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Snowflake announced Claude 3.5 Sonnet inside Cortex AI on November 20, 2024, giving it a documented lead in bringing Claude into a data-platform AI product. But “beat Databricks” is only defensible under a narrow definition: Databricks also documents Claude 3.5 access, including through external-model configurations and Amazon Bedrock, and the available dated evidence does not establish when that access first launched.
What Snowflake announced in November 2024
Snowflake and Anthropic announced a multi-year partnership on November 20, 2024. The initial named model was Claude 3.5 Sonnet, which Snowflake said would be available within Cortex AI. The announcement described an initial rollout for Snowflake on AWS in select U.S. regions where Amazon Bedrock was available. It did not mean every Snowflake customer, cloud, or region received access at once. Snowflake’s announcement positioned Claude as part of Cortex experiences for working with enterprise information, including conversational analytics.
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The pitch was that customers could invoke a leading language model in the same platform where their data and governance controls already operated, rather than build a separate AI application and move data into it. Snowflake also linked the offering to Horizon Catalog controls and Cortex AI features. Those are vendor-described capabilities, not independent evidence that the integration eliminates security risks, incorrect answers, or data-quality problems.
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What “directly integrated” means—and what it does not
“Directly” describes the customer-facing integration: Claude could be accessed through Snowflake’s managed Cortex AI feature set. The original announcement tied the initial availability to Amazon Bedrock, so it does not establish that Snowflake owned Claude’s model weights or independently operated all inference infrastructure. Snowflake’s Cortex REST API documentation describes Snowflake endpoints for model access; for Claude models, the Messages API follows Anthropic’s specification.
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This distinction matters because “integrated” can mean several things: a model may be called through a platform endpoint, routed to a cloud provider, reached via an external provider endpoint, or hosted by the platform itself. Those arrangements can differ in regional availability, billing, logging, network boundaries, and operational responsibility.
Snowflake and Databricks: what the evidence supports
| Question | Snowflake | Databricks |
|---|---|---|
| Publicly documented Claude 3.5 announcement or support | Snowflake announced Claude 3.5 Sonnet in Cortex AI on November 20, 2024. | Databricks documents Claude 3.5 support, but the first announcement date is not established by the cited documentation. |
| Access route established by the cited source | Cortex AI integration; initial availability was on Snowflake on AWS in select U.S. regions where Amazon Bedrock was available. | External-model configurations and Amazon Bedrock routes are documented. |
| Does that prove the model was hosted by the platform? | No. The original announcement’s Bedrock qualification does not establish Snowflake-hosted model weights. | No. External-model support is distinct from a Databricks-hosted model; the documentation lists these as different routes. |
| Evidence one platform offered any Claude 3.5 access first | Snowflake’s public announcement date is verified. | The cited documentation does not establish an earlier or later first-access date. |
Databricks’ foundation-model documentation distinguishes external models, Amazon Bedrock access, and Databricks-hosted models available through Mosaic AI Model Serving. It also documents Claude 3.5 Sonnet function calling through an external Anthropic model configuration. That establishes Claude access in Databricks, but does not make every access route equivalent to Snowflake’s Cortex integration.
The most supportable verdict is that Snowflake was first in the documented comparison to announce a tightly integrated Claude 3.5 experience within its own data-platform AI product. The evidence does not prove that Snowflake was first to provide any Claude 3.5 access to data-platform customers. The result changes if “beats” means first native product announcement rather than first model availability by any route.
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Why a platform-level integration could matter
For an organization already using Snowflake, an integrated route can reduce the work of connecting data, model calls, and platform controls. Natural-language analysis, summarization, and multi-step workflows can be built closer to the enterprise data environment, rather than assembled around a separately operated AI application. The claimed benefit is less integration overhead and a more familiar governance and billing layer—not a guarantee that a model will produce correct SQL or reliable answers.
- Data access: Existing permissions and catalog controls may inform what a workflow can reach, but the exact behavior depends on the feature and its configuration.
- Application work: Platform features can reduce the need to build every routing and data-access component from scratch.
- Governance: Access controls and auditability do not prevent hallucinations, prompt injection, unsafe tool calls, or poor source data.
- Portability: Using a platform as the billing, governance, and application layer can make a later move to another route more involved.
What the 2024 announcement did not establish
- That Claude 3.5 was the best model for every enterprise task, or that Snowflake’s route was cheaper than direct Anthropic, Bedrock, or Databricks access.
- That Snowflake had exclusive Claude access or hosted the model weights itself.
- That all clouds, regions, editions, and customer accounts received access simultaneously.
- That customers incurred no additional Snowflake consumption charges.
- That governance controls eliminate inaccurate outputs, data-quality issues, or application-level safety risks.
- That Databricks lacked Claude integration; its documentation lists Claude 3.5 access through external-model and Bedrock routes.
Availability and cost need account-level checking
Before committing to a route, confirm the exact model, region, cloud, account eligibility, release stage, and any partner-AI settings. Model catalogs and identifiers change; Claude 3.5 is a historical milestone, not a safe assumption about the model currently offered for a new workload. Databricks’ current documentation notes that Claude 3.7 Sonnet is no longer available, illustrating how quickly a catalog can change.
Snowflake bills Cortex AI through consumption-based units. Its pricing documentation gives an example of $3 per platform credit and $2 per AI credit for an Enterprise Edition account with global routing; those are example assumptions in the documentation, not a universal quote. Snowflake also publishes model-specific consumption rates, which can change. Check the current Cortex pricing documentation and consumption table for the applicable account and model.
A meaningful cost comparison must normalize input and output tokens, cached tokens, tool or agent calls, platform and warehouse compute, data transfer, and any minimum commitments or cloud-provider charges. A credit rate cannot be compared directly with an API token price unless the billing unit and included infrastructure are aligned.
How the partnership and product landscape changed
The original 2024 announcement should not be read using later availability as though it existed on launch day. On December 3, 2025, Snowflake and Anthropic announced a $200 million, multi-year partnership expansion. The companies described Claude access for Snowflake customers across Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Azure. Snowflake’s partnership announcement and Anthropic’s account describe that broader arrangement.
In June 2026, Snowflake said Claude was integrated into Cortex AI across major cloud platforms and referenced products including Cortex Code and Snowflake Intelligence. That later statement reflects an expanded relationship, not the narrower AWS-and-Bedrock conditions of November 2024. See Snowflake’s June 2026 update.
Databricks also expanded its Claude support after the 3.5 era. Its release notes document Claude 4 models as Databricks-hosted Foundation Model APIs in U.S. regions in May 2025, Claude Haiku 4.5 in December 2025, and Claude Sonnet 4.6 and Opus 4.6 in February 2026. These dated entries show continued product development, but they do not retroactively settle which platform first offered Claude 3.5 access. See the May 2025, December 2025, and February 2026 release notes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which route should a buyer evaluate?
Snowflake Cortex AI
Start with Cortex if enterprise data and governed analytics already center on Snowflake, and teams want SQL-oriented analytics, agents, or REST API access within that environment. It is less compelling if workloads are primarily Spark- and ML-pipeline-oriented, the organization does not use Snowflake, or provider-neutral inference is a priority. Confirm required regional availability and how Cortex consumption maps to the account’s bill.
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Evaluate Databricks when data engineering, ML pipelines, and serving workflows already run there, especially if teams depend on Spark, MLflow, Unity Catalog, or Mosaic AI. Compare external-model configuration, cloud-provider routing, and hosted-model APIs separately; they are not interchangeable deployment choices. The cited materials establish pay-per-token access for some hosted foundation models but do not give a complete current Claude price card across models and regions.
Cloud-provider model services
Amazon Bedrock, Google Cloud Vertex AI, or Microsoft Azure may be a lower-friction fit when procurement, security controls, and AI operations are already standardized on that cloud. This can keep the model-service choice more independent of Snowflake or Databricks, though platform-specific analytics and governance features still have to be integrated.
Anthropic’s API
Direct API access suits application teams that want control over Claude integration and portability across data platforms. The trade-off is building more of the surrounding enterprise control plane—authorization, retrieval, observability, safety measures, and procurement workflows—rather than relying on a data platform to provide it.
For a buyer, the key question is usually not which platform won a 2024 announcement race, but where data, governance, billing, and AI operations already live. Snowflake’s lead was meaningful as a first-party product-positioning move; it was not proof of exclusive access, lower cost, or universal Claude availability.
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