Releasesby IbisRate Limits for Runner and App Management
Update11 days agoUnverifiedAdded Oct 17, 2026
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
Topics: Developer tools
More fal releases
Every fal releaseKey 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.
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} .
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
Platform 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...
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 .
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 .
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.
Also shipped on Oct 13, 2026
fal in October 2026What's included:
- Three training methods: Supervised fine-tuning (SFT) , direct preference optimization (DPO) , and reinforcement learning with group relative policy optimization (GRPO) . Each method trains a low-rank adapter (LoRA) or the full model weights. Custom loss functions : Train with any objective you compute in PyTorch from the model's log-probabilities.
The AI.AGG function is generally available (GA)
The AI.AGG function is generally available (GA). You can use this function to semantically aggregate unstructured input data based on natural language instructions.
BigQuery generative AI functions now support the gemini-4-argon model
BigQuery generative AI functions now support the gemini-4-argon model. This model is generally available .
Gemini 4 Argon in CodeMender (Preview)
CodeMender now supports Gemini 4 Argon. To use it, pass --model gemini-4-argon to cm find , cm verify , or cm fix . Gemini 3.8 Flash remains the default model. For more information, see Specifying the model .
Gemini 4 Argon and Gemini 4 Argon Cyber Permissive
Gemini 4 Argon ( gemini-4-argon ) is available in General Availability (GA) . Gemini 4 Argon is Google's frontier model that excels at deep work in complex, real-world tasks across domains including coding, finance, legal, cybersecurity, and science.
On-demand state repartitioning is now generally available
On-demand state repartitioning is now generally available. You can resize the number of shuffle partitions for a stateful Structured Streaming query or Lakeflow pipeline without losing checkpoint state. See On-demand state repartitioning for stateful streaming queries .