Everyone's default answer seems to be "just use W&B" for deep learning. For a team of five, that's a costly assumption. The real question isn't which tool is better in a vacuum, but which one creates the least friction and future risk for your specific setup.
MLflow isn't just some "inferior open source project." Its Tracking and Model Registry components are perfectly adequate for a team of this size, and you host it yourself. The primary trade-offs are:
* **W&B gives you** beautiful, opinionated, out-of-the-box dashboards and superior experiment comparison. The collaboration features are slicker.
* **MLflow gives you** control. No per-user pricing that explodes as you grow. No data leaving your cluster. You can bolt on exactly what you need.
The critical blind spot I see teams ignore is vendor lock-in. With W&B, your entire experiment history is in their cloud. Their pricing model is based on active users and tracked compute. What happens when you're 15 engineers and your bill is $30k/year? Migrating years of experiment metadata is non-trivial.
For a 5-person DL team, you should answer these questions first:
* Where are your models deployed? If it's a cloud provider, hosting MLflow on the same VPC is trivial.
* How much time are you spending on custom visualization vs. needing standard charts? MLflow requires more setup here.
* What's your tolerance for recurring operational costs vs. one-time setup engineering? W&B is a SaaS expense; MLflow is an infra/maintenance expense.
I've seen teams pick W&B for the polish, then scramble to justify its cost later when management asks why the "free" open-source tool wasn't sufficient. Conversely, I've seen teams sink weeks into over-engineering their MLflow deployment.
The popular opinion is that W&B is the obvious winner for DL. I'd argue for a team of five, MLflow is often the more strategic, less risky choice.
Trust but verify.
I'm a lead MLE at a 120-person fintech, managing a 7-person applied research team. We've used both in production: MLflow for our internal R&D tracking and model registry for the last two years, and we ran a 6-month W&B trial for a specific high-velocity hyperparameter search project.
**Core comparison for a 5-engineer DL team:**
1. **Real, predictable cost.** MLflow's cost is the engineering time to host it (we run it on EKS; initial setup took about 5 person-days) and the infra (~$150/month for the tracking server and Postgres). W&B's cost is a line item that scales directly with team size and activity. Their Team tier starts around $50/user/month with a 5-seat minimum, so you're at $3k/year immediately, and that scales linearly as you add engineers.
2. **Integration and control surface.** MLflow is a set of components (Tracking, Models, Registry, Projects) you integrate piecemeal. You'll write more glue code (e.g., wrapping your training loops). W&B is a more monolithic SDK; you `wandb.init()` and get a lot automatically, but customizing the data flow (like logging to a private blob store first) adds complexity.
3. **Where W&B clearly wins.** Experiment comparison and visualization for non-experts. For a team constantly A/B testing architectures, the side-by-side run charts, parallel coordinates plots, and media panels are superior out-of-the-box. Our PMs could use the W&B dashboards directly. With MLflow, we built a custom Streamlit app to get 80% of that functionality.
4. **Where MLflow breaks.** The out-of-the-box UI is functional but clunky for deep dives. The real limitation is artifact storage performance at scale. Using the default local artifact store (e.g., on a pod's volume) is a trap. You must configure it for a scalable backend like S3 immediately, or you'll see significant latency when logging thousands of large model checkpoints.
My pick for a team of five is MLflow, provided you have the DevOps bandwidth to own the initial deployment and are comfortable with cloud storage. The cost control and data sovereignty are decisive at this scale. If your primary constraint is enabling immediate, polished collaboration with non-engineers (like PMs or researchers) and you have the budget, W&B is justifiable. To make the call clean, tell us your cloud provider and whether you have a dedicated platform engineer who can own the MLflow deployment.
brianh