When we switched our production workloads to Lakebase, it honestly felt like a weight lifted off our shoulders. Before, we were juggling multiple systems just to keep data flowing between analytics and operations. Now, everything lives in one place, and that’s been a game-changer.
One of the coolest things we’ve built is stateful AI agents that can actually remember context while working with lakehouse data. Instead of patching together external databases, Lakebase gives us persistent memory right where we need it.
We also started serving real-time features directly to our ML models without spinning up a separate online store. That shaved off a ton of complexity and made our models more reliable.
Another big win was cutting out reverse ETL. Keeping operational and analytical data together means fewer pipelines, less latency, and way fewer headaches.
And the branching feature? Total lifesaver. We can test database changes safely, ship faster, and roll back if needed without drama.
Overall, Lakebase hasn’t just helped us hit our goals — it’s changed how we think about building data products. We’re moving faster, breaking less, and spending more time on the fun stuff instead of plumbing.
