Solutions
Cost down. Performance up.Agentic AI ready.
LakeOps automates compaction, snapshot hygiene, and routing while you keep your existing tables, catalogs, and engines. Reduce spend, improve latency, and keep table health visible in one control plane—then enable agent-ready data and multi-engine routing when your team is ready.
Popular use cases
Managed Iceberg
Autonomous compaction, snapshots, manifests, and orphan cleanup for every table.
Explore Managed IcebergCost optimization
Eliminate small files, orphans, and over-provisioned compute automatically.
Explore cost savingsPerformance optimization
Adaptive data layout, lean manifests, and optimized file sizes for 12× faster reads.
Explore performance impactMulti-engine routing
Route queries across Trino, Spark, Snowflake, and more — optimized per workload.
Explore routingLakehouse observability
Table health, engine metrics, and cross-system telemetry from one control plane.
Explore observabilityAgentic AI readiness
Agent-native MCP interface, guardrails, and a self-optimizing lake for AI workloads.
Explore AI enablementResults
Measured impact on
real Iceberg workloads
Benchmarks from production-grade tables across multiple engines and cloud providers.
Compaction speed
vs. Apache Spark on identical datasets
Query performance
After compaction + layout optimization
Cost savings
In compute & storage spend
What your data platform gains
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
LakeOps analyzes real query patterns and table telemetry to decide what to compact, when, and how aggressively. A Rust-based engine runs compaction up to 20× faster than Spark with a fraction of the compute, so you stop paying for idle clusters and over-provisioned jobs. Orphan files, expired snapshots, and bloated manifests are cleaned continuously—storage costs drop without anyone writing a script.
Avg. acceleration across engines
12.4×
Incredible query performance
LakeOps continuously reshapes data layout based on actual query patterns—sort order, file sizes, and partition structure all adapt to how your tables are really used. Engines scan less data, planners prune faster, and manifests stay lean. The result: up to 12× faster queries across Trino, Spark, and Snowflake without changing a single query or pipeline.
Automated table optimization
Set policies once—LakeOps handles the rest. It monitors every table for small files, stale snapshots, orphan data, and metadata bloat, then runs the right operation at the right time: bin-pack compaction, sort-order optimization, snapshot expiration, manifest rewrites, and orphan cleanup. All orchestrated safely in the background with conflict-aware scheduling across concurrent writers.
One view, all catalogs
Visibility and control
See every table's health in one place: small-file counts, partition skew, snapshot depth, storage cost by engine, and optimization history. Drill into any table for file-size distributions, record counts over time, and partition-level metrics. Set alerts on drift, trigger one-click remediation, or let policies handle it—full observability with guardrails instead of guesswork.
Agentic AI readiness
AI agents need fast, well-organized data and safe SQL access. LakeOps exposes an agent-native MCP interface so agents can query, discover, and act on your lake directly. Layered guardrails limit what agents can touch, intelligent routing sends agent queries to the right engine at the right cost, and the lake self-optimizes in a closed loop—learning from every query to keep data agent-ready.
Query router
Cost, latency & table-aware routing
Adaptive + LLM + semantic routers · cached decisions
Multi-engine query routing
One policy layer routes queries across Trino, Spark, Flink, Athena, Snowflake, and Databricks. LakeOps picks the engine per query using cost ceilings, latency targets, table health, and historical patterns—interactive dashboards hit low-latency engines while heavy ETL and compaction land where compute is cheapest. No duplicate configs, no per-engine tuning.
Minutes to value with no risk
Connect & collect telemetry
Manual or autonomous management
Operations run & optimize
Observability & governance
Runs on your stack
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.
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.”
Production benchmarks
5.5 TB across 10 production tables
Real workloads. Real data. Batch, streaming, delete-heavy, multi-writer, and terabyte-scale tables — all on the same engine, same hardware.
| Table | Size | Workload | Files (B → A) | Throughput | Time | Notes |
|---|---|---|---|---|---|---|
| balance_snapshots | 1,192 GB | TB-Scale batch | 11,957 → 3,270 | 1,572 MB/s | 11 min | Spark OOM on same hardware |
| user_accounts | 174 GB | Batch | 878 → 400 | 2,269 MB/s | 74s | Single Node |
| events_analytics | 484 GB | Delete-Heavy | 16,128 → 7,198 | 729 MB/s | 11m 21s | 23,433 delete files; 551M rows removed |
| raw_sdk_events | 8 GB | Streaming | 42,633 → 69 | 167 MB/s | 138s | 99.8% file reduction |
| site_traffic | 292 GB | Multi-Writer | 2,740 → 754 | 1,465 MB/s | 3m 25s | Single partition |
| cluster_registry | 322 GB | Batch | 998 → 440 | 2,522 MB/s | 2m | Peak throughput |
Compaction cost per TB
Normalized to Spark = 100%
Source: 200 GB (~1 TB uncompressed) benchmark. Spark cost index 100 vs LakeOps 10.
Self-improving: same table, zero config changes
balance_snapshots — 1.192 TB across consecutive runs
Same data and hardware; planner learns workload telemetry and improves runtime from 22 to 11 minutes.