Hey team! Been deep-diving into cloud data warehouses for a new project and the pricing models can get wild. 😅 I'm trying to nail down a real-world cost comparison between the big three:
**Azure Synapse vs Google BigQuery vs AWS Redshift**
I've got some initial numbers from our tests (mostly around on-demand querying and storage), but I'd love to hear from anyone who's run this in production. Specifically:
* **The breakpoint:** At what data/query volume does a provisioned Redshift cluster become cheaper than BigQuery's on-demand?
* **Synapse's serverless SQL:** How does the per-TB scanned cost really play out with complex joins?
* **Commitment traps:** Any gotchas with the 1-3 year reservations for any of these?
Our rough early take: BigQuery is winning for sporadic, unpredictable loads, but the others might catch up at scale. Would love to see your latency/cost-per-query data if you have it!
ā Jason
Let's build better workflows.
I'm a platform engineer at a mid-sized e-commerce company (around 200-person tech team), and we've run production data pipelines on all three platforms over the last few years. We currently use BigQuery as our primary warehouse, but we've had Redshift clusters in the past and recently completed a POC with Azure Synapse for a new analytics product.
Here's my breakdown based on our actual bills and operational headaches:
1. **The real on-demand vs. provisioned crossover point:** For us, Redshift's provisioned RA3 nodes became cheaper than BigQuery on-demand when we had a consistent, predictable load scanning over 12-15 TB of data *every single day*. Below that, especially with spiky weekend traffic, BigQuery's pay-per-query model saved us about 30% on average. The hidden killer with Redshift was the cost of scaling for monthly reporting - peaks required more nodes sitting idle 80% of the time.
2. **Synapse serverless SQL per-TB scanning trap:** Synapse serverless charges $5 per TB scanned. With complex joins, especially on poorly partitioned tables, we saw the scanned data volume balloon to 4-5x the actual data size returned. A 1 TB table could trigger a 4.5 TB scan charge for a bad query. BigQuery's on-demand model ($6.25 per TB scanned) has the same risk, but its columnar storage and BI Engine felt more forgiving in our tests.
3. **Commitment gotchas and lock-in:** AWS Redshift's 3-year Reserved Instance discount looks great on paper (up to 75% off), but you're locked into a specific node type and count. Our data growth pattern shifted, and we were stuck with outdated, dense-storage nodes for two painful years. Azure Synapse's reservation is for the entire dedicated SQL pool, not individual vCores, which made scaling a nightmare. BigQuery's flat-rate pricing commitments are the most flexible - you commit to slot-hours, not infrastructure, and can assign them across any project.
4. **The concurrency wall:** This was the deciding factor for us. On Redshift, with a 4-node RA3.xlarge cluster, we consistently hit concurrency limits with 40+ simultaneous queries; they'd queue and timeouts would spike. BigQuery's 2,000 concurrent slot baseline for on-demand and Synapse's serverless "scales automatically" claim both handled our 100+ user dashboards at 9 AM without a hiccup. Synapse did introduce a 3-5 second cold start penalty for the first query in a period of inactivity.
My pick is **BigQuery on-demand**, specifically for your described "sporadic, unpredictable loads." Its separation of storage and compute, combined with the ability to mix on-demand with flat-rate slots for specific batch jobs, gives you the most knobs to turn without long-term commitment. If your data volume is predictably massive and grows linearly, tell us your expected monthly scan volume and your peak concurrent user count - that's what will make the call clean.
YAML is not a programming language, but I treat it like one.
Your point about Synapse's scanning cost trap is so crucial! We saw something similar during our trial, but it was exacerbated by the system's metadata caching behavior, or lack thereof. We had a few pre-aggregated reporting tables where even a simple `SELECT * FROM report_daily` with a date filter would still trigger a full table scan behind the scenes because of how the stats were collected. The per-TB scanned model really punishes exploratory work on large datasets.
Also, on your Redshift point - did you find the Concurrency Scaling feature helped with those monthly reporting peaks, or did the cost of enabling it just add another layer of complexity? We considered it but got nervous about the autoscaling bill.
Another trial, another spreadsheet
The Synapse scanning behavior you mentioned is exactly why I tell teams to treat their pricing page like a horror movie script. It gets worse when you realize their default automatic statistics collection can actually increase your scanned volume during peak usage windows, creating this lovely feedback loop where optimizing your queries costs you more money to discover.
On the Redshift concurrency scaling question you asked the other user - it's a clever tax on your own poor capacity planning. Every team I've seen enable it ends up treating it as permanent permission to under-provision their main cluster, making the "scaling" cost a fixed monthly line item that wipes out any reservation savings. The real fix is to batch those monthly reports onto a separate, cheap provisioned cluster you spin up and tear down with Terraform, but that requires someone to actually think about workload isolation.
Your 12-15 TB daily breakpoint for Redshift matches what I've seen, but that assumes perfect workload compression on RA3. If your data isn't sorted perfectly or your queries go wide, you're just paying for S3 scanning with extra steps and a heftier markup.
keep it simple