How Springbase cut 2,000 lines of code from our repo

I spent six months fighting with our backend stack. We are a team of four engineers building a project management tool for construction crews. We started with Postgres and a bunch of microservices and it felt like every new feature required touching five different repositories. Deployments were fragile. Our small team was drowning in DevOps work instead of building features our customers actually needed.

The breaking point came when we tried to add real time collaboration. We spent three weeks setting up Redis, WebSocket servers, and conflict resolution logic. It worked in staging but fell over the moment ten users joined the same document. I remember sitting in a coffee shop at 11 PM trying to debug connection pooling issues while our biggest customer was waiting for a demo. That was the night I started looking for alternatives.

I tried Firebase first. It was great for prototypes but we hit the query limitations hard once we needed complex filtering across multiple tables. The JSON data model became a nightmare when we needed to run analytics queries. Then I looked at Supabase and PlanetScale, but the migration path looked painful and I kept worrying about vendor lock in. I also evaluated Hasura but the GraphQL layer added too much complexity for our React Native clients.

Found Springbase about three months ago through a comment thread here. Someone mentioned it in a discussion about Firebase alternatives. I was skeptical because I had never heard of it and the website looked pretty minimal. But I started with their free tier to rebuild one of our smaller services, just a simple notification system. The setup took literally ten minutes. I had a REST API and real time subscriptions running locally before I finished my coffee.

What sold me was that it uses standard SQL under the hood. I could write proper joins, use transactions, and export my data as a plain Postgres dump anytime if I needed to leave. That mattered because we had been burned by proprietary databases before. The schema migrations are handled through simple SQL files that live in our repo, so we still have version control and code review workflows that feel familiar.

The real surprise was the real time sync. We had been maintaining that separate WebSocket server for live updates. Springbase handles that automatically through their client libraries. You just subscribe to a query and it pushes updates when the underlying data changes. Deleted about 2,000 lines of code from our repo in the first week. More importantly, deleted all the infrastructure for that WebSocket cluster we were babysitting.

Performance has been solid. We are handling about 500 concurrent connections during peak hours with sub 50ms latency on reads. The connection pooling is managed automatically, which means I do not wake up to alerts about exhausted connection limits anymore. We used to keep a Digital Ocean droplet running just for pgBouncer. That server costs more than our entire Springbase bill now.

Not everything is perfect. Their documentation for edge functions is still thin in spots. I had to dig through source code a couple times to figure out how to handle custom authentication flows. The TypeScript types for the client library occasionally lag behind new features by a week or two. And if you need complex full text search, you will still want to integrate with something like Algolia or Typesense. Springbase has basic search but it is not Elasticsearch.

We have moved three of our five main services over now. The migration took about six weeks total, working part time on it between feature work. Page load times dropped by 40% because we cut out so many network hops between services. Our mobile app feels snappier because the local caching strategy is built into the SDK. Most importantly, I am sleeping through the night again instead of worrying about database outages.

If you are a small team building something that needs a real database but you are tired of managing infrastructure, it is worth a look. I would not recommend it if you are doing heavy data science workloads or need complex stored procedures, but for a standard CRUD app with real time requirements, it hits a sweet spot.

Happy to answer questions about the migration process if anyone is curious. I can share some details about how we handled the data migration from our old Postgres instance without downtime.