Amazon Redshift Alternatives: BigQuery, Snowflake and Databricks Cost Compared

Most teams searching for an Amazon Redshift alternative are not unhappy with Redshift. They are unhappy with the invoice. That distinction matters, because three of the four best answers below involve a migration that costs more in engineering time than it saves in licence fees, and the fourth involves no migration at all. Every rate here was verified in August 2026 against the vendors' own published sources.

The four options, priced

The honest problem with any warehouse comparison is that the compute units do not convert. A Redshift node hour, a BigQuery scanned tebibyte, a Snowflake credit and a Databricks DBU measure four different things, and anyone who lines them up in a single column is inventing a number. Storage does convert, so that is where a comparison can actually be trusted.

Read the storage column first, because it is the only one where the comparison is like for like. BigQuery is the cheapest place to keep a terabyte, and the gap widens for cold data: tables untouched for 90 days drop automatically to long term storage at roughly $14.90 per TB per month, which is 38 percent below Redshift Managed Storage. If you are keeping seven years of history for a compliance requirement and querying almost none of it, that single line can justify the move on its own.

The option nobody prices: stay on Redshift and change node family

This is the finding that should come before any migration business case. In May 2026 AWS launched RG, a Graviton based node family, and priced it at exactly 70 percent of the RA3 rate for the same vCPU count. Not "up to 30 percent". Exactly. Pull the AWS Price List API and the ratios come out to five decimal places: rg.large at $0.3801 against ra3.large at $0.543, rg.xlarge at $0.7602 against ra3.xlplus at $1.086, rg.12xlarge at $9.128 against ra3.16xlarge at $13.04. All three land on 0.70000.

Two consequences follow, and neither is widely written up. The first is that RG's on-demand price is identical to what a one-year No Upfront RA3 reservation costs, to the cent, on every matched size. The new generation hands you the commitment discount without the commitment, so anyone about to sign a twelve month RA3 reservation is buying something they can now have for free. The second is that RG includes a built-in data lake query engine, so the $5.00 per TB Redshift Spectrum scan charge does not apply. A cluster scanning 20 TB of Parquet a month saves another $100 on top of the 30 percent.

AWS states RG maintains feature parity with RA3 and that existing clusters can move using snapshot and restore, elastic resize or classic resize. Compared against a cross-platform migration, that is a weekend of work. The full rate card, the reservation ladder and the serverless break-even arithmetic are laid out in our Amazon Redshift pricing breakdown.

When BigQuery is the right alternative

BigQuery wins on shape of usage rather than on unit price. Redshift provisioned clusters bill by the hour whether or not anyone is querying, so a warehouse used hard from 9am to 6pm and idle overnight is paying for roughly 15 hours a day of nothing. BigQuery on demand bills only the bytes your queries scan, with the first TiB each month free, so the overnight silence is genuinely free.

The arithmetic that decides it is scan volume. At $6.25 per TiB, BigQuery costs about $5.68 per decimal TB scanned. A two node rg.large cluster costs $554.95 a month, which buys you roughly 98 TB of BigQuery scanning before BigQuery becomes the more expensive option. If your team scans well under 98 TB a month, BigQuery is cheaper and requires no capacity planning at all. If it scans multiples of that, the fixed cluster wins and keeps winning. Teams that outgrow on-demand can move to capacity pricing at $0.04 per slot hour, which is covered in our BigQuery pricing breakdown.

The catch worth naming: BigQuery's billing model punishes undisciplined SQL in a way Redshift's does not. A SELECT * against a wide table costs real money every time somebody runs it, and there is no cluster ceiling to stop it. Partitioning, clustering and a maximum bytes billed setting are not optimizations on BigQuery, they are cost controls.

When Snowflake is the right alternative

Snowflake's argument against Redshift has never really been price, it is portability and predictability. Redshift only runs on AWS. Snowflake runs on AWS, Azure and Google Cloud, which matters if your company is mid-negotiation with a cloud provider or has a second cloud for regulatory reasons.

On cost, Snowflake credits are all-in, meaning the price includes the infrastructure, and tier upgrades are a clean multiplier: Standard to Enterprise is exactly 1.5x and Standard to Business Critical exactly 2x, applied uniformly to every credit. That makes the bill genuinely forecastable, which is worth real money to a data team that has to defend a number to finance. Warehouses auto-suspend by default, so the idle time problem that afflicts Redshift provisioned clusters is handled for you.

Where it loses to Redshift is the always-on case. A warehouse running continuously burns credits continuously, and at $3.00 per Enterprise credit that adds up faster than a reserved RG node at 62.5 percent off. Full detail in our Snowflake pricing breakdown.

When Databricks is the right alternative

Databricks is the right answer to a different question. If the reason you are unhappy with Redshift is that your team also runs Spark jobs, trains models, and maintains pipelines in three other tools, then consolidating onto a lakehouse can remove more cost than the warehouse line item ever represented.

The pricing trap to know about first: Databricks classic SKUs exclude the cloud instances underneath them, and serverless SKUs include them. So SQL Classic at $0.22 per DBU and SQL Serverless at $0.70 are not comparable numbers, and neither is comparable to a Redshift node hour without adding your EC2 bill back in. Data stays in your own S3 bucket, which means Databricks charges nothing for storage and AWS charges you directly at roughly $23.00 per TB. Our Databricks pricing breakdown works through the DBU rate card and the compliance add-on arithmetic.

The migration cost nobody puts in the business case

Every comparison above is a rate card, and rate cards are the cheap part. Moving off Redshift means rewriting SQL that leans on Redshift specific behaviour, re-testing every dashboard that reads from it, running both warehouses in parallel until you trust the new numbers, and above all re-pointing every pipeline that loads it. On a warehouse with a dozen upstream sources that is a quarter of engineering time, not a line item.

Set that against the numbers. A two node RA3 warehouse moving to RG saves $237.83 a month, $2,853.96 a year, for a resize. A migration to BigQuery might save more in year two and beyond, but it has to clear the engineering cost first. Price both, honestly, before deciding which one you are actually solving for.

So which one should you choose?

• If the complaint is the bill and you are staying on AWS, move to RG first. Exactly 30 percent off compute, no Spectrum scan charge, feature parity, and a resize instead of a rebuild. Try this before you build a migration business case, because it may end the conversation.
• If your query volume is spiky and modest, BigQuery. Cheapest storage of the four, automatic long term storage discounts, no cluster to size, and free below 1 TiB of scanning a month. Just put guardrails on scanned bytes.
• If you need multi-cloud or a forecastable bill, Snowflake. Credits are all-in and tier maths is a clean multiplier, which makes budgeting genuinely easier than any of the alternatives here.
• If one team owns pipelines, SQL and ML, Databricks. Consolidation is the saving, not the unit price, and the unit price is easy to misread.

The cost that appears on none of these rate cards

All four platforms are places to put data and run compute against it. None of them changes what happens when someone in operations needs a number on a Tuesday afternoon: the question becomes a ticket, the ticket waits for whoever writes SQL, and that delay costs more than the warehouse does.

That is the gap Agentsql fills. It connects read-only to Postgres, MySQL, Snowflake or BigQuery, turns a plain-English question into SQL, runs it, and shows the SQL every time so an analyst can check the logic before anyone acts on the number. Flat monthly price, no credit meter and no RPU meter attached. To be clear about the boundary: we do not connect to Redshift or Databricks, so if you stay on Redshift, Amazon Q generative SQL in Query Editor v2 is the built-in option to evaluate, and our Redshift pricing page is where to price the warehouse underneath it.

Still narrowing the list? The seat-priced BI layer is covered in Tableau pricing, Looker pricing and Power BI pricing, the AWS-native option in QuickSight pricing, and everything side by side in our BI tools comparison.