All release notes

• Openflow BYOC: per-runtime cost attribution in Account Usage views

You can now configure a Cortex Search Service to automatically suspend its serving compute after a period of query inactivity and resume when it receives a new query. This helps reduce serving costs on idle services without requiring manual intervention.

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You can now specify tuple values for ingress private endpoint identifiers in network rules. This feature allows PrivateLink identifiers such as AWSVPCEID, AZURELINKID, and GCPPSCID to use tuple syntax that combines endpoint identifiers with IP addresses and/or CIDR ranges.

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The following new features and enhancements are now available in Snowflake Data Clean Rooms:

• Activity history in collaborations: You can now use VIEW_ACTIVITY_HISTORY to audit analysis activity in a collaboration. Each collaborator sees only the activity relevant to their account and the resources they contribute. This feature is available only in collaborations created after this update. For details, see Monitor analysis activity in collaborations.
• Collaborators can clean up activation segment records: For collaborations created after this update, the CLEANROOM_PUBLIC_ROLE has permission to delete data from the ACTIVATION.SEGMENT_RECORDS table, allowing collaborators to clean up segment data after activation jobs complete.
• General performance improvements and bug fixes.

Support for version 3 (v3) of the Apache Iceberg™ table specification is now generally available.

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Listing share restrictions are now generally available. Providers can enable share restrictions on private external listings to prevent consumers in lower editions or non-HIPAA accounts from accessing their data.

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Organization administrators can now change the Snowflake edition of an account in the organization by using ALTER ACCOUNT SET EDITION = { 'STANDARD' | 'ENTERPRISE' | 'BUSINESS_CRITICAL' }. Run the command from an account other than the account whose edition you're changing.

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The ORGANIZATION_USAGE schema now includes three new premium views that are available in the organization account. These views provide visibility into network policies and network rules across all accounts in your organization.

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When defining a semantic view, you can now define a fact or dimension as a filter by specifying LABELS = (FILTER) in the create semantic view command. A filter is a fact or dimension that resolves to a BOOLEAN value and can be used as a condition in the WHERE clause of a query.

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You can now assign tags to semantic views and the attributes within them, including logical tables, dimensions, facts, and metrics. You can use tags to track sensitive data, manage governance policies, and organize your semantic view objects.

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With this release, four new instance families are generally available for Snowpark Container Services:

• GEN_X64_G2 (AWS, Azure): Second-generation general-purpose compute, replacing CPU_X64 as the recommended instance family for all new workloads.
• MEM_X64_G2 (AWS, Azure): Second-generation high-memory compute, replacing HIGHMEM_X64 as the recommended instance family for memory-intensive workloads.
• GPU_L40S (AWS): NVIDIA L40S GPU (Ada Lovelace) with 48 GB VRAM per GPU, scaling up to 8 GPUs (384 GB total).

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• Apply data quality checks by group (General availability)
• New columns in DMF results and references views

The SAP® BDC Zerocopy Connector is now generally available, enabling zero-copy data sharing between SAP® Business Data Cloud and Snowflake without moving or duplicating data.

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Snowflake Cortex AI_COMPLETE now enables advanced multimodal analysis of video and audio content in public preview, allowing organizations to extract brand, sentiment, and behavioral insights from rich media directly in Snowflake. This unlocks powerful new capabilities for marketing, content intelligence, and compliance workflows, without moving data outside of Snowflake.

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The Snowflake Cortex AI_PARSE_DOCUMENT function has launched significant quality improvements for OCR mode, building on the strong OCR performance already available in AI_PARSE_DOCUMENT and delivering even higher accuracy and faster processing for enterprise-scale document workflows that require efficient, high-quality text extraction.

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With this release, autoscaling policies for Snowpark Container Services are now generally available. Autoscaling policies extend the existing CPU-based autoscaling by letting you define metric-based rules that control how Snowflake scales service instances up, scales them down, or suspends them automatically. Policies support Snowflake-provided platform metrics and custom metrics that your service emits, giving you fine-grained control over scaling behavior based on CPU utilization, memory usage, ingress connection rate, and more.

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The ORGANIZATION_USAGE schema now includes a new premium view: outbound privatelink endpoints. The view is available in the organization account. You can use this view to monitor outbound private connectivity endpoints across all accounts in your organization.

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You can now authenticate SCIM API requests with the following types of tokens:

• Programmatic access tokens (PATs)
• OAuth tokens obtained from a third-party identity provider (External OAuth)

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The Snowflake Cortex AI_PARSE_DOCUMENT function now supports processing documents of up to 2,000 pages in both LAYOUT and OCR modes, enabling organizations to efficiently handle large, multi-page documents such as healthcare records, insurance claims, regulatory filings, and technical manuals in a single request with reduced pre-processing and orchestration.

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High-fidelity image extraction with the Snowflake Cortex AI_PARSE_DOCUMENT function using LAYOUT mode is now generally available, enabling organizations to unlock visual data from documents and build richer, multimodal document processing workflows. By extracting images alongside text and layout, teams can incorporate visual context from documents such as healthcare records, financial reports, and regulatory filings, without additional tooling or pipelines.

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When you work with identifiers inside a catalog-linked database that uses CASE_INSENSITIVE mode (the default for case-insensitive catalogs like AWS Glue and Unity Catalog), you no longer need to surround schema, table, and column names in double quotes for DDL commands. Identifiers outside the catalog-linked database continue to follow standard Snowflake identifier resolution rules.

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The following new features and enhancements are now available in Snowflake Data Clean Rooms:

• Creation timestamps for registry objects: VIEW_REGISTERED_DATA_OFFERINGS, VIEW_REGISTERED_TEMPLATES, and VIEW_REGISTERED_CODE_SPECS now also return the CREATED_ON timestamp, so you can see when the object was first created.
• Data Clean Rooms UI in Snowsight (public preview). You can now manage Data Clean Rooms collaborations directly in Snowsight. The UI lets you browse, create, join, edit collaborations, and run analysis. The UI also integrates with Cortex Code for natural-language assistance. Learn more.

With this preview release, you can create tags in Snowsight by opening Governance & security » Tags & policies, selecting the Tags tab, and selecting + Create tag.

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The following Trust Center capabilities are now generally available:

• Detection findings: A new finding type to surface anomalous and potentially suspicious events. For more information, see trust center detections.
• Event-driven scanners: scanners that generate detection findings in response to relevant events, such as changes to sensitive parameters or sign-ins from unusual IP addresses, in addition to the existing schedule-based scanners. For more information, see trust center scanners event driven.
• New scanners in the Threat Intelligence scanner package that report detection findings:

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With this release, app configuration is now generally available for Snowflake Native Apps. Providers can define configurations that request values from consumers at installation or runtime, covering application names for inter-app communication, free-form strings, and secret-based authorization. Consumers provide values through Snowsight, the Python Permission SDK, or SQL.

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With this release, inter-app communication is now generally available for Snowflake Native Apps. Inter-app communication allows a Snowflake Native App to provide additional functionality to other apps in the same consumer account by providing access to objects that other apps can use.

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• Programmatic access tokens: role restrictions and blocked roles

• COUNT with DISTINCT in window functions (General availability)

• New ACCOUNT_USAGE.TASKS view

With this release, the Data Security experience in the Trust Center is generally available in Snowsight.

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Storage lifecycle policies now support archival on Google Cloud:

• Google Cloud COOL and COLD tiers: You can now create archival policies that use either the COOL or COLD storage tier on accounts hosted on Google Cloud.

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The following new features and enhancements are now available in Snowflake Data Clean Rooms:

• Improved Visibility for Freeform SQL Data Offerings: Data providers can now view Freeform SQL View names and column policies for their data offerings using VIEW_DATA_OFFERINGS.
• Updates to private preview features.

Snowpipe Streaming with high-performance architecture is now generally available in the China region.

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You can now use the SYSTEM$GET_STAGE_PRIVATELINK_AUTHORIZED_ENDPOINTS function to view the private connectivity sources that are authorized to access the internal stage of your account. The function is supported on Microsoft Azure and Google Cloud, and helps administrators verify which Microsoft Azure private endpoints or Google Cloud VPC networks have been authorized through previous calls to system authorize stage privatelink access.

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• Session policies: maximum session lifespan

• Schema-level data metric functions (General availability)
• New columns in DATA_METRIC_FUNCTION_REFERENCES

• Dynamic tables: expanded outer join support
• ACCUMULATE aggregate function

Cortex AI Guardrails are now generally available. Cortex AI Guardrails, part of Snowflake Horizon Catalog, provide run-time protection against prompt injection and jailbreak attacks on Cortex Code.

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Fine-tuning arctic-extract models is now generally available. You can adapt the model to your documents and domains for more consistent structured extraction, and run inference with AI_EXTRACT using your fine-tuned model.

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With this release, Snowflake bidirectionally syncs table and column descriptions between your external Iceberg REST catalog and your catalog-linked database. Sync can update a description to a new value, but never replaces a non-empty description with an empty one.

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The Snowflake Connector for Kafka version 4.0 is now generally available. Version 4.0 is a ground-up rewrite built on Snowflake's high-performance Snowpipe Streaming architecture, delivering up to 10 GB/s throughput per table with 5 to 10 second end-to-end latency, and exactly-once and ordered delivery semantics.

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This release updates the Performance Explorer experience in Snowsight:

• A tabbed layout with Queries, Warehouses, and Tables pages so you can easily navigate between metric areas.
• Filter presets to save your current filter settings (time period, warehouse, database, and role filters) and set a default preset for future visits.
• Export the data in the detail tables in the side panels as a CSV file.

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The following updates are now available in Snowflake Data Clean Rooms:

• General performance improvements and bug fixes.
• Updates to private preview features.

Snowflake can now use primary keys in dynamic tables to track row-level changes and enable incremental refresh downstream of full-refresh dynamic tables. This release includes the following capabilities:

• Base table-defined primary keys: When a base table has a primary key with the RELY property, Snowflake uses it for change tracking in downstream dynamic tables. This is especially useful when the base table is periodically rewritten through INSERT OVERWRITE, which normally prevents change tracking across table versions.
• Query-derived primary keys: Snowflake automatically derives primary keys from the query definition of a dynamic table. Queries with GROUP BY clauses or QUALIFY ROW_NUMBER() = 1 filters produce unique constraints that Snowflake relies on for change tracking.
• Incremental refresh on full-refresh dynamic tables: Dynamic tables in incremental refresh mode can now read from upstream dynamic tables that use full refresh mode, as long as the upstream table has a system-derived primary key. To use this capability, set REFRESH_MODE = INCREMENTAL explicitly on the downstream dynamic table.

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Provider-side support for consumer-controlled maintenance policies is now in public preview for Snowflake Native Apps.

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Snowflake documentation is now easier for AI coding assistants, agents, and large language models (LLMs) to discover and consume.

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Cortex Search request monitoring is now available in public preview.

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Replication of Cortex Search Services is now generally available. You can replicate Cortex Search Services from a source account to one or more target accounts in the same organization using replication or failover groups.

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With this preview release, you can create Apache Iceberg™ tables that use Snowflake storage. This option lets Snowflake store and manage the Iceberg table files for you, so you don't need to set up access to external cloud storage.

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Cortex Agents that use Cortex Analyst semantic views as tools now generate SQL directly, rather than delegating SQL generation to the Cortex Analyst service as a separate step. This results in improved accuracy and lower latency for analytical queries.

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Dynamic Apache Iceberg™ tables now support the following table properties:

• PARTITION BY: Partition the table using Iceberg partition expressions such as identity, bucket, truncate, year, month, day, and hour transforms.
• TARGET_FILE_SIZE: Control the target Parquet file size for table writes. Defaults to AUTO, which lets Snowflake choose the optimal file size.
• PATH_LAYOUT: Choose between a flat or hierarchical (Hive-style) directory layout for data files. Use HIERARCHICAL together with PARTITION BY to write data to partition-aware paths.

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