Apache Iceberg18 min read
Apache Iceberg Cost Optimization in 2026
Your Iceberg lake is overcharging you from four directions at once — storage bloat, query compute waste, compaction overhead, and engineering time.
Cost Optimization
Cut CPU & storage costs
by 80%
Small files, snapshot bloat, orphan data, and over-provisioned compute silently inflate your cloud bill. LakeOps eliminates each source of waste autonomously — so every query scans less data, costs less CPU, and your lake stays optimized without pipeline changes.
80%CPU & Storage saving
86%faster compaction
51%less data scanned
12×faster queries
Results
Measured impact on
real Iceberg workloads
Benchmarks from production-grade tables across multiple engines and cloud providers.
Compaction speed
95%faster
vs. Apache Spark on identical datasets
Spark
LakeOps
+ Sort
Query performance
12×faster
After compaction + layout optimization
Cost savings
80%reduction
In compute & storage spend
Table health
100%healthy
Autonomous maintenance keeps every table optimized
TPC-DS benchmark suiteProduction Iceberg tablesMulti-cloud, multi-engine
How we cut costs
Intelligent optimization,
autonomously managed.
Not a faster compaction job — a system-level optimization loop. Event-driven triggers, query-aware layouts, coordinated maintenance, a Rust engine, and safe simulation work together to cut total lake cost up to 80%.
Compaction Duration86% faster
6300sS3 Tables
1612sApache Spark
221sLakeOps
780sLakeOps (Sort)
95% faster with RustQuery-aware compactionCoordinated with all opsSuper cost-efficient
Intelligent compaction
Smarter compaction that cuts cloud bills
LakeOps triggers compaction based on real table state, sorts data using actual query patterns, and executes on a Rust engine at a fraction of Spark's cost.
- 95% faster Rust engine — no JVM, no GC, $5/TB vs $50/TB
- Query-aware: sorts data by how it's actually queried, per table
- Coordinated with all maintenance ops — clean input, tight output
Maintenance PipelineSequenced
1120 TB
Snapshot Expiration
Trim stale data, release files
2200 TB
Orphan Cleanup
Remove dereferenced files
381% fewer files
Compaction
Rewrite the clean, current dataset
4263 MB
Manifest Optimization
Consolidate metadata against final layout
Annual savings
$216K+
Monthly recovery
$18K/mo
Coordinated maintenance
Clean first, compact second — no wasted rewrites
LakeOps sequences every operation: expire snapshots → remove orphans → compact → optimize manifests. Compaction always runs on the pruned dataset, never on files about to be deleted.
- 350 TB → 230 TB in 10 min — $33K/yr storage + compute savings from one run
- 200 TB orphan data removed across 324 tables at near-zero compute cost
- Each step feeds the next — no redundant rewrites, no wasted CPU
Query Acceleration12× faster
Files before
47,000
Files after
280
Query before
52s
Query after
5.8s
Scan volume reduced 51%
Sorted layouts enable predicate pushdown across all engines
Query compute
Every query scans less data, opens fewer files, costs less CPU
LakeOps reduces query compute from multiple angles — smarter data layout, fewer files, cleaner metadata — so every read across every engine is cheaper.
- Query-aware sort: data sorted by your real filter columns — 51% less scanned
- Fewer, larger files: less S3 API overhead, better predicate pushdown
- Delete files and manifests compacted — no read-time reconciliation cost
- Puffin column statistics enable engines to skip row groups aggressively
Query EnginesCost-aware routing
AWS AthenaCost/query: $0.052.3s
128 queriesactive
TrinoCost/query: $0.031.8s
256 queriesactive
DuckDBCost/query: $0.010.5s
64 queriesactive
StarRocksCost/query: $0.021.2s
32 queriesactive
SnowflakeCost/query: $0.082.1s
192 queriesactive
+ Add engine
Workload cost reduction
up to 56%
Multi-engine routing
Route each query to the cheapest engine that fits
Each engine bills differently — CPU-seconds vs bytes scanned. Without a routing layer, every query hits the default engine regardless of fit. LakeOps routes by cost model, latency target, and workload shape across all connected engines.
- Up to 56% workload cost reduction — right engine per query shape
- One endpoint, SQL dialect translation, per-group concurrency limits
- Per-agent, per-user, per-pipeline cost attribution across engines
Layout SimulationBranch-based
1Create Iceberg branchDone
2Apply proposed layoutDone
3Replay production queriesDone
4Compare cost/performanceDone
Results
12 TB · 1,124 files
Predicted savings
$84K/yr
Safe to commit
97%
Simulation & safety
Test layout changes before committing to production
Changing a sort order rewrites every file. LakeOps tests changes on an Iceberg branch first — applies the layout, replays production queries, compares cost. The branch is discarded. No production data touched.
- Branch-based simulations — zero production impact
- Predicted vs actual production queries for each table
- Full audit trail — every action logged and reversible
The problem
Where Iceberg costs
silently grow
Data lakes grow by tables, not by vertical capacity. Without active maintenance, entropy compounds — files fragment, metadata bloats, and every query pays an invisible tax.
Slow queries from poor data layout
Fragmented files, suboptimal sort orders, and accumulated delete files force queries to scan more data than necessary — increasing latency and compute cost on every read.
Storage bloat from dead data
Expired snapshots, orphan files, and unreferenced data accumulate over time. Without active cleanup, storage costs grow continuously while none of that data serves a single query.
Expensive compaction compute
Traditional compaction engines carry heavy runtime overhead — JVM startup, garbage collection, over-provisioned clusters. The compute cost of maintaining tables often rivals the cost of querying them.
Manual maintenance overhead
Compaction, snapshot expiry, and orphan cleanup require custom scripts, monitoring, and on-call support. As the lake grows, maintenance toil scales linearly while team capacity stays flat.
Runs on your stack
Learn more
Apache Iceberg15 min read
Optimizing Iceberg Lake Compaction: A Guide
Compaction is the most impactful operation in an Apache Iceberg lakehouse — and the hardest to get right at scale.
Apache Iceberg24 min read
Managed Iceberg in 2026: Autonomous Data Lake
Iceberg tables degrade silently — small files pile up, snapshots bloat metadata, and query latency creeps higher. The nine components every production data lake needs.
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.
101K → 19K
files (81% reduction)
2,522 MB/s
peak throughput
99.8%
max file reduction
551M
deleted rows cleaned
| 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%
Apache Spark100%
AWS S3 Tables / Databricks100%
LakeOps10%
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
Run 122 min · 925 MB/s
Run 218 min · 1,100 MB/s
Run 3 (learned)11 min · 1,572 MB/s
Same data and hardware; planner learns workload telemetry and improves runtime from 22 to 11 minutes.
Agentic AI readiness
Ready for agentic AI,
built for cost-efficient scale
Optimized tables let AI agents run more tasks with less query compute and more predictable spend — cost savings compound as agent workloads scale.
Lower token-to-query cost
Faster, cleaner tables mean agents execute fewer expensive retries and complete tasks with less compute.
Agent-safe optimization loop
LakeOps simulates and validates changes before promotion, so autonomous workflows can scale without surprise regressions.
Scale AI workloads confidently
As agent query volume grows, adaptive compaction and routing keep query latency and infrastructure spend predictable.
Super high ROI
LakeOps pays for itself.
No credits, no surprises.
LakeOps continuously trims storage and compute waste so savings keep pace with and typically exceed what you pay for the platform. Pricing stays straightforward: a management fee plus per-TB usage, with no credit bundles, guesswork, or surprise overages.
Super-high ROI from day 1
Avg. 60–80% costs saved
Always save more than you pay
Flat TB-based pricing
No credits complexity
Full visibility and control
Minutes to value with no risk
1
Connect & collect telemetry
2
Manual or autonomous management
Manual
Autonomous
3
Operations run & optimize
Compaction
Snapshots
Orphan cleanup
Manifests & metadata
4
Observability & governance
Metrics
Health
Agents
Routing
Logs
Policies
No vendor lock-in
No code / infra changes
No data changes
Set up in 10 minutes · Works with your existing stack
See the impact
See your projected savings
Connect your catalog and get a free cost analysis in 10 minutes — see exactly where your Iceberg lake is overspending and how much LakeOps can save.
LakeOps optimizes data layout, eliminates orphan files, expires stale snapshots, and rewrites manifests — so every query scans less data, opens fewer files, and costs less CPU across every engine.
