Snowflake's model converts every inefficiency into revenue: oversized warehouses, idle compute, and commitments sized from sales projections. We optimize the burn first, then size and negotiate the capacity deal from evidence.
This engagement is bought by organizations whose Snowflake bill grew past its business case: warehouses sized up for one bad query and never sized down, auto suspend timers idling compute at full billing, and storage growing without lifecycle discipline, all burning credits around the clock.
It fits data platform and FinOps teams facing a capacity renewal where Snowflake proposes a commitment from its own growth curves, with unused credits expiring at term end and overage landing at on demand rates. The renewal is won or lost before it starts, in the consumption data.
Consumption pricing turns operational habits into commercial outcomes:
Optimizing first and committing second reverses the vendor's preferred order, and the difference lands directly in the commitment size and the rate you pay.
The engagement follows the four workstreams of our Snowflake statement of work. Consumption is baselined and analyzed, the burn is optimized, the commitment is sized and benchmarked from the optimized run rate, and the negotiation runs to signature.
| Deliverable | What it contains |
|---|---|
| Consumption baseline report | Credit burn by warehouse, workload, and storage with the drivers ranked and the commitment position documented. |
| Rightsizing report | The optimization register with savings per action and the optimized run rate for commitment sizing. |
| Commitment and benchmark paper | The target commitment, structure, and discount benchmarked against comparable Snowflake agreements. |
| Negotiation playbook | Sequencing, timing, and the rollover and overage protections to pursue. |
| Proposal assessments to signature | Every Snowflake proposal assessed in writing against the model and benchmarks. |
Snowflake's sellers size commitments from growth curves because growth is what they are paid on. A commitment sized from your optimized run rate is routinely 20 to 30 percent smaller, and the optimization itself keeps paying every month after the deal closes.
The consumption levers are operational, warehouse sizing, suspend policies, workload placement, storage lifecycle, and we specify them with the platform team so the savings are engineered rather than hoped for.
We hold no Snowflake relationship revenue and no reseller position across the data stack, so the sizing verdict and the benchmark come with no thumb on the scale.
The engagement runs fixed price, all inclusive, or on contingency at 25 percent of the savings we deliver: you keep 75 percent, and if we save you nothing, you pay nothing.
Consumption commitments negotiated across the stack, on the record.
A San Francisco financial institution gained strategic flexibility and cut projected consumption spend.
✓ Published case studyA SaaS company cut its cloud run rate through systematic waste elimination before committing.
✓ Published case studyA Florida hospitality group saved 20 percent through compute optimization ahead of its commitment.
✓ Published case studyA Dubai media group saved 15 percent on its cloud commitment through consumption discipline.
On consumption: credits burned by compute warehouses per second of use, plus storage and services. Capacity agreements trade a committed credit purchase for discounts, with unused credits expiring and overage billed at on demand rates.
In warehouses sized above their workloads, suspend timers idling compute at full billing, workloads placed on larger warehouses than their queries need, and storage without lifecycle discipline. Each is measurable from your usage data.
From the optimized run rate plus validated growth, never from Snowflake's projections. Oversizing strands credits at term end; undersizing pushes real usage to on demand rates. Both directions of error are expensive.
Rollover of unused credits, overage rate protection, commitment flexibility across terms, and clean treatment of new workloads. The negotiation pursues the structure alongside the rate.
No. Rightsizing maps warehouses to what queries actually need, and suspend policies are tuned against workload patterns. The recommendations are specified with the platform team so performance constraints stay explicit.
Two quarters out. The optimization needs time to show in the consumption data Snowflake sees, which is what makes the smaller commitment credible at the table.
Snowflake usage and billing data, warehouse configuration, the current agreement and any proposals, and workload context from the platform team.
Fixed price, all inclusive, covering all four workstreams, up to four advisory calls, and email support, or contingency at 25 percent of the savings we deliver: you keep 75 percent, and if we save you nothing, you pay nothing.
The consumption optimized, the commitment sized from evidence, and the capacity deal negotiated with protections in writing.
One letter a month. Negotiation moves, audit signals, and price book shifts.