Pricing last verified: April 2026. Plans and pricing may change — check the vendor site for current details.
Pricing Overview
Apache Hudi is free and open source under Apache 2.0 — no license cost for the format. Costs come from the surrounding infrastructure: compute (Spark or Flink) to write and query Hudi tables, object storage for the data, and optionally a catalog and managed service. Commercial support is primarily through Onehouse (founded by Hudi's creators) with custom pricing, plus cloud-vendor managed deployments (AWS EMR, Google Cloud Dataproc, Databricks).
For teams running Hudi self-managed, total cost is dominated by Spark or Flink compute — typically 70-85% of Hudi-based spend. Object storage is 10-25%, catalog infrastructure 2-5%. Small teams running Hudi on AWS EMR plus S3 plus AWS Glue Data Catalog can total under $500/month at modest scale; large streaming organizations running petabyte-scale lakehouses spend $50K-$500K+/month driven almost entirely by compute.
Plan Comparison
Hudi has no tiers — you compose costs from underlying components:
| Component | Pricing | Notes |
|---|---|---|
| Hudi format | Free (Apache 2.0) | Core Java library and spec |
| Compute (self-hosted Spark/Flink) | Free software, pay for infrastructure | Dominant cost driver |
| Compute (AWS EMR) | EMR surcharge plus EC2/EKS costs | Native Hudi support |
| Compute (Google Cloud Dataproc) | Dataproc surcharge plus Compute Engine | Native Hudi connector |
| Compute (Databricks) | DBU-based pricing | Supports Hudi but Delta Lake is native |
| Object storage | S3 ($0.023/GB/month) or equivalent on GCS/Azure | Scales with data volume |
| Catalog | AWS Glue ($1/100K requests) or Hive Metastore (self-hosted) | Often shared across lakehouse tables |
| Onehouse managed service | Custom pricing | Dedicated Hudi commercial service |
Hidden Costs and Considerations
Three cost drivers hit teams running Hudi:
- Compaction jobs are essential and expensive — MoR tables accumulate delta logs that must be compacted to Parquet for read performance. Scheduled compaction adds compute cost but skipping it degrades query performance linearly.
- Small-file problem from streaming ingestion — high-throughput writes create many small files. Hudi's clustering and file-sizing features help but require tuning.
- Indexing backend choice affects ongoing cost — Bloom-filter indexing is free but has limits at scale; HBase or record-level indexing improves write performance but adds infrastructure.
Onehouse offers managed Hudi with volume-based pricing. Cloud-vendor managed Hudi (EMR, Dataproc) typically includes a management surcharge of 20-30% over raw compute costs. Enterprise contracts for Onehouse or Databricks' Hudi support are negotiated.
Cost Estimates by Team Size
- Small team (5 engineers, <1 TB active data, light streaming): $200-$500/month. Typically EMR plus S3 plus AWS Glue Data Catalog with modest compute.
- Mid-size team (20 engineers, 10-100 TB, active CDC pipelines): $3,000-$15,000/month. Usually managed Spark or Flink running continuous streaming ingestion with scheduled compaction.
- Large enterprise (100+ engineers, petabyte scale, heavy streaming): $50,000-$500,000+/month. Driven by continuous compute for streaming pipelines plus large object-storage footprints plus potentially Onehouse or Databricks contracts.
Most teams underestimate compaction costs — budget 20-40% of streaming-ingestion compute for compaction jobs. Teams that skip compaction see query performance degrade until they're forced to catch up.
How Apache Hudi Pricing Compares
Hudi's free-format model matches Iceberg and Delta Lake; the cost differences come from ecosystem:
- Apache Iceberg: Also free (Apache 2.0), similar cost structure. Iceberg is often cheaper for analytics-only workloads because compaction overhead is lower; Hudi is often cheaper for streaming-upsert-heavy workloads because it handles them natively.
- Delta Lake: Also free (Apache 2.0), similar cost structure. Delta plus Databricks is more expensive than Hudi plus EMR for equivalent functionality; Delta plus Databricks Unity Catalog is meaningfully better integrated.
- Snowflake: Proprietary, credit-based. Snowpipe plus Snowflake can handle CDC workloads; typically 3-5x more expensive than Hudi plus EMR at scale but meaningfully less operational complexity.
- Google BigQuery: Serverless warehouse with native streaming ingest. Hudi on GCS can be cheaper than BigQuery at large scale; BigQuery wins on operational simplicity.
- Databricks: Supports Hudi but Delta Lake is native. Running Hudi on Databricks is often more expensive than running Delta Lake because you're paying DBU premium without gaining Unity Catalog integration.
The honest summary: Hudi is cheapest for teams that need streaming upserts and have Spark or Flink expertise. For teams wanting managed streaming ingestion without self-managing compute, Snowflake or BigQuery are the path of least resistance. For teams committed to Databricks, Delta Lake is cheaper and better-integrated than Hudi.