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A practical guide to cost optimization with Lakebase Postgres

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  • Lakebase is cost-efficient by design because its separated storage and compute architecture lets branching, read replicas, and high availability share one storage layer, while serverless autoscaling and scale-to-zero mean you pay only for the compute you actually use.
  • The biggest practical savings come from syncing only the working subset of Lakehouse data into Lakebase, matching your sync mode (Snapshot, Triggered, or Continuous) to how fresh the data truly needs to be, and right-sizing compute so your hot working set fits in cache.
  • Applying these practices, syncing just the working set, matching sync mode to freshness needs, right-sizing compute so hot data fits in cache, and tuning PITR and snapshots, keeps costs predictable and low without giving up the performance, availability, and developer experience your applications need.

    • Lakebase is a fully managed Postgres database, built for the operational realities of modern application development. What sets it apart from other database vendors in the market also offering a Postgres engine is the architecture underneath, separated storage and compute with a serverless compute layer, and its tight integration with the Lakehouse and the data intelligence platform. You can read more about this architecture and some of the benefits here. A benefit that often flies under the radar, however, is that this architecture also makes Lakebase highly cost efficient. In this blog, we’ll break down where those cost efficiencies come from and share practical tips for getting the most out of them.

    How Lakebase is cost-efficient by design

    Avoid duplicate storage costs with branching

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