LakeSail
Rust-native Spark replacement for AWS data pipelines and governed AI agent workloads.
About LakeSail
LakeSail is a Rust-native data and AI platform designed as a drop-in replacement for Spark workloads. It implements the Spark Connect protocol so teams can point existing PySpark DataFrame code, Spark SQL queries, and Python UDF workflows at a different endpoint without rewriting pipelines. The engine runs without a JVM and supports open data formats including Apache Iceberg and Delta Lake. For data engineering, it combines Python and SQL jobs, scheduling, dependencies, automatic retries, on-demand provisioning, autoscaling, and connections to orchestration tools such as Airflow and Dagster. Deployments run in the customer's AWS account, where data files, query results, and Glue catalogs remain in customer-owned S3 storage; LakeSail's control plane uses a scoped IAM role to manage LakeSail resources and reads CloudWatch metrics. Agent workloads can use elastic compute, dynamic Python data sources and UDFs, governed execution, and automatic lakehouse branching. Pricing is paid and hardware-cost oriented, while the Enterprise plan uses custom pricing for large deployments. LakeSail fits AWS-based teams replacing managed or self-managed Spark while retaining its APIs and open table formats. Adoption still involves AWS infrastructure, a control-plane relationship, and workload-specific validation because published benchmark results may not match every production workload.
Best for
Best use cases
A team changes its Spark Connect remote endpoint, then runs existing DataFrame operations, Spark SQL queries, and Python UDFs on LakeSail.
A pipeline schedules Python and SQL jobs with dependencies and retries while compute provisions on demand and scales down after completion.
An agent creates runtime Python tools and works in an isolated lakehouse branch so experiments do not directly modify production data.
Compare before choosing
LakeSail retains Spark Connect APIs while replacing JVM execution with a Rust-native engine, reducing migration work for compatible workloads.
Unlike platforms that store data in a vendor environment, LakeSail runs in the customer AWS account and keeps data files in customer-owned S3.
Compared with long-lived cluster workflows, LakeSail provisions compute per job and scales workers down, but production results remain workload dependent.
Features
Replace Spark compute
Retain Spark Connect workflows while moving PySpark, Spark SQL, Delta Lake, and Iceberg execution to a Rust-native engine.
Operate data pipelines
Schedule Python and SQL jobs with dependencies, retries, on-demand compute, autoscaling, and orchestration integrations.
Execute governed agent workloads
Run agent Python and SQL tasks with dynamic tools, elastic compute, audit controls, and isolated lakehouse branches.
Pros
- Spark Connect compatibility can reduce migration work because existing PySpark, SQL, and Python UDF pipelines keep the same interface.
- Running in the customer AWS account keeps data files and query results in customer-owned S3 and supports existing residency controls.
- On-demand provisioning, autoscaling, scheduling, dependencies, and retries combine compute operation with day-to-day pipeline workflow.
Cons
- Deployment depends on AWS infrastructure, including EKS, S3, VPC configuration, IAM access, and CloudWatch metrics for the managed workflow.
- Enterprise pricing is custom, so large teams need a sales conversation before they can establish a complete budget.
- Published speed and compute-cost comparisons are benchmark based, so gains can differ across production data, queries, and operating conditions.
Pricing Plans
Enterprise
- Dedicated support with SLA
- Enterprise SSO (SAML, OIDC) and RBAC
- Custom licensing models
- On-premises deployment available
* Pricing information is for reference only. Please refer to the latest announcement on LakeSail's official website.