Partners/LakeSail
Compute Partner

LakeSail

Rust-native compute for data and AI agents

LakeSail reimagines the compute layer with Sail — a Rust-native, Spark-compatible distributed engine that delivers up to 8× Spark throughput with 94% lower cost, built for both data engineering and production AI agents.

Throughput
Runtime
Rust
Cost Savings
~94%
Reference Architecture

Realtime compute path

Live
01LaserData
LaserData streams
Ordered event delivery feeds compute and transformation workloads continuously.
02LakeSail
Sail distributed compute
Rust-native, Spark-compatible execution runs without JVM overhead.
03Consumers
Jobs, outputs, agents
Data products and AI workloads consume fresh results with lower cost and latency.
Performance
Faster than Apache Spark on average
Runtime
Rust
Zero JVM overhead, native speed
Compatibility
Spark API
Drop-in, no rewrites required
Cost Savings
~94%
Lower infrastructure cost vs Spark
About LakeSail

Compute reimagined in Rust

LakeSail is building the next generation of distributed compute on top of Sail — an open-source, Rust-native engine that is fully Spark-compatible. Teams drop it in as a replacement for Apache Spark without rewriting a single line of PySpark or SQL, and immediately benefit from orders-of-magnitude lower latency and cost.

Where traditional Spark deployments carry the JVM's garbage collector and object model overhead into every shuffle and aggregation, Sail eliminates that entire layer. The result is predictable sub-millisecond scheduling, tighter p99 bounds, and a fraction of the memory footprint at equivalent throughput.

LakeSail deploys in your own AWS account — BYOC with your IAM roles and encryption keys — so data sovereignty is built in from day one, not bolted on as a compliance checkbox.

Core Engine

Sail — Rust-native, Spark-compatible

An open-source distributed compute engine that speaks the Spark Connect API. Existing workloads migrate without rewrites. Performance improvements are immediate and measurable.

Agentic Layer

Governed execution for AI agents

Agents can submit queries, Python workloads, and custom UDFs at runtime. Lakehouse branching gives agents isolated sandboxes to explore and validate without touching production data.

Deployment Model

BYOC — your cloud, your keys

Runs inside your AWS account. Your IAM roles, your encryption keys, your VPC. Full data sovereignty with zero ops overhead on the infrastructure layer.

Integration

Why LaserData + LakeSail

Streaming at the edge, Rust-native compute in the middle — a coherent, low-latency stack for data engineering and AI agents.

LaserData as the Real-time Input

LakeSail's Sail engine consumes live event streams from LaserData, enabling continuous data engineering workloads that react to new data as it arrives — not hours later.

Rust End to End

LaserData's io_uring-powered streaming core and LakeSail's Rust-native Sail engine share a common philosophy — no GC pauses, no JVM overhead, predictable tail latencies from ingestion through compute.

Agentic Workload Execution

AI agents running on LaserData streams can delegate compute-heavy tasks — transformation, enrichment, validation — to LakeSail's agentic execution layer, which provisions elastic Rust-native compute per workload.

Open Source at the Core

LakeSail is built on Sail — an open-source, Apache-licensed distributed compute engine on GitHub. LaserData is built on Apache Iggy. Both teams build in public, contribute upstream, and give customers full portability.

Joint Stack

Stream to compute, end to end

01
Event Stream
LaserData
02
Real-time Delivery
LaserData
03
Distributed Compute
LakeSail
04
Agent Execution
LakeSail

LaserData delivers ordered event streams into LakeSail's Rust-native compute layer — enabling continuous, low-latency data engineering and agentic workloads with no JVM in the path.

Get started

Build the compute layer

Talk to us about running LaserData streams through LakeSail's Rust-native engine in your production environment.