LakeSail is a high-performance data and AI platform powered by a Rust-native compute engine. Run data engineering workloads 8x faster with 94% lower hardware cost than Apache Spark (derived TPC-H). It's Spark-compatible: your existing PySpark and Spark SQL code runs without change. Deploys in your AWS account with BYOC.
LakeSail is a high-performance data and AI platform powered by a Rust-native compute engine. It deploys directly in your AWS account (Bring Your Own Cloud), giving you full control over your data, security, and compliance.
KEY CAPABILITIES:
8x faster at 94% lower hardware costs (derived TPC-H). Get 16x more compute per dollar spent with drastically fewer hardware resources required.
No code rewrite: Sail is a drop-in replacement for Spark SQL and the Spark DataFrame API. Your existing PySpark and Spark SQL code runs unchanged on LakeSail. Plug and play, no migration project.
Native lakehouse support: Read and write Apache Iceberg and Delta Lake tables directly. Works with AWS Glue Catalog out of the box.
BYOC deployment: The Sail engine runs on EC2 instances in your AWS account. Your data stays in your S3 buckets. LakeSail's control plane orchestrates jobs without accessing your data.
Built for data engineering, analytics, and AI and agentic pipelines: ETL, interactive SQL, Python DataFrames, and ML feature engineering on a single engine.
GET STARTED:
After subscribing, you will be redirected to platform.lakesail.com to create or link your LakeSail account and connect your AWS account for BYOC deployment. See the LakeSail documentation for full setup instructions.
LakeSail deploys compute into your AWS account. Additional AWS infrastructure costs (EKS, EC2, S3, networking) are billed by AWS and are separate from your LakeSail Marketplace subscription. Use the AWS Pricing Calculator to estimate infrastructure costs.
Highlights
8x faster with 94% less hardware costs (derived TPC-H). Rust-native engine, no JVM.
Next-gen lakehouse. Native Apache Iceberg and Delta Lake support.
Spark-compatible. Run your existing PySpark and Spark SQL code without any changes. Just plug and play.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
This listing uses a single usage-based dimension. You are billed in units, where each unit equals 1 cent of platform usage. Your total scales directly with how much of the platform you consume, so there is no fixed subscription or tier to choose. The more you use, the more units accrue. This metered model lets your bill track actual consumption. You pay separately for your AWS compute resources, while these units cover the managed platform usage itself.
Top-of-mind questions for buyers
What does one billing unit represent, and how is my usage counted?
Each unit equals 1 cent of platform usage. The platform meters the compute it runs in your AWS account, charging by vCPU-hour and memory GiB-hour. Those metered amounts convert into units on your bill. Usage accrues per second while jobs run and scales to zero between jobs.
What happens to my usage charges when no jobs are running?
The platform scales workers to zero between jobs, so idle time does not accrue platform usage units. Charges apply only during active compute. There is no minimum spend. When jobs finish, compute releases and metering stops until the next job starts.
Does this usage charge cover my AWS compute cost too, or is that separate?
The units cover the managed platform usage only. You pay AWS directly for the EC2 and related infrastructure the platform provisions in your account. The platform fee and your AWS hardware usage both appear on the same AWS bill, so there is nothing separate to reconcile.
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