Releasesby GlyphPlatform MCP Server
fal's second MCP server , the Platform API MCP , is now available at https://api.fal.ai/v1/mcp/platform . Where the Run MCP lets your AI assistant build with models , this one lets it operate your fal account : connect Claude Code, Cursor, or any MCP-compatible client with your API key and ask "why did my last request to my-app fail?" , your assistant walks...
Also shipped Aug 17, 2026fal in August 2026 →
AI mode for sensitive data classification (Public Preview)
Sensitive data classification now supports AI mode in public preview. When you enable AI mode on a classification profile, Snowflake uses the OpenAI GPT-5 Mini model to identify additional semantic categories beyond those detected by standard classification, which can improve accuracy.
New SNOWFLAKE_COCO_USAGE_HISTORY view in Account Usage and Organization Usage (General availability)
The SNOWFLAKE_COCO_USAGE_HISTORY view is now generally available in the ACCOUNT_USAGE and ORGANIZATION_USAGE schemas. It provides a unified view of usage history for CoCo across the CLI, Desktop, and Snowsight interfaces. The information in the view includes the number of credits consumed each time a user interacts with CoCo.
Upcoming deprecation of the data quality monitoring downstream_impact column
The downstream_impact column in the data quality monitoring results system table is being deprecated. Deprecation begins September 7, 2026, and rolls out gradually across regions over the following week. Until the rollout reaches your workspace, you might still be able to query downstream_impact .
Upcoming deprecation of the data quality monitoring root_cause_analysis column
The root_cause_analysis column in the data quality monitoring results system table is being deprecated. Deprecation begins August 18, 2026, and rolls out gradually across regions over the following several days. Until the rollout reaches your workspace, you might still be able to query root_cause_analysis .
Session restore for serverless jobs is now in Beta
Restore the Python variables and Spark session from a serverless compute job run into a new interactive notebook, so you can debug a failure or explore results without rerunning the job. Databricks automatically captures a state snapshot when an eligible run fails, runs 30 minutes or longer, or is cancelled, and keeps it for 7 days after the run completes.
Identity attributes in ABAC column mask policies (Beta)
Identity attributes are in Beta. You can now write Unity Catalog ABAC column mask policy conditions that target users by attributes synced from your identity provider, such as department or country , in addition to group membership.
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GPU Utilization in Runner Telemetry
Runner telemetry now includes a dedicated GPU Utilization chart alongside the existing CPU, memory, and VRAM charts, so you can see how much compute your GPUs are actually doing -- not just how much memory they hold.
Deploy Messages and Annotations
You can now attach a freeform message and custom annotations to each revision when deploying: Messages and annotations are set at deploy time and stay with the revision. View and search them on the app's Versions page in the dashboard, or fetch them via the revisions API .
Switch Between App Endpoints in the Dashboard Playground
Deployed Serverless apps now include Testing > Playground in the app dashboard. Select an available endpoint, fill in its generated input form, and run a request without leaving the app. Multi-endpoint apps have one place to test their endpoints, using the existing Playground authentication and billing behavior. Open the guide
Billing Headers for WebSocket Endpoints
Shared WebSocket endpoints can now declare billing on their 101 upgrade response. Set x-fal-billable-units when the unit count is known at connect time. Set x-fal-billable-units-webhook to 1 when the count is only known at the end of the session. Then report the total with POST /requests/billable-units/{request_id} .
App-Level Retry Configuration
Apps can now register a default per-condition retry budget at deploy time with the new retry_config deployment option, using the same format as the X-Fal-Retry-Config request header. Set it as a class attribute on fal.App or under the app's table in pyproject.toml .
Key Presets and Custom Policies
Keys now use permission presets instead of the ADMIN and API scopes. When you create a key, select one of seven presets: API , SERVERLESS , DEPLOY , COMPUTE , BILLING , READONLY , or FULL . If no preset matches your case, select the exact permissions the key needs. You can also limit a key to specific models and set it to expire.
Rate Limits for Runner and App Management
fal now limits how often you can list, inspect, shell into, stop, and kill apps and runners. The limits apply to the CLI, the Python SDK, and the REST API. When you exceed a limit, the REST API returns 429 Too Many Requests , and the CLI and SDK return the gRPC status RESOURCE_EXHAUSTED . Learn more about the limits
New Serverless Usage Page
The new Serverless Usage page (Serverless → Usage) provides attributable, dimensioned reporting of serverless compute spend in the dashboard. Compute reported in machine-seconds , broken down per app, environment, and machine type. Spend is split by price category: Reserve, Burst, On-Demand, and List.