Databricks on GCP vs AWS

Databricks on Google Cloud launched into public preview in March 2021 and is the newest of the three Databricks clouds. Databricks on AWS is the original deployment (2014) and the most battle-tested. Both run the same platform, but the cloud underneath shapes cost, reliability, and what your team ends up maintaining. This page compares them honestly.

Where GCP shines

• BigQuery interop. If your organization is already invested in BigQuery, Databricks on GCP reads and writes it natively, and the two platforms share GCS as common storage. Nothing on AWS matches that pairing as directly.
• Google's AI stack. Vertex AI and access to Google's Gemini models are first-class citizens of the GCP ecosystem. If your AI roadmap is anchored on Google's models, running Databricks next to them keeps data and model training in one estate.
• Sustainable pricing. GCS storage pricing is competitive with S3, and GCP's committed-use discounts are straightforward. For organizations standardized on GCP billing, consolidating Databricks there keeps procurement simple.
• Per-second billing and custom machine types. Compute Engine's per-second billing and the ability to define custom VM shapes are genuinely nice for tuning cluster cost against workload profiles.

Where GCP gets harder in production

These are the friction points that show up once real workloads are running. None are deal-breakers, but each is something your platform team will own.

GCS as the Delta Lake storage layer

GCS has strong consistency and atomic object writes, which is actually a point in its favor for Delta Lake: unlike the historical S3 story, Delta on GCS needs no external locking provider for concurrent writes. The friction shows up at the ecosystem edges, not the core guarantees:

• The connector tax. Outside Databricks' managed runtime, Spark and every other engine must talk to GCS through the gcs-connector Hadoop library. Misconfigure it and you get the classic ClassNotFoundException: com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem error. The delta.io GCP guide leads with this error because it is what every new user hits first.
• Authentication surface area. delta-rs users have hit a string of GCS-specific credential bugs: delta-rs #1978 ("Unsupported ApplicationCredentials type: service_account") and #2192 (Invalid RSA key on ARM64) are representative. GCS auth has more modes (service account JSON, workload identity, OAuth) and more ways to misconfigure them than S3's access-key model.
• The S3-interoperability trap. GCS offers an S3-compatible XML API, and teams routinely try to point S3-native tooling at storage.googleapis.com rather than adopt the native connector. It half-works: delta-rs #2394 (commit error 411 Length Required) and #4303 (multi-file writes fail in S3 compatibility mode) document commits failing exactly at the transaction log boundary, which is the worst place for a partial failure. If you take one thing from this page: use the native GCS path, not the S3 shim.
• Fewer eyes on the GCS path. The delta-rs concurrency test suite historically only exercised S3 and Azure; delta-rs #780 (enable concurrency test for GCS integration) existed precisely because GCS coverage lagged. GCS-tagged bugs like #4433 (corruption in checkpoint parquet files) follow the same pattern as every non-primary storage backend: your platform team ends up closer to the compatibility test suite than the S3 crowd.

Platform and ecosystem gaps

• Fewer regions. Databricks on GCP is available in 15 regions; Databricks on AWS spans well over 20, including GovCloud and China. If you need a region GCP does not serve, the decision is already made.
• Feature lag. Databricks maintains separate limited regional availability tables for GCP, and serverless features (notebooks, SQL warehouses, Databricks Apps, serverless workspaces) are supported in only a subset of GCP regions, with several regions missing Databricks Apps entirely. New capabilities still reach AWS first.
• Smaller instance menu. AWS offers the industry's widest selection of compute profiles, including Graviton (ARM) instances that delivered up to 3x better price performance for Databricks at launch. GCP has no ARM option on Databricks, and some GCP regions do not support GPU instance types at all.
• Spot mechanics are weaker. Both clouds offer deeply discounted preemptible capacity, but GCP gives a 30-second preemption notice versus AWS's 2-minute warning, and legacy GCP Preemptible VMs terminate after 24 hours maximum regardless of demand. AWS Spot has no runtime cap, and Databricks Fleet Clusters (AWS-only) hunt for capacity across instance types and availability zones automatically.

Side by side

The verdict

Choose Databricks on GCP when your data gravity, AI roadmap, or procurement is already anchored on Google Cloud: the BigQuery and Vertex AI pairing is real, and GCS's native consistency makes the Delta core solid. The costs are a smaller region footprint, feature lag, and a GCS storage path that demands more care outside the managed runtime.

Choose Databricks on AWS when you want the most mature platform with the widest options: more regions, faster feature availability, ARM and GPU compute economics, stronger Spot tooling, and an S3 storage layer that every engine in the ecosystem treats as the reference implementation.

If you are weighing the two, our Azure-to-AWS migration service covers the same cross-cloud assessment and execution discipline, and we run GCP-to-AWS evaluations on the same model. For the storage-layer view, see our Delta Lake on S3 vs OneLake comparison.