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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Snowflake and Mistral AI announced a global, multiyear partnership on March 5, 2024, to make selected Mistral models available through Snowflake Cortex. Snowflake Ventures also invested in Mistral’s Series A, but the amount was not disclosed. The practical proposition was managed access to language models alongside data held in Snowflake—not a promise that every Mistral model would be available or that data could never be routed outside a customer’s region.
Snowflake’s current documentation lists mistral-large2, mistral-7b and mixtral-8x7b, subject to availability and account settings. That is a current product snapshot, not a continuation of the 2024 public-preview status. For a personal-finance reader assessing the business news, the key distinction is that this is an enterprise software distribution and infrastructure deal, not a consumer AI product launch.
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What Snowflake and Mistral announced
The March 5, 2024 announcement had three parts: a global, multiyear commercial partnership; distribution of selected Mistral models through Snowflake Cortex; and a Snowflake Ventures investment in Mistral’s Series A. The investment amount was not disclosed by the companies or in contemporary coverage. The agreement was not described as exclusive. A UK Competition and Markets Authority decision later listed Mistral models as available through several channels, including Snowflake, Amazon Bedrock and Mistral’s own platform (Snowflake’s announcement; VentureBeat’s contemporary report; CMA decision).
The initial models named were Mistral Large, Mixtral 8x7B and Mistral 7B. Snowflake said they were available in public preview through Cortex at launch. That preview label describes the announcement-era availability; it should not be read as the status of every model or feature today.
Which Mistral models were involved?
The models represent different capability and workload trade-offs. Snowflake’s current documentation uses the identifier mistral-large2; the original announcement named Mistral Large, not Large 2. Current model names and availability should be checked against Snowflake’s live regional-availability documentation.
| Model | What the 2024 announcement said | Practical interpretation |
|---|---|---|
| Mistral Large | Snowflake positioned it as the highest-capability model in the initial set. | A choice for more demanding generation or analysis; the original announcement does not establish its current rank against newer models. |
| Mixtral 8x7B | Included as an open-source model; Mixtral is a mixture-of-experts model. | A general-purpose option from the launch-era set. Its actual speed, quality and cost depend on the task and current service configuration. |
| Mistral 7B | Included as a smaller open-source model. | Snowflake currently describes mistral-7b as suitable for simpler summarization, structuring and question-answering tasks. Smaller does not mean a better fit for complex reasoning or generation. |
mistral-large2 |
Not named in the original 2024 announcement. | Snowflake currently lists it as a high-capability Cortex option. Treat this as a later catalog entry, not the name of the original model. |
Snowflake’s current documentation lists mistral-large2, mistral-7b and mixtral-8x7b, but a listing is not a guarantee that a model is enabled in every account, cloud or region. The company’s model and region guidance is at Cortex regional availability.
What “open LLMs” means—and does not mean
Snowflake’s announcement described Mixtral 8x7B and Mistral 7B as open-source models, and contemporary coverage used “open LLMs” in its headline. That wording should not be generalized to every model in Mistral’s portfolio. Open weights, open-source software, permission to use a model commercially and access through a managed API are different things. Check the license for the exact model and version before deploying or redistributing it; managed access through Cortex does not itself establish that a customer receives or can freely use model weights.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsWhat Cortex adds beyond a model catalog
Cortex is Snowflake’s managed AI layer. The 2024 announcement described language-model functions for tasks such as sentiment analysis, translation and summarization; foundation-model access for applications and retrieval-augmented generation (RAG); vector functions and data types; and integration with Python and Streamlit. The aim is to let teams build AI workflows using Snowflake’s data and platform services without independently procuring and operating GPU infrastructure for each model endpoint.
That can support practical workflows such as summarizing support tickets or contracts, classifying documents, extracting fields, analyzing multilingual text, enriching table rows with generated labels, and building internal question-answering tools over retrieved company material. These are possible applications of the platform, not evidence of named customers or measured business outcomes from the partnership.
“Taking models to the data cloud” is best understood as a workflow and platform proposition: teams can invoke a managed model from SQL or an API while working with data stored and governed in Snowflake. It does not guarantee that inference physically runs inside a customer’s account or that information never crosses a regional boundary. Cloud, region, feature, routing and account settings matter; Snowflake documents cross-region inference and model availability separately.
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How a current SQL workflow can look
For new SQL use cases, Snowflake recommends AI_COMPLETE. The following is an illustrative current pattern, not a claim that the launch-preview interface in 2024 was identical:
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →SELECT
AI_COMPLETE(
'mistral-7b',
'Summarize the following support ticket in one sentence: ' || ticket_text
) AS summary
FROM support_tickets;In practice, a team should confirm that the selected model is available to its account and region, that the role and account controls permit its use, and that the prompt and output handling meet its requirements. Snowflake says the legacy COMPLETE function is expected to be deprecated by the end of 2026; consult the function guidance before building a new workflow around it. The current SQL function catalog is documented at Cortex AI Functions.
Governance, regional routing and availability
Using Cortex can put inference access within a Snowflake-managed identity, governance and usage environment. That is useful when data teams already control permissions and workflows in Snowflake, but it is not a blanket privacy guarantee. Before sending sensitive material, verify the actual model route, cloud and region, whether cross-region inference is enabled or required, relevant provider and contract terms, and applicable retention and access controls. Snowflake’s own regional guidance and Cortex REST API documentation are relevant starting points.
- Model unavailable: Check regional availability and cross-region inference settings.
- Permission error: Review the account’s model controls or allowlists, role permissions and any required account-level settings.
- Compliance question: Confirm the route and geography for the particular function and account rather than relying on the shorthand that data stays “in Snowflake.”
How Cortex charges for model use
Snowflake’s Cortex AI Functions pricing is based on AI Credits per million tokens, with input and output counted separately. Ordinary platform costs—including warehouse compute, storage and data transfer—can still apply, as can costs for related services such as document parsing, embeddings or vector search. Snowflake separates AI Credits from Platform Credits in its pricing documentation.
Snowflake’s current consumption table lists approximately 1.00 AI Credit per million input tokens and 3.00 per million output tokens for mistral-large2; 0.08 input and 0.10 output for mistral-7b; and 0.23 input and 0.35 output for mixtral-8x7b. These are consumption rates in the table, not a universal dollar quote. Snowflake’s pricing example uses $2 per AI Credit and $3 per Platform Credit, but a customer’s contract, edition, cloud, region and discounts can change effective prices. Check the current consumption table and price the whole workflow, not tokens alone.
For budget monitoring, Snowflake documents AI usage-history views and cost-governance controls at AI cost management and governance. If latency or predictable throughput is a concern, test the actual workload and consider whether a smaller model is sufficient; Snowflake lists Mistral Large 2 as eligible for provisioned throughput in AWS and Azure clouds in its provisioned-throughput documentation.
When Cortex makes sense—and when another route may fit better
| Route | Best fit | Trade-off to assess |
|---|---|---|
| Snowflake Cortex | Enterprise data already lives in Snowflake, and teams want SQL or Snowflake-managed REST access, platform governance and centralized usage controls. | Model selection, regional routing and pricing are bounded by Snowflake’s supported catalog and account setup; total cost includes more than token consumption. |
| Mistral direct | An application is not centered on Snowflake, or the team wants direct provider integration and control over its API stack. | Identity, logging, networking, data-residency review and billing may be separate from the data platform; the team must evaluate its own operations and controls. |
| Amazon Bedrock | The organization is standardized on AWS identity, networking, procurement and operational tooling. | It is a different integration path from Snowflake-native SQL and governance; compare the workload and data architecture rather than assuming one channel is cheaper. |
| Databricks Mosaic AI | The organization is built around Databricks Lakehouse, MLflow, Unity Catalog or Databricks-native model serving and machine-learning workflows. | Its fit depends on existing platform investment and workflow needs; no universal cost or performance winner follows from the partnership announcement. |
| Self-hosted model | There is a strong need for control over weights, deployment, networking or data locality, and the organization has GPU and MLOps capabilities. | The organization takes on infrastructure procurement, serving, scaling, patching, monitoring and license review. |
The decision is primarily architectural: choose Cortex when the value of keeping data workflows, permissions and inference in the Snowflake environment outweighs the cost and catalog constraints. Consider direct Mistral for provider control, Bedrock for AWS-native operations, Databricks for Lakehouse-centric ML, or self-hosting when deployment control justifies operating the stack.
What the partnership means for Mistral
For Mistral, the deal provided another route into enterprise procurement and a place in a data platform used for analytics and application development. For Snowflake, Mistral broadened the model selection available through Cortex. The partnership and investment signal commercial alignment, but the announced terms do not reveal the investment amount, establish exclusivity, or guarantee that every future Mistral release will be added to Cortex.
Current status
As of October 3, 2026, Snowflake’s documentation located for this article lists mistral-large2, mistral-7b and mixtral-8x7b among Cortex AI model options. Availability, routing and consumption terms can vary by cloud, region and account configuration, so confirm the live model catalog before designing or pricing a production workload.
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