Presto alternatives

3 tools to consider instead of Presto, shown against it.

Presto Trino Starburst Apache Spark
Vendor Presto Foundation (Linux Foundation) Trino Software Foundation Starburst Data, Inc. Apache Software Foundation
Pricing model Open source + paid options Open source + paid options Usage-based Open source + paid options
Free tier Yes Yes Yes Yes
Deployment Self-hosted Self-hosted Cloud, Self-hosted Self-hosted
Open source Yes (Apache-2.0) Yes (Apache-2.0) No Yes (Apache-2.0)
Best for Organizations running large-scale interactive SQL over Hadoop/lake storage who want an Apache-governed alternative to Trino. Teams needing a single SQL query layer across data already spread across multiple systems. Enterprises needing governed, federated SQL access across many existing data systems with vendor support. Data engineering teams building large-scale batch ETL, streaming or machine-learning pipelines.
Pricing

Free and open source under the Presto Foundation; no single vendor sells a dedicated managed Presto service.

Pricing has not been verified yet — see the vendor's site.

Free and open source; commercial managed and enterprise-supported distributions are sold separately by Starburst.

Pricing has not been verified yet — see the vendor's site.

Free tier for small workloads; paid tiers bill by compute credit consumption at increasing per-credit rates as support and governance features expand.

Free $0
Pro From $0.50/credit
Enterprise From $0.75/credit
Mission-Critical From $1.00/credit

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Free and open source under the Apache Software Foundation; managed lakehouse platforms built on Spark, such as Databricks, are priced separately.

Pricing has not been verified yet — see the vendor's site.

Features
  • Federated SQL queries across Hive, object storage and databases
  • Coordinator/worker distributed execution
  • Pluggable connector architecture
  • ANSI SQL support
  • Cost-based optimizer
  • Used as the engine behind Amazon Athena
  • Federated SQL queries across heterogeneous data sources
  • Pluggable connector architecture (Iceberg, Hive, Kafka, JDBC sources)
  • Massively parallel, in-memory distributed execution
  • ANSI SQL compatibility
  • Cost-based query optimizer
  • Fine-grained access control via connectors
  • Managed (Galaxy) and self-hosted (Enterprise) Trino distributions
  • Federated queries across lakes, warehouses and databases
  • Fine-grained access control and data catalog
  • Autoscaling cluster management
  • Query result caching
  • AI-assisted query and governance tooling (AIDA)
  • Unified batch, SQL, streaming and ML APIs
  • In-memory distributed execution (RDDs/DataFrames)
  • Structured Streaming for near-real-time pipelines
  • MLlib for distributed machine learning
  • Runs on Kubernetes, YARN or standalone
  • Broad connector ecosystem for storage and lakehouse formats

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