This brief reports what Snowflake's 10-Q, filed 2026-09-04, 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 | $9.0 billion | July 31, 2026 |
| Weighted-average remaining life of our capacity contracts | 2.4 years | July 31, 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
As of July 31, 2026, our remaining performance obligations were approximately $9.0 billion, of which we expect approximately 54% to be recognized as revenue in the 12 months ending July 31, 2027 based on historical customer consumption patterns.
The weighted-average remaining life of our capacity contracts was 2.4 years as of July 31, 2026.
We monitor our dollar-based net revenue retention rate to measure this growth.
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.
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.
seat vs consumption / usage-based pricing mix shift
We monitor our dollar-based net revenue retention rate to measure this growth.
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.
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.
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.
Product revenue excludes our professional services and other revenue, which has been less than 10% of revenue for each of the periods presented.
competitive displacement / win-loss / platform consolidation
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 (including Snowflake CoWork and Cortex Code), Postgres, Observability, Apache Iceberg tables, and Snowpark.
We must also continue to enhance our data sharing and marketplace capabilities so customers can share their data with internal business units, their customers, and other third parties, acquire additional third-party data and data products to combine with their own data to gain additional business insights, and develop and monetize applications on our platform.
As we expand further into the public sector and highly regulated countries and industries, our platform and operations will need to address additional requirements specific to those markets, including data sovereignty requirements.
Frontier AI model providers may seek to vertically integrate their offerings by expanding into the data storage and management layers and developing their own database solutions.
We also face and may in the future face increased competition from some of our customers and vendors, including observability solution providers and AI model providers.
Attempts by third-party application or database providers to restrict the use of drivers and connectors may make it more difficult for customers to use our platform, which could lead to reduced sales and consumption.
If we fail to innovate in response to changing customer needs, new technologies, or other market requirements, our business, financial condition, and results of operations could be harmed.
We compete in markets that evolve rapidly. We believe that the pace of innovation will continue to accelerate as customers increasingly base their purchases of cloud data platforms on a broad range of factors, including performance and scale, cost, markets addressed, types of data processed, ease of data ingress and egress, support of open data formats, user experience and programming languages, use of AI, interoperability and integrations across tools, applications, and platforms, and data governance, security, and regulatory compliance.
Scope: based on performance and scale, cost, markets addressed, types of data processed, ease of data ingress and egress, support of open data formats, user experience and programming languages, use of AI, interoperability and integrations across tools, applications, and platforms, and data governance, security, and regulatory compliance.
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
- 26 statements proposed by the extractor
- 22 verified verbatim against the fetched filing (85%)
- 4 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.