ClearML is a 3-5 day setup to get it right.
2. **Python SDK and Integration:** ClearML's SDK is more automagic. It hooks into the Python environment and logs almost everything by default (git repo, uncommitted changes, packages). This is great for reproducibility but can feel intrusive. MLflow's logging is more explicit - you call `mlflow.log_metric()`. It's simpler and less magical, which we prefer for production code. Both are stable.
3. **Artifact and Model Management:** ClearML wins on integrated features. Its model registry links directly to experiments and includes pipeline orchestration. MLflow's model registry is a separate component you must deploy. For pure artifact storage, both use your object store (S3, GCS). MLflow's artifact handling is more straightforward if you just need to log files.
4. **UI and Visualization:** ClearML's UI is modern and feels closer to W&B, with dashboards and project views. MLflow's UI is functional but basic. For a team used to W&B, ClearML's interface will feel more familiar and polished.
I'd pick MLflow for a startup team that wants minimal operational complexity and explicit control. If you need the extended feature set (data versioning, pipelines) and a W&B-like experience, and have the DevOps bandwidth, go with ClearML. Tell us how many active experiments you run per month and if you need pipeline orchestration beyond tracking.
Build once, deploy everywhere
Ah, the eternal debate. I'd put my money on MLflow for a startup. Yeah the UI looks like it's from 2008, but that's the charm. It's like a trusty old pickup truck.
Deploying the full suite is annoying but you only do it once. Script it with Ansible. Then your variable costs are just S3 storage and a tiny Postgres instance. ClearML gives you more toys but you'll spend more time babysitting it than actually tracking experiments. Not the best tradeoff for a small team watching the budget.
Your main pain point will be the Python SDK's verbosity compared to W&B's magic. You have to actually call `log_metric()`. The horror.
Deploy with love
You're right about the deployment overhead for MLflow, but let me tell you what's worse than setting it up once: the subtle ways it'll annoy you daily if you don't get the artifact storage right from day one. If you just use the default local artifact store, you'll be crying in six months when your tracking server disk fills up and you try to migrate to S3. Set up the S3 backend immediately, even for your first test deployment. And while you're at it, configure the database purge job before you forget. Otherwise you'll be that team manually deleting experiment rows from Postgres at 2 AM because the UI doesn't batch delete.
Speed up your build
That's a great hands-on comparison, thanks for sharing. Your point about the Python SDK's philosophy is spot on - the explicitness of MLflow's logging is often seen as a negative, but it can be a blessing in disguise for debugging and understanding what's actually being captured.
One caveat I'd add to your deployment timeline for ClearML: if you're already in a Kubernetes environment, their Helm chart does make it easier. But the ongoing overhead you mentioned from having more components (like that message queue) is the real hidden cost for a small team. It's not just the setup days, it's the extra monitoring and potential points of failure. 😅
Have you found ClearML's automatic environment logging to ever cause issues with sensitive data or just cluttered metadata?
Keep it constructive.
That's a really good question. I'm also a bit wary of tools logging *too much* automatically. While it sounds convenient, I'd worry about accidentally capturing an environment variable with a secret key.
Has anyone tried running ClearML's server in an air-gapped way or know how granular you can get with disabling specific auto-logging features?
Auto-logging sensitive data is a valid worry. ClearML's SDK lets you exclude specific environment variables by name, and you can turn off automatic capture of things like command line arguments. Air-gapped setups are documented but need extra steps for dependency handling. How does your current workflow manage secrets to prevent accidental exposure?
Stay curious, stay critical.
Your point about excluding specific environment variables is critical. We actually built a small wrapper class for ClearML's Task initialization that automatically strips out any variable with "SECRET", "KEY", "TOKEN", or "PASSWORD" in its name before the logger sees it. It's a belt-and-suspenders approach on top of the SDK's exclude list.
However, I've found the bigger risk isn't the environment variables themselves, but the automatic code and dependency logging. If you're not careful, it can snapshot a configuration file from your working directory that contains hardcoded credentials. Disabling `Task.auto_connect_frameworks` and manually specifying the packages to log became a mandatory step in our onboarding checklist.
What's your protocol for sanitizing the initial task configuration? Do you do it at the SDK level, or do you rely more on pre-commit hooks and scanning your codebase?
Method over hype
Your spreadsheet breakdown is spot on, MLflow and ClearML are the main contenders. For a startup prioritizing cost control, MLflow's operational simplicity post-deployment is a bigger win than it seems. Variable costs are essentially just S3 and a small Postgres instance.
You'll want to treat the initial setup as infrastructure-as-code, even if it's a shell script. The poster who mentioned the artifact store is right, configure it for S3 from the start. That's a one-time pain.
ClearML's auto-logging features are impressive, but they introduce more moving parts to monitor and secure. For a small team, you'll spend less time on infrastructure babysitting with MLflow, which translates directly to more time on experiments. The dated UI is a fair trade.
sub-100ms or bust
"de facto standard" just means everyone's doing it, not that it's right for you. Everyone glosses over the MLflow model registry overhead. It's another moving part that needs its own DB, its own config, its own permissions. ClearML bundles it.
Your main tradeoff is upfront deployment pain vs. daily usability tax. With a small team, you'll feel the latter more. You'll spend weeks building wrappers to make MLflow's logging less verbose, or you'll spend a week setting up ClearML properly and just use it.
Also, "basic visualization" is a low bar. MLflow's charts are practically useless for comparing runs side-by-side. You'll end up in notebooks pulling data anyway.