Snowflake vs. Databricks Pricing

The Real Difference: Snowflake Bundles, Databricks Doesn't

Snowflake's credit price is close to an all-in number. Databricks' DBU price is a platform fee stacked on top of a separate cloud bill you pay directly to AWS, Azure, or GCP. That one structural fact is what most "Snowflake vs.

Databricks pricing" comparisons skip, and it's why lining up a per-credit dollar figure against a per-DBU dollar figure produces a misleading answer. When you spin up a Snowflake virtual warehouse, the credit rate covers the compute and Snowflake's own storage bills separately but still comes from Snowflake. When you spin up a non-serverless Databricks cluster, the DBU rate covers only the Databricks platform layer: the underlying EC2 instances, Azure VMs, or GCP compute, plus the S3, ADLS, or GCS storage holding your actual data, land on a separate bill from your cloud provider.

Databricks says this directly on its own pricing calculator page: non-serverless estimates "do not include cost for any required AWS services (e.g., EC2 instances)." A workload that looks cheaper on Databricks' published DBU rate can cost more once the cloud infrastructure is added back in, and a workload that looks expensive on Snowflake's per-credit price already has that infrastructure baked in. Neither platform is more expensive by default. They're pricing different things.

Snowflake's Pricing Model: Credits, Editions, and Exact Rates

Snowflake's Service Consumption Table publishes an exact on-demand credit price for every cloud provider, region, and edition combination. In AWS US East (Northern Virginia), Standard edition runs $2.00 per credit, Enterprise $3.00, Business Critical $4.00, and Virtual Private Snowflake $6.00. Move to a different region and the rate shifts: Standard ranges from $2.00 to $3.25 across published regions, Enterprise from $3.00 to $4.90, Business Critical from $4.00 to $6.50, and VPS from $6.00 to $9.75.

Virtual warehouses consume credits at a rate fixed by size, doubling at each step: an XS Standard warehouse burns 1 credit per hour, Small burns 2, Medium 4, Large 8, all the way to a 6XL warehouse at 512 credits per hour, billed per second after a one-minute minimum on startup. A separate cloud services layer, handling authentication, metadata, and query compilation, bills at 4.4 credits per hour but is waived entirely if daily cloud services usage stays under 10% of daily warehouse usage. Storage is billed by Snowflake directly, separate from compute, at $20 to $40 per terabyte per month depending on region and cloud provider ($23/TB/month is the modal US rate), with volume-based Capacity discounts bringing that down to as low as $13.80/TB/month at the highest committed-spend tier.

Databricks' Pricing Model: Why One DBU Rate Doesn't Exist

Ask "what does a DBU cost" and the honest answer is: it depends entirely on which compute type is running. A direct, live session with Databricks' own pricing calculator (Premium tier, AWS, US East) showed Jobs Compute priced at $0.15 per DBU, SQL Compute (Classic) at $0.22 per DBU, All-Purpose Compute at $0.55 per DBU, and SQL Serverless at $0.70 per DBU, a nearly 5x spread depending purely on workload type, before tier or region even enter the picture. Move to Enterprise tier and the same Jobs Compute rate climbs to $0.20 per DBU, a 33% premium over Premium for identical compute.

This is a two-dimensional pricing surface, workload type multiplied by tier, that Snowflake's single-dimension, edition-based credit model doesn't have. Also worth knowing before comparing rates at all: Databricks retired its Standard tier on AWS and Google Cloud in October 2025, with Azure following by October 1, 2026, meaning Premium is now the effective baseline tier for any new Databricks deployment.

The Storage Line Item Only One Platform Bills

Snowflake bills storage itself, as part of its own proprietary, compressed storage format, at the $20 to $40 per terabyte per month rate covered above. Databricks generally does not, because the core lakehouse data, stored as Delta Lake or open-format files, lives in the customer's own S3, ADLS, or GCS bucket, billed directly by the cloud provider at that provider's standard object-storage rate, typically far cheaper per terabyte than Snowflake's bundled rate. Databricks does bill separately, through its own Databricks Storage Unit (DSU), for a narrower set of Databricks-managed storage products layered on top: Lakebase database storage at a 15x DSU multiplier, point-in-time restore at 8.7x, snapshots at 3.91x, and AI Search index storage at 10x, all confirmed directly from Microsoft's official Azure Databricks pricing documentation.

The practical consequence: a raw storage-cost comparison between the two platforms isn't apples-to-apples unless the Databricks side explicitly adds back the customer's own cloud storage bill. Workloads with very large, cold, infrequently-queried data volumes structurally favor Databricks' unbundled model on storage cost alone, independent of anything happening on the compute side.

The Databricks Compute-Type Mistake That Costs 3.7x More

The single most avoidable Databricks cost mistake is running a scheduled, unattended job on All-Purpose Compute instead of Jobs Compute. Both are the same underlying instance type; the only difference is the DBU rate attached to the compute type selected. At $0.55 per DBU for All-Purpose versus $0.15 per DBU for Jobs Compute, the identical workload costs roughly 3.7x more for no functional difference, confirmed directly against Databricks' own live rate card rather than taken solely on a third party's word.

All-Purpose Compute is priced for interactive, notebook-driven work where a human is actively querying; Jobs Compute is priced for scheduled, unattended pipeline runs. The two are easy to conflate in the Databricks UI, which is exactly why this specific gap shows up repeatedly across independent Databricks cost-optimization writeups. Before estimating or auditing a Databricks bill, check which compute type is actually attached to each recurring job, not just how much data it processes.

Snowflake's Gen2 Warehouses Cost More, By Design

Snowflake's newer Gen2 warehouse generation consumes more credits per hour than a Standard warehouse of the identical nominal size, confirmed directly from Snowflake's own credit tables: on AWS and GCP, Gen2 runs at a 1.35x multiplier over Standard (an XS Gen2 warehouse burns 1.35 credits per hour against Standard's 1.0), and on Azure the multiplier is 1.25x. That's a 25 to 35% higher hourly rate for the same warehouse size, and it's easy to miss if a cost estimate is built off the Standard credit table while production actually runs on Gen2. Snowflake's own positioning is that Gen2's per-query speed improvement more than offsets the higher hourly rate, which is a performance claim, not a pricing one, and it doesn't hold for every workload.

Anyone estimating Snowflake cost should confirm which warehouse generation is actually deployed before applying either credit table.

Elasticity: Multi-Cluster Warehouses vs. Serverless Compute

Snowflake handles concurrency, not per-query size, through multi-cluster warehouses: a warehouse can run multiple equal-size clusters at once, with credit consumption equal to the per-cluster rate multiplied by the number of running clusters, plus auto-suspend to stop billing entirely once a warehouse sits idle. Multi-cluster warehouses require Enterprise edition or above; Standard edition can't create them. Databricks splits elasticity across compute types instead: non-serverless clusters autoscale by adding or removing nodes but keep billing both DBUs and the underlying cloud VM cost continuously while running, even when mostly idle, while Serverless compute bundles infrastructure into the DBU rate and scales to zero between uses, at a materially higher per-DBU price ($0.70 for SQL Serverless versus $0.22 for SQL Classic, both confirmed above).

The pattern is the same shape on both platforms: steady, predictable workloads are cheaper on the lower per-unit rate with careful sizing; spiky, hard-to-forecast workloads are cheaper on the platform's scale-to-zero option despite its higher unit price.

AI Pricing: Cortex Credits vs. Databricks Model Serving DBUs

Both platforms have layered LLM and AI-specific billing on top of their existing unit systems, and both spread it across several distinct sub-tables rather than one blended rate. Snowflake's Cortex Complete bills LLM inference per model, per million tokens, confirmed directly from Snowflake's own consumption table: from 0.12 credits per million tokens on mistral-7b up to 5.50 credits per million tokens on reka-core, with claude-3-5-sonnet at 2.55 and llama3.1-405b at 3.00 in between. Other Cortex features bill on entirely different units within the same table: Cortex Analyst charges a flat 0.067 credits per message regardless of length, Cortex Search bills 6.3 credits per gigabyte of indexed data per month, and Document AI runs 8 credits per compute-hour.

Databricks' AI billing is at least as fragmented, per Microsoft's official Azure Databricks pricing documentation: Foundation Model Serving pay-per-token rates run from 1.0 DBU per million input tokens on GPT OSS 20B up to 214.3 DBU per million input tokens for Claude Opus 4/4.1, output tokens typically billed at 3 to 5x the input rate, GPU Model Serving bills by instance size regardless of request volume (10.48 DBU/hour for a T4-equivalent instance up to 314.4 DBU/hour for a four-GPU instance), and fine-tuning bills separately again, with a Llama 3.3 70B fine-tuning run against 10 million training words landing around 225 DBUs, roughly $146 at a $0.65/DBU reference rate. Comparing "AI pricing" between the two platforms only makes sense model-by-model and workload-shape-by-workload-shape, chat completion, embedding, fine-tuning, vector search, not as a single number.

Both Vendors Are Pricing Into the Same New Territory

The pricing mechanics above sit on top of two platforms that are still growing fast enough that neither is a settled target. Databricks' own June 2026 press release put its revenue run-rate at $5.4 billion, up more than 65% year over year, with net revenue retention above 140% and more than 800 customers above $1 million in annual recurring revenue. Snowflake's September 2026 earnings call reported similarly strong second-quarter fiscal 2027 numbers: $1.55 billion in quarterly revenue, up 35% year over year, 126% net revenue retention, and 828 customers above $1 million in trailing-twelve-month product revenue.

Both companies are also now selling into the same new category at the same time: Snowflake closed its acquisition of Crunchy Data in June 2025 and shipped Snowflake Postgres, a managed transactional Postgres database, to general availability in February 2026, while Databricks acquired Neon and shipped Lakebase, its own serverless Postgres product, which Databricks' own press release says has already crossed $100 million in revenue run-rate. Neither vendor has published a granular consumption table for its Postgres product the way each has for core warehouse or lakehouse compute, so pricing either one today still means requesting a quote or reading the general-purpose compute rates above as a rough proxy. Anyone pricing "Snowflake vs.

Databricks" for a workload that includes an operational, transactional component should treat this as a fast-moving line item, not a settled one.

What Neither Vendor Publishes, and How to Estimate Your Own Number

Both vendors reserve their steepest discounts for negotiated commitments, and neither publishes the discount schedule. Snowflake's Capacity pricing applies a customer-specific credit discount on top of the published on-demand table; Databricks' Committed Use Contracts are described on its own pricing page only as offering discounts that "increase with larger commitments," with no percentage attached to either. That's a structural similarity between the two platforms, not a transparency gap unique to one of them.

Given that, the only honest way to estimate a real bill on either platform is a workload-specific walk: identify the edition or tier, identify the specific warehouse type (Standard vs. Gen2) or compute type (Jobs, All-Purpose, SQL Classic, SQL Serverless) actually running the work, price compute at the matching rate, add storage (bundled for Snowflake, the cloud provider's own rate plus any DSU-billed features for Databricks), and check AI/ML usage against the relevant per-model sub-table if it's in scope. There's no public calculator, on either vendor's site or a third party's, that replaces doing this by hand once, because "which platform is cheaper" genuinely doesn't have a single answer without knowing which workload is being asked about.

Related: Data Cost Optimization