Why we replace Databricks.
Lakehouse was the right idea. Eisberg is the next iteration — open formats, GPU compute, agent-native APIs, and governance baked in instead of bolted on.
Spark-era complexity wrapped in a UI
Databricks did Spark a service. The platform still carries Spark's mental model — clusters, jobs, notebooks. Eisberg routes per query, scales pools to zero, and presents the platform as APIs first, not a notebook.
Delta is proprietary, even when it claims to be open
Iceberg is the open standard. Apple, Netflix, and the rest of the industry shipped Iceberg for a reason. Eisberg is Iceberg-only — by intention, not by tolerance.
Notebook-first, not agent-first
Databricks bolted on Genie and a chat layer. Eisberg was designed for the world where agents are the primary users — sub-100ms APIs, MCP-native, per-action metering, identity-bound agent governance.
Compliance is a service line, not a primitive
Databricks sells you Unity Catalog. Eisberg ships compliance modules — BCBS 239, SR 11-7, HIPAA, 21 CFR Part 11 — as code, with audit packs that generate themselves on demand.
The honest head-to-head.
| Capability | Eisberg | Snowflake | Databricks |
|---|---|---|---|
| GPU-native query engine | |||
| Open format (Iceberg) by default | |||
| Customer-owned object storage | |||
| Sub-100ms agent API targets | |||
| MCP-protocol native | |||
| Per-action agent metering | |||
| Policy-as-code governance at every layer | |||
| Compliance modules (BCBS 239 / SR 11-7 / HIPAA) | |||
| Autonomous data classification | |||
| Pipelines that resolve their own failures | |||
| Platform that gets smarter every quarter | |||
| Cost ceiling via outcome pricing |
Bring us your Databricks workload.
We will translate your jobs, your tables, and your Unity Catalog policies, then run the same workload on Eisberg. You will see the cost, the latency, and the governance side-by-side.