Alright, I’ll admit it — I’ve been a bit of a W&B fangirl for years. There’s something magical about having all your team’s experiments in one dashboard, especially when you’re trying to untangle why model B suddenly outperformed model A. But here’s the rub: our little 5-person ML team has been scaling up our experimentation (and our AWS bill), and the default W&B cloud storage just isn’t cutting it anymore. We need to own our data, keep it secure in our own S3 buckets, and avoid any surprise vendor lock-in or egress fees.
So, I’ve been on a deep dive for the past few weeks, testing every configuration and alternative I could find. My core criteria were:
- **Lightweight:** Should feel snappy, not like we’re booting up a separate platform.
- **S3 Native:** Artifacts, logs, models — everything must sync seamlessly to our own buckets.
- **Team-Friendly:** Easy for a 5-engineer team to collaborate, compare runs, and not step on each’s toes.
- **Pragmatic Pricing:** We’re happy to pay for value, but it needs to scale predictably with our usage.
Here’s what I found from really putting W&B through its paces with an S3-first mindset:
**The Good:**
- The `wandb.init()` API is still, in my opinion, the gold standard for simplicity. Adding it to a training script takes minutes.
- When you configure the backend to use S3 for artifact storage (setting `WANDB_ARTIFACT_DIR=s3://our-bucket/path`), it works flawlessly. The files go where we want them.
- The experiment comparison tables are incredibly useful for our weekly review syncs. Being able to filter by tags and metrics saves us hours.
**The Gotchas & Configuration Headaches:**
- While artifacts can live in S3, the *metadata* (run info, configs, metrics) still goes to W&B’s servers unless you self-host the entire W&B server (a significant infrastructure lift for a team of 5).
- The “lightweight” feel starts to fade if you have thousands of runs. The web UI can get sluggish, which is ironic since our data is in S3.
- Permissions and IAM roles for S3 access required some fine-tuning. We had to ensure our training instances (and our local machines for that matter) had the right bucket policies. This wasn’t a W&B-specific issue, but it added setup friction.
**The Real Question for This Community:**
Has anyone else gone down this path of using W&B primarily as a front-end with S3 as the storage backbone? Did you eventually consider a more fully self-hosted or S3-native alternative like MLflow (with its S3 backend) or even something more minimalist like Aim?
I’m particularly curious about:
* How you handled the metadata storage dilemma.
* Whether the collaboration features remained smooth with this hybrid setup.
* If the pricing model still made sense once you were storing all the heavy data yourselves.
I love W&B’s user experience, but I’m wondering if we’re trying to fit a square peg in a round hole. Maybe there’s a hidden gem out there that’s built from the ground up for teams like ours. Let’s share some real-world workflow reports!
—Aurora
don't spam bro
Hey there. I'm the one who usually ends up managing our internal tools at a ~10-person fintech startup. We run our own ML pipelines for fraud detection and I've set up our experiment tracking twice now, first with W&B and then by moving off it.
**S3 Sync & Control:** W+B's S3 sync is functional but feels like a bolt-on. You set the bucket, but the internal naming and folder structure is still WandB's. You own the bits, but it's not a clean S3-native experience. Using a self-hosted W+B server was more control but a real ops lift.
**Pricing Reality:** Their per-user seat model is straightforward, but the hidden cost is in the stored artifacts. When we ramped up hyperparameter sweeps, the cloud storage costs ballooned. Moving to our S3 helped, but the platform fee remained. For a team of 5, you're looking at $600/year per seat, so about $3k/year before any overages.
**Lightweight Feel:** The `wandb.init()` API is indeed simple, but the client library is heavy. It pulled in a lot of dependencies that occasionally conflicted with our other packages. The web UI is slick, but it *does* feel like booting up a separate platform, especially when the dashboard lags on 1000+ runs.
**Migration Effort:** Getting data *out* of W&B into another system was the real headache. The API is fine for active runs, but bulk-exporting our historical runs and artifacts to a flat structure for a custom solution took me a solid week of scripting. That's the lock-in they don't talk about.
I'd actually recommend you look at **MLflow** for your case. It's a bit more DIY on the UI, but you can run the tracking server in a container and point it directly at your S3 bucket for everything. It feels truly lightweight because you only take the parts you need. The catch is you'll need to host the UI component yourselves, but for 5 engineers, it's manageable.
If you go with MLflow, just be prepared to spend a day or two setting up the infra and access controls. Let me know if your runs are mostly local or if you're already on Kubernetes, as that changes the deployment advice.