Spark, without the JVM tax
Powered by Sail, the Rust-native multimodal data engine.
SQL and Python for analytics and AI pipelines.
Bring your Spark SQL and DataFrame code.
# Daily active userschart Example dataevents = spark.table("events")daily = events.groupBy("event_date")users = daily.agg( F.count_distinct("user_id").alias("dau"))
Migrating all your Spark workloads?
The free Spark-to-Sail scanner checks your Spark SQL and DataFrame code against Sail before you move it.
14 of 14 Spark calls run on Sail 0 blockers
- groupBy
- agg
- sum
- pandas_udf
- withColumn
- row_number
- mergeInto
- partitionBy
- orderBy
- table
- col
- getOrCreate
- 700K+ downloads
- Runs in browser or CLI
Two reasons to plug in a better engine.
Run the Spark workloads you already have. Build the AI data pipelines you need next. Sail brings SQL, Python, and multimodal data processing together on a Rust-native engine.
Plug in.
10x faster.
Zero rewrites.
Run Spark SQL and DataFrame code through Spark Connect, with native Delta Lake and Iceberg support. Keep your familiar interface and open lakehouse tables while Sail executes the workload on a Rust-native engine.
Built for AI
from day one,
not bolted on.
Build AI pipelines with familiar Spark SQL and DataFrame APIs. Use Python libraries for multimodal processing, embeddings, model scoring, and robot data for physical AI.
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
- 10x faster, 98% lower infra cost
- One engine for batch pipelines, interactive SQL, and Python data workloads
- No lock-in contracts. No minimum spend. Autoscales to zero
What does this mean for your bill?
Methodology
Shows ~1/2 of the TPC-H benchmark's modeled 98% reduction (49% applied here) as a conservative estimate against JVM-based Spark on r8g.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.
Bring your data
and AI workloads.
Talk to a LakeSail engineer about your pipeline, from Spark analytics to training data and embeddings.