Top Amazon Redshift Alternatives in 2026
- If you want data engineering, ML, and BI on one governed lakehouse instead of gluing Redshift to SageMaker and Glue, choose Databricks. Unity Catalog and Mosaic AI unify SQL, Spark data engineering, and model training on one copy of data, and the platform runs on AWS, Azure, and GCP instead of locking you to one cloud.
- If you want to stay in a SQL-warehouse model but drop AWS-only lock-in and get compute and storage that scale independently, choose Snowflake. Snowflake runs natively on AWS, Azure, and GCP, auto-suspends idle warehouses, and supports Apache Iceberg tables natively.
- If your usage is light or spiky and you want the simplest possible serverless bill, choose Google BigQuery. On-demand pricing charges only for data scanned, the first 1 TiB each month is free, and there is no compute floor like the 4-RPU minimum on Redshift Serverless.
- If your data already lives as Apache Iceberg tables and you want to query it without loading it into a warehouse first, choose Dremio. Dremio queries Iceberg tables in place and its Community Edition is free forever to self-host, though it now operates under SAP after the July 2026 acquisition closed.
- If you're already deep in AWS and rely on tight integration with S3, Glue, QuickSight, and SageMaker, choose stay on Amazon Redshift. No alternative here matches Redshift's native AWS integration or Spectrum's ability to query S3 data directly at a fixed per-terabyte rate, and Reserved Instances give real discounts for steady workloads.
Amazon Redshift is AWS's original cloud data warehouse, still wired tightly into S3, Glue, QuickSight, and the rest of AWS. It has no flat price. You pay per RPU-hour or per node-hour for compute, plus separate charges for managed storage, Spectrum scans, and Concurrency Scaling. There is no standing free tier, only a 90-day Serverless credit or a short provisioned trial.
Teams look elsewhere for three main reasons: they want a warehouse that runs outside AWS, they want one platform that also covers heavy data engineering or ML instead of bolting Redshift to SageMaker, or they just want a simpler bill. The four alternatives below split into two groups. Snowflake and Google BigQuery are the closest like-for-like SQL warehouse swaps. Databricks pushes further into lakehouse territory than Redshift does. Dremio sits in between, an Iceberg-native lakehouse with a genuinely free self-hosted tier. None of them escape usage-based billing entirely. Leaving Redshift's RPU-hour model does not automatically buy a flat, predictable bill anywhere else.
Amazon Redshift alternatives compared
| Tool | Best for | Starting price | Free option | Last update |
|---|---|---|---|---|
| DatabricksBest for unified data engineering and ML | Data engineering and data science teams running large-scale Spark pipelines and ML training on the same data | Free tier + custom | Yes | July 2026 |
| SnowflakeBest for enterprise governance | Teams that need to run SQL analytics, data engineering, and AI workloads on one governed copy of data instead of stitching together separate systems | Custom / quote | Trial (30 days with $400 in free credits) | June 2026 |
| Google BigQueryBest for serverless simplicity | Teams already on Google Cloud or using Google Analytics 4, Google Ads, and other Google data sources that need a warehouse with native connectors | $6.25/TiB scanned | Yes | July 2026 |
| DremioBest free option | Teams that already store data as Apache Iceberg tables and want to query it without moving it into a warehouse | $0.2/DCU | Yes | July 2026 |
Why teams switch from Amazon Redshift
No standing free tier
First-time users get a 90-day, $300 Serverless credit or a two-month provisioned trial capped at 750 hours a month. After either expires, standard on-demand billing starts automatically.
Pricing is split across too many separate meters
Compute (node-hour or RPU-hour), managed storage (per GB-month), Spectrum scans ($5/TB), and Concurrency Scaling are all billed separately, which makes total cost harder to estimate than single-metric competitors.
AWS-only, with no multi-cloud option
Redshift does not run on GCP or Azure, unlike Snowflake or Databricks, which limits it for teams pursuing a multi-cloud strategy.
Serverless has an effective cost floor
The 4-RPU minimum means even light, intermittent workloads cost roughly $1.50/hour of active compute, before any actual query usage.
The best Amazon Redshift alternatives, ranked
Databricks is the biggest structural jump from Redshift on this list. Instead of a SQL warehouse, it is a lakehouse built on Apache Spark and Delta Lake, where data engineering, SQL analytics, and machine learning run against one governed copy of data through Unity Catalog. It runs on AWS, Azure, and GCP, so teams that outgrow AWS-only Redshift get real multi-cloud reach. Pricing is usage-based like Redshift, billed per Databricks Unit consumed per second on top of separate cloud compute costs, but there is no published rate table at all, not even Redshift's example node-hour prices. Third-party spend data puts the median Databricks buyer near $300,000 a year. Teams whose workload is mostly SQL dashboards, not Spark pipelines or model training, will find Databricks a bigger and pricier platform than they need.
Pros
- + One platform for data engineering, SQL analytics, and ML/AI, so you avoid separate warehouse-plus-ML-platform sprawl
- + Open formats, Delta Lake and Unity Catalog, cut lock-in compared with proprietary warehouse storage formats
- + Runs natively on AWS, Azure, and GCP, which helps multi-cloud or cloud-migrating organizations
Cons
- – No public price list. Total cost depends on DBU rates that vary by cloud, region, and workload, and you need a calculator or a sales call to estimate it
- – Usage-based DBU billing, plus separate cloud infrastructure cost, makes budgeting and cost governance harder than flat per-seat pricing
Snowflake is the most direct SQL-warehouse swap for Redshift. Like Redshift, it separates compute and storage, but Snowflake's multi-cluster warehouses auto-suspend to zero when idle, and it runs on AWS, Azure, or GCP rather than AWS alone. Snowpark extends SQL into Python, Java, and Scala, and Cortex adds managed LLM functions, covering some of the ground Redshift covers by bolting on SageMaker. Snowflake also has native support for Apache Iceberg tables, which Redshift does not offer directly. Neither vendor publishes a flat price. Snowflake's per-credit compute rate varies by edition, cloud, and region, and higher-security editions like Business Critical need a sales call, similar to how Redshift's Reserved Instance pricing is also negotiated. New accounts get a 30-day trial with $400 in credits, a clearer offer than Redshift's regional patchwork of trial terms.
Pros
- + Compute and storage scale and bill independently, so idle warehouses can auto-suspend to stop charges
- + Native support for Apache Iceberg tables lets teams query open-format data without duplicating it into proprietary storage
- + Snowpark and Cortex let SQL, Python, and AI/LLM workloads run against the same data and governance model instead of separate platforms
Cons
- – No published flat price. You have to use the consumption calculator or get a sales quote to estimate real monthly cost
- – Per-credit compute rates differ by edition, cloud provider, and region, which makes it harder to compare quotes
Google BigQuery is the closest thing to Redshift Serverless taken to its logical end: no clusters, no node types, ever. Google handles all scaling, and you pay $6.25 per TiB scanned on demand, with the first 1 TiB each month free, or buy dedicated slot capacity through Editions starting at $0.04/slot-hour. That on-demand floor beats Redshift Serverless's 4-RPU minimum, which costs roughly $1.50/hour even for light, intermittent workloads. BigQuery ML lets you train models with plain SQL, similar to how Redshift users lean on SageMaker, and native connectors for Google Analytics 4 and Google Ads matter for marketing-heavy teams. The tradeoff is ecosystem: BigQuery is built for GCP the way Redshift is built for AWS, so teams that want to leave single-cloud lock-in behind gain little by moving from one to the other.
Pros
- + A true serverless model: no cluster sizing, node types, or manual scaling decisions
- + On-demand pricing means light or bursty workloads can run for very little, and the first 1 TiB of query data per month is free
- + Deep native integration with Google Analytics 4, Google Ads, Firebase, and other Google Cloud and Workspace data sources via BigQuery Data Transfer Service
Cons
- – On-demand billing is per-query and scan-based, so an unindexed or unfiltered query on a large table can generate a large, surprising charge
- – Editions and slot pricing (Standard, Enterprise, Enterprise Plus) add a second, more complex pricing dimension once teams outgrow on-demand pricing
Dremio takes a narrower approach than a full warehouse replacement. It queries Apache Iceberg tables directly, using a caching layer called Reflections to speed up repeat queries, instead of asking you to load data in first the way Redshift does. That fits teams that already store data as Iceberg and want to skip a load step. Community Edition is free forever and self-hosted, a real advantage over Redshift's time-limited trial credits, though it skips governance and cataloging features. Dremio Cloud bills $0.20 per Dremio Compute Unit, comparable in spirit to Redshift's per-RPU-hour billing. The open question is ownership: SAP completed its acquisition of Dremio on July 6, 2026, so pricing, roadmap, and support terms could shift as it is absorbed into SAP's data and AI lineup. Teams evaluating Dremio should weigh that uncertainty alongside its technical fit.
Pros
- + Community Edition is free with no time limit and no row or data caps for self-hosting
- + Queries Iceberg tables directly, so you skip a separate ETL or copy step into a warehouse
- + Reflections (materialized, auto-maintained accelerations) can cut repeat-query latency a lot without manual tuning
Cons
- – Enterprise pricing is quote-only, so it's hard to budget or compare against fixed-price competitors upfront
- – Community Edition leaves out governance and cataloging features, pushing serious deployments toward paid tiers
Amazon Redshift alternatives: FAQ
What is the best free alternative to Amazon Redshift?+
Dremio's Community Edition is free forever and self-hosted, with no time limit. That beats Redshift's time-limited trial credits for teams that want to try a real workload before paying.
Which Redshift alternative is cheapest for light or unpredictable usage?+
Google BigQuery's on-demand pricing charges only for data scanned, with the first 1 TiB per month free and no minimum floor to keep running. That beats Redshift Serverless's 4-RPU minimum, which costs roughly $1.50/hour even during light activity.
Is there a Redshift alternative that runs outside AWS?+
Yes. Snowflake and Databricks both run natively on AWS, Azure, and GCP. Dremio is more limited: Dremio Cloud, the fully managed option, currently runs on AWS only, with Azure support coming soon and no GCP support announced. Only the self-managed Dremio Enterprise edition can be deployed on any cloud, or on-prem, via Kubernetes. Google BigQuery runs only on Google Cloud, so it does not solve AWS lock-in, it just swaps one single-cloud vendor for another.
Which alternative best replaces Redshift Spectrum's ability to query S3 data directly?+
Dremio queries Apache Iceberg tables in S3 directly without loading them first. Databricks and Snowflake can also reach external data through Iceberg support, but none of the four publish a flat per-terabyte rate like Redshift Spectrum's $5/TB.
Amazon Redshift alternatives: pricing compared
Entry price, billing model, and whether pricing is public. 3 of 5 publish pricing you can check without talking to sales.
| Tool | Starting price | Billing | Free option | Pricing disclosed |
|---|---|---|---|---|
| Amazon Redshift | $0.375/RPU-hour | usage-based | Trial ($300 credit (90-day expiration) for first-time Redshift Serverless users; in regions without Serverless, a two-month free trial for provisioned clusters (up to 750 hours/month)) | Partly public |
| Databricks | Free tier + custom | usage-based | Yes | Not disclosed |
| Snowflake | Custom / quote | usage-based | Trial (30 days with $400 in free credits) | Not disclosed |
| Google BigQuery | $6.25/TiB scanned | usage-based | Yes | Public |
| Dremio | $0.2/DCU | usage-based | Yes | Partly public |
How we made these picks. We compare tools on public pricing, features, and hands-on assessment, then verify every price against the vendor's own page. We never accept payment for rankings. Read the full methodology.