Overview

Built on Trino and hosted in the cloud, Galaxy combines data federation with ingestion and lakehouse maintenance. It runs on AWS, Google Cloud, and Microsoft Azure, and is available through web, Linux, and API interfaces. Organizations short on the resources to manage complex data clusters are a natural fit. Teams that need to control their own deployment or have only a narrow query workload may find the managed service broader than they need.

Key features

Galaxy can query data lakes and warehouses together in a single SQL query, avoiding copies made solely to bring sources together for analysis. Starburst names more than 50 connectors, including Amazon S3, Snowflake, Redshift, BigQuery, and Azure Blob. That breadth matters when data is split across those systems; it is less of a reason to choose Galaxy when a team works from one source.

For data loading, Galaxy streams from Kafka and batch-ingests CSV, JSON, and AVRO into Iceberg. LakeOps automates Iceberg compaction, snapshot expiry, and orphan-file removal on a dedicated serverless engine. Together, these functions cover both getting data into a lakehouse and routine table upkeep, though ingestion compute is billable.

AIDA turns natural-language questions into governed insights through a customizable interface or MCP. On Enterprise and Mission-Critical, AIDA token usage is billed separately, so teams planning to use it should include that charge in their cost assessment.

Security controls include fine-grained RBAC and ABAC, end-to-end encryption, AI-powered data classification, and private connectivity. Starburst describes Galaxy as SOC 2 Type II certified and HIPAA-ready. Users can contact Starburst support professionals within the product UI. The REST API uses TLS/HTTPS, and its authentication tokens expire after 10 minutes—a practical consideration for integrations that must handle token renewal.

Pricing

Galaxy uses usage billing, with rates determined by plan. Documented billable usage includes cluster compute hours, data-ingestion compute, and private-link data transfer, so the starting credit rate alone does not determine a workload’s bill.

Free: 0.00 USD per free, billed Free forever. It allows up to three clusters and standard execution mode for ad hoc queries. This is a bounded way to evaluate the service, but teams needing other execution modes or ongoing production capacity should compare paid plans.
Pro: 0.50 USD per contact, billed Starting at $0.50/credit. It adds flexible cluster execution modes, streaming ingest, and advanced cluster management. It fits teams that need those operating options without the listed Enterprise governance and private-connectivity features.
Enterprise: 0.75 USD per contact, billed Starting at $0.75/credit. Advanced autoscaling, ABAC and SCIM, and AWS PrivateLink for data sources target teams with stronger governance or connectivity requirements. AIDA token usage is billed separately.
Mission-Critical: 1.00 USD per contact, billed Starting at $1.00/credit. It adds elite support, advanced governance integrations, and the highest uptime guarantees. It is aimed at teams for whom those service and governance assurances justify the higher starting rate; AIDA token usage is also separate.

A 30-day free trial includes Enterprise features and $500 in Galaxy compute resources. It offers a more capable evaluation than the free tier, but the trial’s duration and compute allocation make it an evaluation window rather than a long-term operating plan.

Platforms

Galaxy supports API, Linux, and web access, and is available on AWS, Google Cloud, and Microsoft Azure. Its open table-format support, SQL analytics, ACID transactions, streaming ingestion, and governance catalog make it relevant to teams building around lakehouse data as well as federated queries.

Who it's for

Galaxy is strongest for organizations that need SQL access across multiple data lakes and warehouses, want to ingest into Iceberg, and would rather not operate complex clusters themselves. The combination of managed federation and Iceberg maintenance can consolidate work that would otherwise span separate query and upkeep tasks. It is a weaker fit for teams requiring self-hosted deployment or predictable costs independent of usage.

Pros and cons

Pros: More than 50 named data sources and single-query federation help teams analyze distributed data without copying it first.
Pros: Kafka streaming, batch ingestion, and automated Iceberg maintenance cover data landing and recurring table upkeep.
Pros: The trial includes Enterprise features and $500 in compute resources, giving teams a defined period to evaluate advanced capabilities.
Cons: Cluster compute, ingestion compute, and private-link transfer can all be billable, so actual spend depends on usage as well as the plan’s starting rate.
Cons: The free tier is capped at three clusters and standard execution for ad hoc queries, limiting its usefulness for teams that need flexible execution.
Cons: Separate AIDA token charges on the two upper plans add another usage cost to account for.

Alternatives

For a broader comparison, browse Data Lakehouse Platforms, Data Virtualization Software, Data Federation Software, and Query Engine Software.

Oracle Autonomous AI Lakehouse is worth comparing for teams seeking a free autonomous lakehouse option with capacity limits.
Starburst Data Platform is the related Starburst platform to consider if its broader deployment options, including self-hosted, better match the stack.
Amazon SageMaker Autopilot is an option for teams seeking a paid service with pay-as-you-go pricing and no minimum fees or upfront commitments.
Bauplan may suit teams that prefer a shared sandbox with public datasets or their own uploads and a full CLI, SDK, and API.
IOMETE is an alternative for teams prioritizing self-hosted, on-premises deployment and community support on its free plan.
LavaLake is another freemium option with a free single-machine plan capped at 16 vCPU.
Cloudera Data Lake Service is a paid alternative with custom pricing.
Hapi Data Lakehouse is another data lakehouse alternative.

Verdict

Choose Starburst Galaxy if your team needs to query data spread across cloud sources and wants managed Iceberg ingestion and maintenance without operating complex clusters. Its combination of federation and lakehouse operations is the central reason to choose it. Look elsewhere if self-hosting is essential or if usage-based compute, ingestion, transfer, and AI charges make spend too difficult to manage.

	Ontop	Starburst Galaxy
Free plan	No	Yes
Free trial	No	Yes
Paid from	—	Free
Open source	No	No
Platforms	api, Linux, macOS, self-hosted, Web, Windows	api, Linux, Web
Free plan	—	Yes

Ontop vs Starburst Galaxy

Ontop

Starburst Galaxy