Spark, without the JVM tax
Run your Spark workloads on a better engine.
Zero rewrites, same interface, same code.
Plus a runtime your AI agents can actually drive.
Two reasons to plug in a better engine.
Most data platforms were built for the JVM era and are now retrofitting for AI. LakeSail rebuilt the runtime in Rust, kept the full Spark API, and shipped an agent layer from day one. Your code stays the same. The engine gets an upgrade.
Plug in.
8x faster.
Zero rewrites.
Spark Connect protocol means your existing PySpark, Spark SQL, Delta Lake, and Iceberg code runs unchanged, natively, not as an add-on. One line of config to swap the engine. Same developer experience, same interface, dramatically faster and lower cost.
Built for AI
from day one,
not bolted on.
While others retrofit AI agents onto JVM platforms, LakeSail ships an MCP server, dynamic Python tooling, and lakehouse branching. Agents get a substrate where every action is sandboxed, observable, and reversible.
The same code.
A different decade.
Apache Spark won the 2010s. The JVM is now the bottleneck. LakeSail keeps the API, replaces the runtime.
JVM-based Spark today
Where most data teams are stuck
- JVM startup, GC pauses, constant tuning
- Cluster management overhead and idle costs
- Python workloads pay the JVM serialization tax
- AI agents bolted on after the fact
- Vendor lock-in via proprietary formats
- Slow jobs and oversized clusters inflate infra cost
- Stitching together separate engines for batch, stream, SQL, & AI
- Lock-in contracts, minimum spend, and idle capacity
LakeSail / Sail
What changes when the engine is Rust
- No JVM tax: instant startup, no GC, no memory tuning
- Stateless Rust runtime that scales to zero
- Native Python at engine speed without the JVM overhead
- Agent-first: MCP server, branching, lineage
- Open formats: Iceberg, Delta Lake, Spark Connect, DataFusion
- 8x faster, 94% lower infra cost
- One engine for batch, stream, ad hoc SQL, and AI agents
- No lock-in contracts. No minimum spend. Autoscales to zero
What does this mean for your bill?
Plug in your current Spark or Databricks compute spend. See your new run rate on LakeSail.
Methodology
Shows ~1/2 of the TPC-H benchmark's 94% reduction (47% applied here) as a conservative estimate against JVM-based Spark on c6a.4xlarge. Engineering hours valued at $150 fully loaded. Your actual savings depend on workload mix and current cluster utilization.
LakeSail embodies the best next generation lakehouse architecture, combining native performance with managed ease of use. A compelling platform for data intensive applications.
Your Spark workloads.
A better engine.
Get a 30-minute demo and a benchmark of LakeSail against your existing Spark workloads.