If you’re sketching out a data platform on a whiteboard right now, I want you to do something. Stop calculating storage costs. They’re not the bill.
\nI pulled the public pricing for AWS, Azure, GCP, Databricks, and Snowflake and stacked them next to each other. Storage is the cheap part. The expensive part is everything that moves the data, and the expensive part is the part you’re least likely to model correctly when you’re picking a vendor.
\nLet me walk through what actually shows up on the invoice.
\nFor hot, frequently accessed data, the big three are within a rounding error of each other:
\nDrop into the cool tiers and AWS S3 takes the lead at $0.0125 per GB. Drop into deep archive and you’re paying $0.00099 per GB on either AWS Glacier Deep Archive or Azure Archive. That’s a tenth of a cent per gigabyte, per month, for data you almost never touch.
\nGood for you, but I think anyone leading with “per-GB storage cost” in a procurement deck is selling you a story. Storage capacity is roughly five percent of a typical Databricks bill. Five. The other 95% is the part nobody wants to talk about.
\nIngress is free. Always. The cloud providers want your data in.
\nGetting it back out is where they collect.
\nThen layer on API operations. A million GET requests on S3 costs about $0.40. The same million GETs on Google Cloud Storage can run closer to $5.00 because they classify operations differently. If your analytics workload is hammering small files, those API calls add up faster than the storage they’re reading.
\nStoring 10 TB? Maybe $200 a month. Storing 500 TB? You’re at $10,000 a month before a single byte leaves the region or a single query fires.
\nDatabricks uses what’s commonly called a Two-Bill Model. You get one invoice from your cloud provider for the actual VMs and storage, and a separate invoice from Databricks for the software, measured in DBUs (Databricks Units).
\nIn a typical mid-sized deployment around $18,000/month, the breakdown looks like this:
\nThe DBU rate changes based on what you’re doing. Automated jobs start at $0.15/DBU. Interactive notebooks for analysts start at $0.40/DBU. That’s not an accident. Databricks wants you running production workloads on cheap job clusters, not on the expensive all-purpose clusters your data scientists love to leave running over a weekend.
\nIf you’re not actively pushing teams toward job clusters and ARM-based instances, you’re leaving real money on the table.
\nSnowflake’s pricing pitch sounds clean. Pass-through storage at $40/TB/month on-demand, dropping to $23/TB/month with a capacity commitment. Compute as Credits. Done.
\nExcept it isn’t done. Snowflake stores data in immutable 16MB micro-partitions. Immutable. You can’t change them in place. Update a single row in a 1 TB table and Snowflake writes a new file and keeps the old one around.
\nWhy keep the old one? Two features:
\nThis is the part that gets people. A 1 TB table that’s getting updated multiple times a day can balloon to 25 TB of billed storage because Snowflake is retaining every prior version of every micro-partition you’ve touched. Your dashboard says “1 TB table.” Your invoice says otherwise.
\nAnd compute? Virtual Warehouses bill per second, but with a 60-second minimum every single time you resume or resize. Aggressive auto-suspend sounds like a cost optimization. It’s not. If you’re spinning a warehouse up and down every 30 seconds, you’re paying the 60-second minimum every time and quietly multiplying your bill.
\nA few things I’d put on the wall before signing anything:
\nThe vendors all have a story about why their model is the cheap one. Read past the per-GB number on the slide. The bill is somewhere else.
\nHappy modeling.
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\n", "date_published": "2026-05-04T10:00:00-05:00", "url": "https://llbbl.blog/2026/05/04/the-real-cost-of-your.html", "tags": ["DevOps","Cloud","Data","Snowflake","Databricks"] } ] }