This brief reports what Snowflake's 10-Q, filed 2026-05-29, states, and nothing else. Every item below is a sentence the filing contains, quoted exactly and verified character-for-character against the document fetched from EDGAR. No inference is drawn and no claim is made about what any figure means.
The figures
| Metric | Value | Period |
|---|---|---|
| Remaining performance obligations | $9.2 billion | April 30, 2026 |
| Weighted-average remaining life of our capacity contracts | 2.6 years | April 30, 2026 |
Every figure above is quoted from the filing; the sentence it was read from appears under its beat below.
The evidence, by beat
ARR / NRR / NDR growth or dilution
Historical numbers for (i) net revenue retention rate, (ii) customers with trailing 12-month product revenue greater than $1 million, and (iii) Forbes Global 2000 customers reflect any adjustments for acquisitions, consolidations, spin-offs, and other market activity.
Our net revenue retention rate is subject to adjustments for acquisitions, consolidations, spin-offs, and other market activity, and we present our net revenue retention rate for historical periods reflecting these adjustments.
To calculate this metric, we first specify a measurement period consisting of the trailing two years from our current period end.
We then calculate our net revenue retention rate as the quotient obtained by dividing our product revenue from this cohort in the second year of the measurement period by our product revenue from this cohort in the first year of the measurement period.
As of April 30, 2026, our remaining performance obligations were approximately $9.2 billion, of which we expect approximately 50% to be recognized as revenue in the 12 months ending April 30, 2027 based on historical customer consumption patterns.
The weighted-average remaining life of our capacity contracts was 2.6 years as of April 30, 2026.
seat vs consumption / usage-based pricing mix shift
We monitor our dollar-based net revenue retention rate to measure this growth.
To calculate this metric, we first specify a measurement period consisting of the trailing two years from our current period end.
The cohorts used to calculate net revenue retention rate include end-customers under a reseller arrangement.
We then calculate our net revenue retention rate as the quotient obtained by dividing our product revenue from this cohort in the second year of the measurement period by our product revenue from this cohort in the first year of the measurement period.
Any customer in the cohort that did not use our platform in the second year remains in the calculation and contributes zero product revenue in the second year.
Our net revenue retention rate is subject to adjustments for acquisitions, consolidations, spin-offs, and other market activity, and we present our net revenue retention rate for historical periods reflecting these adjustments.
We expect our net revenue retention rate to decrease over the long-term as customers that have consumed our platform for an extended period of time become a larger portion of both our overall customer base and our product revenue that we use to calculate net revenue retention rate, and as their consumption growth primarily relates to existing use cases rather than new use cases.
Scope: customers that have consumed the platform for an extended period and consumption growth primarily relates to existing use cases.
We do not include customers that consume our platform only under on-demand arrangements for purposes of determining our customer count.
Our customer count is subject to adjustments for acquisitions, consolidations, spin-offs, and other market activity, and we present our customer count for historical periods reflecting these adjustments.
Our Forbes Global 2000 customer count is a subset of our customer count based on the 2025 Forbes Global 2000 list.
Our Forbes Global 2000 customer count is subject to adjustments for annual updates to the list by Forbes, as well as acquisitions, consolidations, spin-offs, and other market activity with respect to such customers, and we present our Forbes Global 2000 customer count for historical periods reflecting these adjustments.
We define free cash flow, a non-GAAP financial measure, as GAAP net cash provided by operating activities reduced by purchases of property and equipment and any capitalized software development costs.
Cash outflows for employee payroll tax items related to the net share settlement of equity awards are included in cash flow for financing activities and, as a result, do not have an effect on the calculation of free cash flow.
competitive displacement / win-loss / platform consolidation
We currently offer our platform on the public clouds provided by AWS, Azure, and GCP, which are also some of our primary competitors.
Currently, a substantial majority of our business is run on the AWS public cloud.
We introduced data warehousing on our platform in 2014 as our core use case, and our customers subsequently began using our platform for additional product categories, including data engineering, analytics, transactions, AI, and applications and collaboration.
Our future success depends on our ability to continue to innovate rapidly and effectively and increase customer adoption of our platform and the AI Data Cloud, including emerging product areas such as AI, Postgres, Observability, Apache Iceberg tables, and Snowpark.
AI product monetisation / attach
The filing names this beat, but no statement in it verified as a verbatim quote, so none is reported here.
guidance raised/cut, outlook change
The filing names this beat, but no statement in it verified as a verbatim quote, so none is reported here.
cost action / restructuring / headcount
The filing names this beat, but no statement in it verified as a verbatim quote, so none is reported here.
Verification ledger
- 24 statements proposed by the extractor
- 23 verified verbatim against the fetched filing (96%)
- 1 discarded — not quotable character-for-character
The verbatim check contains no model: the extractor proposes a statement, and a deterministic substring match against the fetched text decides whether it is admissible. A proposal that does not verify is dropped.
This brief publishes no inference and no synthesis. The publication's inference layer must clear a measured second-lab confirmation threshold before it may appear; it has not, so it is absent by rule rather than by omission.