Releasesby GlyphDataflow now supports NVIDIA RTX Pro 6000 GPUs
Dataflow now supports NVIDIA RTX Pro 6000 GPUs. You can use this GPU model to run your Apache Beam pipelines on Dataflow. RTX Pro 6000 GPUs are recommended for large, medium, and small model inference workloads. To configure your workers with this GPU model, set the accelerator type to nvidia-rtx-pro-6000 .
Also shipped Jun 16, 2026Google Cloud in June 2026 →
How to Use Claude Code with OpenRouter
Claude Code now supports routing through OpenRouter, providing budget controls and provider failover for Anthropic models.
How to Use OpenRouter With Any Coding Agent or AI Tool
Any tool using the OpenAI API can now integrate with OpenRouter by updating the base URL and API key.
Subagent: Let Your Model Delegate the Busywork
The new subagent tool allows frontier models to delegate self-contained tasks to smaller, faster worker models during generation.
Draft mode for V8.1 and new feature previews
Hi everyone - a few updates today! Draft mode for V8.1 Each draft mode generation creates 24 images at a lower resolution and quality. Click "Vary" on images you'd like to render at full-quality and full-resolution Draft jobs use half as many fast hours as V8.1 SD jobs (even though they generate 24 images!) Turn on draft mode by clicking the ⚡ button in the...
Redesigned Usage & Billing Page
The Usage & Billing page has been rebuilt to support usage attribution (the Settings "Overview" tab is now "Usage"). Filter by auth method, API key, and login username to attribute consumption to a specific credential or user. Normalized app names and revised charts improve readability and export.
Annotations clustering now generally available
Recently, we introduced several improvements to annotations to make them easier to explore, navigate, and manage at scale, including annotations clustering and indicator controls. These changes were introduced in public preview and now they are generally available. For more information about these changes, refer to the original announcement .
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Dataflow has updated and expanded its pipeline update features for streaming jobs: - Automated...
Dataflow has updated and expanded its pipeline update features for streaming jobs: Automated stop-and-replace updates : You can perform automated, declarative stop-and-replace updates to streaming jobs. Parallel updates with the same job name : When you perform automated parallel updates, you can use the same job name for the new replacement job.
You can now use Hyperdisk Balanced disks for Dataflow worker VMs
You can now use Hyperdisk Balanced disks for Dataflow worker VMs. With Hyperdisk Balanced disks, you can provision IOPS and throughput independently of disk size by using the diskProvisionedIOPS and diskProvisionedThroughput pipeline options (Java SDK) or disk_provisioned_iops and disk_provisioned_throughput_mibps pipeline options (Python and Go SDKs).
You can now pause a Dataflow batch job using the pause_on_failure service option
You can now pause a Dataflow batch job using the pause_on_failure service option. This feature lets you preserve the state of your batch pipeline job, address external issues, and resume processing without losing completed work. You can use this option to automatically pause a job on failure, or use this feature to manually pause a job when you chose to.
Dataflow job builder now supports external Iceberg REST Catalogs as a source
Dataflow job builder now supports external Iceberg REST Catalogs as a source. You can now ingest data from external Apache Iceberg REST catalogs (IRC) directly into Lakehouse tables using Dataflow's job builder UI without writing code. For more information, see Import data from external Iceberg catalogs to Lakehouse using Dataflow .
When you use min_ram or cpu_count resource hints for pipeline steps that don't require accelerators, Auto VM...
When you use min_ram or cpu_count resource hints for pipeline steps that don't require accelerators, Auto VM Selection (Instance Flexibility) is enabled automatically. With Auto VM Selection, workers are provisioned from a curated list of machine types that meet your RAM and CPU requirements.
Dataflow support for the C4A machine series of Arm processors is now generally available
Dataflow support for the C4A machine series of Arm processors is now generally available. Arm-based VMs are optimized for power efficiency and can provide improved price-performance for many workloads. For more information, see Use Arm VMs on Dataflow .
Dataflow Auto VM Selection now supports 4th generation Compute Engine machine series
Dataflow Auto VM Selection now supports 4th generation Compute Engine machine series. When Auto VM Selection is enabled, the service prioritizes these newer generation VMs for improved performance.
Dataflow now supports streaming high availability for Streaming Engine jobs ( Preview )
Dataflow now supports streaming high availability for Streaming Engine jobs ( Preview ). When you enable the requires_high_availability service option, Dataflow replicates backend state across multiple zones in a region to protect your job from single-zone failures. For more information, see Streaming high availability .