Autonomous management
for your data lake
Cut costs and accelerate queries with autonomous management across your data and engines—with built-in agentic AI support.
Runs on your stack
Platform
Cost down. Performance up.
Agentic AI ready.
End-to-end optimization for every table op, across storage and engines. Telemetry-driven smart orchestration with full visibility and control.
LakeOpsLast 30 days Optimization Activity
Key Metrics
Recent Operations
Last 10 operations| Operation | Table | Duration | Impact | Time | Status |
|---|---|---|---|---|---|
| Compact Data Files | customer_orders orders | 4s | 1.24 TB, 16 → 1 files | 57 minutes ago | SUCCESS |
| Expire Snapshots | payment_transactions payments | 27s | 8.2 TB | 4 hours ago | SUCCESS |
| Expire Snapshots | inventory_snapshots_20250702 warehouse | 3s | 2.1 TB | 4 hours ago | SUCCESS |
Faster and Smarter Compaction
20x faster Rust-based compaction engine that organizes data by real query usage — so every compaction cycle directly cuts IO and speeds up reads.
Explore compactionAutonomous Table Management
Snapshot retention, manifest rewrites, and orphan file cleanup — automated, scheduled, and safe across every table.
Explore managementMulti-Engine Query Routing
Route queries across Trino, Spark, Snowflake, and more — optimized for cost, latency, or throughput.
Explore routingAgentic AI Enablement
Optimized metadata and table structure for AI agents, feature stores, and autonomous pipelines.
Explore AI enablementFull-Stack Observability
Monitor engines, query latency, throughput, and error rates. Cross-system telemetry from one place.
Explore observabilityOrganization-Wide Policies
Define and enforce maintenance policies across catalogs. Auditable, versioned, one toggle.
Explore governanceMinutes to value with zero risk
Connect in ~10 minutes
Connect your catalog and storage in ~10 minutes. No agents, data movement, or pipeline changes.
AI analyzes & simulates
LakeOps continuously models table health from metadata, query patterns, and cost signals.
Automated optimization
Compaction, manifest cleanup, snapshot hygiene, and layout tuning run continuously on autopilot.
Visibility & governance
Unified dashboards track cost, performance, and table health. Every action is logged and reversible.
Why LakeOps
Tech freedom. Optimized results.Autonomous management.
From cost and performance to AI readiness — one platform that covers every dimension of lake operations.
Managed Iceberg
Autonomous compaction, snapshots, manifests, and orphan cleanup for every table.
Explore Managed IcebergAgentic AI readiness
Agent-native MCP interface, guardrails, and a self-optimizing lake for AI workloads.
Explore AI enablementCost reduction
Eliminate small files, orphans, and over-provisioned compute automatically.
Explore cost optimizationQuery performance
Adaptive data layout, lean manifests, and optimized file sizes for faster reads.
Explore performance impactMulti-engine routing
Route queries across Trino, Spark, Snowflake, and more — optimized per workload.
Explore routingFull observability
Table health, engine metrics, and cross-system telemetry from one control plane.
Explore observabilityScale with LakeOps Enterprise
SOC 2, SSO, RBAC, scale, and dedicated support—for the largest Iceberg lakes.
Security & compliance
SOC 2 Type II, encryption in transit and at rest, SSO/RBAC, and audit trails. Built for regulated teams with strict security standards.
Scale & control
One control plane for your full lake footprint. Gain real-time visibility, policy control, and predictable performance as workloads grow.
Support & training
Dedicated onboarding, expert training, and enterprise SLAs. Deploy in your VPC or on-prem with a success team that stays hands-on.
Loved by data platform teams
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.
We evaluated several options for Iceberg operations. LakeOps stood out for its focus on automation and multi-engine support. Deployment was straightforward and the impact on cost and latency was measurable within weeks.
Our tables were suffering from small files and fragmented metadata. LakeOps runs continuously in the background—we set policies once and the system handles the rest. Maintenance automation that actually works.
Get in touch
See LakeOps on your stack
Short call. Your architecture. We'll show how the control plane fits Snowflake, Databricks, Trino, and your lake—no vendor lock-in.
No commitment · Typically 30 min