BigQuery's serverless architecture and separation of storage/compute make it simpler and more cost-effective for most workloads. Redshift offers more control and tighter AWS integration, but requires more operational overhead.
I've lived inside Redshift since the ds2 node days, and I've run production analytics on BigQuery for years. The BigQuery-vs-Redshift question is honestly less interesting than the vendor marketing makes it sound — if you've already picked a cloud, the answer is usually decided before the meeting starts. But there's a real architectural split underneath, and it's worth understanding before you commit.
Google BigQuery is the purest serverless warehouse on the market. There are no clusters, no nodes, no "what size should we provision" conversation. Google runs a massive shared pool of slots (their unit of compute) and your query grabs slots on demand. Pricing is either on-demand ($6.25 per TB scanned) or reserved slots via Editions ($0.04–$0.10 per slot-hour depending on tier). The mental model is simple: write SQL, pay for either the bytes you scanned or the slot-hours you reserved.
Amazon Redshift is the warehouse AWS wishes people thought about first. The original Redshift was cluster-based — you picked ds2/dc2 node types, sized them, and paid per node-hour regardless of how much you used them. The modern RA3 nodes decoupled storage from compute (finally, in 2019), and Redshift Serverless (2022) added a serverless compute option that bills per RPU-second. If you're adopting Redshift fresh today, you're almost certainly using RA3 + Redshift Serverless, not the old cluster model.
When I help teams pick between these two, the decision almost always comes down to four factors:
I helped a retail analytics team on AWS evaluate both for a migration off on-prem SQL Server. Their workload was textbook steady — roughly 500 concurrent dashboard users during business hours, minimal nights and weekends, fairly predictable query patterns. We priced it both ways. Redshift RA3 provisioned came in about 35% cheaper than BigQuery Editions Enterprise at the same SLA. They stayed on AWS, picked Redshift, and haven't regretted it. Conversely, I had a media startup with spiky analyst usage — five hours a day during the workweek, zero on weekends — and BigQuery's true zero-cost idle time made it the clear winner on cost.
This really is a cloud-footprint decision for 80% of teams. The technical gaps have narrowed enough that you're not leaving performance on the table with either. Here's how I'd break it down.
Choose BigQuery if:
SELECT * with no predicate scans 50 TB and shows up as a five-figure line item. Set quotas on day one. Also: partitioning isn't optional on fact tables — without it you burn money on every query.No — it depends heavily on workload shape. For bursty, sporadic queries, BigQuery's pay-per-scan or auto-scaling Editions model is usually cheaper because idle time is free. For sustained, high-concurrency dashboard workloads, Redshift RA3 provisioned at 60%+ utilization is often 20–40% cheaper than BigQuery at the same SLA. The two biggest cost levers aren't platform choice but (1) partitioning/clustering discipline on BigQuery and (2) cluster right-sizing plus pause-when-idle on Redshift.
Yes, and Google actively encourages it — they ship the BigQuery Migration Service with a SQL translator that handles most of the dialect differences automatically. Data-type mappings (BigQuery has no CHAR/VARCHAR distinction, different timestamp semantics, no SUPER-style nested types mapping cleanly) will bite you, and you'll rewrite any stored procedures. Budget 2-3 months for a typical enterprise migration — the SQL is the easy part, the downstream BI tool rewrites are the hard part.
BigQuery, by a wide margin. It's fully serverless — no vacuuming, no distribution keys, no cluster resizing. Redshift (provisioned) requires VACUUM and ANALYZE to maintain sort/dist key efficiency and stats. Redshift Serverless closes most of this gap but still needs base RPU tuning. For a team without a dedicated data platform engineer, BigQuery is the lower-toil choice.
Provisioned clusters (RA3) give you node-level control — you pick ra3.xlplus/4xlarge/16xlarge, set a node count, and pay per node-hour regardless of utilization. Great for steady, predictable workloads. Serverless gives you a base RPU capacity that auto-scales up and down with demand and bills per RPU-second — great for bursty or unpredictable workloads. Most teams today default to Serverless for dev/test and provisioned for high-utilization production clusters.
BigQuery has had native JSON, ARRAY, and STRUCT types for a decade — nested data is first-class, and you write queries against it the same way you would relational data. Redshift's SUPER type handles JSON but feels more bolted-on; you frequently end up wrapping JSON access in subqueries. If a meaningful chunk of your workload is event-log-style nested data, BigQuery is noticeably more pleasant.
Redshift ML exists but it's essentially a SageMaker front-end — you write CREATE MODEL in SQL and Redshift hands off the training to SageMaker Autopilot. BigQuery ML keeps the training inside BigQuery and supports a broader set of built-in model types (linear, logistic, k-means, ARIMA, boosted trees, matrix factorization, DNN). For analyst-driven ML, BigQuery ML is meaningfully more capable. For production ML, both approaches are placeholders compared to a real ML platform.
Both let you query data sitting in object storage (S3 for Spectrum, GCS or other locations for BigQuery external tables) without loading it. Spectrum is more mature and tightly integrated with Glue Data Catalog — it's been production-hardened at petabyte scale since 2017. BigQuery external tables now include BigLake which adds governance and performance features. Practically, if you already have data in S3 with a Glue catalog, Spectrum is the lower-friction path. For multi-format (Iceberg, Delta) external data, both work but BigLake has slightly better Iceberg support as of this year.