Solutions
Make a real impact
on your business
Cut cost, speed up queries, automate maintenance, gain full visibility, get AI-ready data, and run across engines and clouds—from a single control plane.
Outcomes that matter for your business
One control plane to optimize, observe, and automate your lake—consistently across engines, clouds, and workloads.
$1,374,672
Saved in last 3 months
-76%
Resources saved in last 3 months
Up to 80% cost reduction
We compact data smarter using telemetry and usage patterns—so the right data gets optimized at the right time. A Rust-based engine runs compaction up to 20× faster with less IO, CPU, and storage across batch, streaming, and interactive workloads. Fewer small files, leaner manifests, and automatic snapshot hygiene cut cost without manual cleanup.
Avg. acceleration across engines
12.4×
Incredible query performance
We reshape tables to match how they are actually queried, so engines read less data for every request. LakeOps keeps file sizes in the sweet spot, partitions aligned with access patterns, and manifests compact so planners can prune quickly and skip cold data. The result is consistently faster queries across engines without application changes.
Automated table optimization
Run maintenance manually when you need it—or let LakeOps fly on autopilot. It automatically identifies tables that need work, chooses the right operation, and orchestrates compaction, snapshot expiration, and cleanup safely in the background.
One view, all catalogs
Visibility and control
LakeOps turns table health into a live, queryable surface: small-file hotspots, skewed partitions, snapshot bloat, and cost impact by engine. From a single view you can see what needs attention, drill into the worst offenders, and trigger or tune optimization with guardrails instead of manual guesswork.
AI readiness
Feature stores, training pipelines, and real-time inference depend on predictable tables. LakeOps keeps data layouts stable, freshness SLAs visible, and history preserved so AI and ML workloads see consistent, high-signal data—not noisy, fragmented tables that erode model quality.
LakeOps
One control plane on top of your stack
Engines
Snowflake
Databricks
Trino
Apache Flink
Spark
StarRocks
Amazon Athena
Dremio
DuckDBCatalogs & lake
AWS Glue
Apache Polaris
Apache Gravitino
Project NessieStorage
AWS
Azure
Google CloudCross-system operability
One control plane spans engines, catalogs, and clouds, so you don’t rebuild maintenance and observability for each stack. Policies are defined once and enforced everywhere, with engine- and cloud-specific details handled by LakeOps—freeing you from vendor-specific scripts and one-off ops tooling.
See it in action
Turn these outcomes on for your lake
Get a walkthrough of how LakeOps applies policies, runs optimization, and surfaces impact across engines—on your own tables.
What platform teams say
Trusted by data platform engineers
“LakeOps took the pain out of compaction and maintenance. We went from ad-hoc scripts and firefighting to a single control plane. Query performance improved and our platform team finally has visibility across the lake.”
Full Iceberg benefits. Snowflake-level ease.
Get a personalized walkthrough of the control plane and your data lake.