Been there! I love W&B for experiment tracking, but for a recent smaller project, the overhead felt like too much. I just needed clean logging, basic metric comparisons, and a simple dashboard—without the external service dependency or the full suite of features.
So I built a super lightweight alternative over the weekend using **Plotly** for visuals and **SQLite** for storage. It's not a replacement for complex workflows, but perfect for when you want something self-contained and transparent. Here's the gist of my approach:
**Core Components:**
* **SQLite Database:** One simple table stores run_id, metric name, value, step, and timestamp.
* **Logger Class:** A Python class that handles connecting to the DB and inserting metric data. It mimics the simple `wandb.log()` feel.
* **Plotly Dashboard:** A separate script queries the SQLite DB and generates interactive Plotly figures (line charts for metrics, bar charts for comparing final values).
**Why I like this setup:**
* **Total control & portability:** Everything lives in local files. Perfect for internal tools or quick prototypes.
* **Zero cost & no vendor lock-in:** It's just SQLite and Plotly.
* **Easy to extend:** Need to log hyperparameters? Just add another column and table. Want to automate reports? Hook it into a cron job.
**Trade-offs to consider:**
* No built-in collaboration features (everyone needs DB access).
* No automatic artifact storage or model versioning.
* You're on the hook for your own dashboard hosting if you share it.
If you're working on a personal project, a small team prototype, or just want to understand exactly how your metrics are stored and served, rolling your own can be a fantastic learning experience. It really demystifies what's happening under the hood of the bigger platforms.
Would anyone be interested in me sharing the core code snippets? Happy to post them if it's helpful!
Cheers,
Anna
Keep it simple.
I run a 200-person data science consultancy, and we've deployed everything from lightweight trackers to full W&B Enterprise for client projects.
If you're weighing a custom SQLite/Plotly solution against WandB, here are four concrete dimensions from my experience:
1. **Implementation and maintenance cost.** W&B's hosted tier starts at $0 for individual users, but team seats run $50-120/user/month depending on features. Your solution's cost is developer time, which can easily reach 5Iny-10 days annually for bug fixes, dependency updates, and feature requests from teammates.
2. **Team collaboration overhead.** W&B centralizes access, dashboards, and model artifacts out of the box. With a custom solution, sharing results requires distributing SQLite files or building a simple server, adding roughly 2-3 weeks of engineering effort for a basic multi-user setup.
3. **Experiment scalability.** Your SQLite setup works well for maybe a few hundred runs locally. We hit performance issues with SQLite around 10,000+ metric entries on shared network drives, where W&B's backend handles millions of runs without our team managing infrastructure.
4. **Debugging and audit capability.** W&B provides automatic system metrics, diff tracking, and guaranteed artifact lineage. In a custom tracker, you'll need to manually add logging for environment details, code state, and dependencies, which teams often overlook until a reproducibility crisis happens.
For a solo prototype or a tightly scoped internal tool with a single primary user, your lightweight approach is a great fit. If you're already feeling collaboration pains or need to guarantee reproducibility for audits, I'd recommend W&B. To make a clearer choice, tell us how many people need access and whether this project has external compliance requirements.
Stay factual, stay helpful.
You've accurately framed the trade-off as a cost analysis, which is the right lens. The "5-10 days annually" maintenance estimate for a custom solution is often low, however. That assumes a static feature set. In my tracking, once a tool like this proves useful, feature creep begins. Requests for model artifact storage, hyperparameter logging, or permission tiers can easily double that annual upkeep, making the ongoing SaaS cost look much more efficient for teams above a certain size.
Your point on **experiment scalability** is particularly critical for production use. SQLite's file locking and lack of concurrent write capabilities become a major constraint even before hitting data volume limits, causing conflicts in collaborative environments. That's a hidden cost that's hard to quantify until a sprint is blocked by a database lock.
Totally get the appeal of that setup for solo projects and quick prototypes. I've done something similar with SQLite and FastAPI for a basic internal dashboard.
But I hit a snag when I tried to scale it. The lack of concurrent writes in SQLite becomes a problem even with just 2-3 team members running experiments simultaneously. We ended up with database locks and corrupted files, which sort of defeated the purpose of having a shared tracking system.
Have you considered using something like DuckDB for the storage layer instead? It handles concurrent reads much better while keeping that single-file simplicity.
✌️
That's a really practical point about the scaling bottleneck. File locking with SQLite is a real issue when you move from solo to team use, even with just a few people, as you found.
DuckDB is a clever suggestion for a more performant single-file alternative. The challenge I've seen with both in a collaborative setting, though, is moving past the single file itself. You still need a way to host and serve that shared file reliably, which starts to pull you back toward building a simple server component. It's a great step up, but the architecture discussion tends to shift from the database layer to the access layer pretty quickly.
That's a solid architecture for the exact use case you described - a single developer on a focused prototype. The portability and transparency are huge benefits there.
My only immediate caveat would be on your **"easy to extend"** point. It's true for adding another plot type, but tread carefully with schema changes. If you decide to log hyperparameters or artifacts later, altering that single table while preserving historical data can get messy. I usually recommend starting with separate, linked tables (e.g., `runs`, `metrics`, `params`) from day one, even if it feels like overkill. It prevents a painful migration later when you realize you need to query all runs where `learning_rate > 0.001`.
For the dashboard script, are you handling incremental updates? I've found a simple `WHERE timestamp > last_queried` pattern avoids re-rendering the entire history every refresh.
Mike