SkyPilot 0.11: Multi-Cloud Pools for Batch Inference, Fast Managed Jobs, Enterprise-Ready at Scale, Programmability

We are excited to bring you SkyPilot 0.11! This release introduces Pools for batch inference across clouds and Kubernetes, brings Managed Jobs Consolidation Mode to GA with 6x faster submission, and delivers enterprise-ready improvements supporting hundreds of AI engineers on a single API server instance.

Get it now:

Or upgrade your team SkyPilot API server:

[Beta] Pools: Batch inference across clouds & Kubernetes #

SkyPilot now supports spawning a pool that launches workers across multiple clouds and Kubernetes clusters. Jobs can be scheduled on this pool and distributed to workers as they become available.

Key benefits include:

Fully utilize your GPU capacity across clouds & Kubernetes
Unified queue for jobs on all infrastructure
Keep workers warm, scale elastically
Step aside for higher priority jobs; reschedule when GPUs become available

Learn more in our blog post, documentation, and example.

[GA] Managed Jobs Consolidation Mode #

Consolidation Mode is now generally available, which allows the jobs controller to collocate with the API server, delivering:

6x faster job submission
Consistent credentials across the API server and jobs controller
Persistent managed jobs state on PostgreSQL

Enable it in your configuration:

We've also optimized the Managed Jobs controller to handle 2,000+ parallel jobs on a single 8-CPU controller - an 18x improvement in job capacity.

Enterprise-ready SkyPilot at large scale #

SkyPilot 0.11 brings significant enterprise improvements (API server docs), enabling support for hundreds of AI engineers with a single SkyPilot API server instance.

SSO support with Microsoft Entra ID

Secure your SkyPilot deployment with enterprise single sign-on (auth docs):

Memory and performance improvements

We’ve made significant reductions in memory consumption with OOM avoidance for the API server, along with CLI/SDK/Dashboard speedups when handling large amounts of clusters and jobs:

Comprehensive API server metrics

Monitor your SkyPilot deployment with detailed operational metrics:

Kubernetes improvements #

SkyPilot 0.11 delivers robust Kubernetes support (docs):

Robust SSH for SkyPilot clusters on Kubernetes
Improved resource cleanup after termination
Intelligent GPU name detection
Retry on transient Kubernetes API server issues
Improved volume support with labels, name validation, and SDK support (volumes docs)

Existing PVC support: reference pre-existing Kubernetes PersistentVolumeClaims as a SkyPilot volume:

Ephemeral volumes: automatically create volumes when a cluster launches and delete them on teardown - ideal for temporary storage like caches and intermediate results:

CoreWeave and AMD GPU support #

CoreWeave now officially supports SkyPilot with Infiniband support, object storage, and autoscaling (docs, example). See the CoreWeave blog for details.

AMD GPUs are fully supported on Kubernetes clusters with GPU detection and scheduling, dashboard metrics, and ROCm support (docs, example). See the AMD ROCm blog for details.

User Experience #

SkyPilot templates

SkyPilot now ships predefined YAML templates for launching clusters with popular frameworks. Templates are automatically available on all new SkyPilot clusters.

Launch a multi-node Ray cluster with a single line:

Improved Python SDK

The SkyPilot Python SDK (docs) is significantly improved with:

Type hints for better IDE support and code completion:

Log streaming for real-time job monitoring:

Admin policy helpers (docs) for building policies programmatically:

GPU count in setup: New SKYPILOT_SETUP_NUM_GPUS_PER_NODE environment variable available during setup phase for configuring software based on GPU count (env vars docs).

CI/CD integration

With the improved SDK, you can integrate SkyPilot with GitHub Actions and other orchestrators to automatically spin up your AI workloads:

Native Git support

Use private git repositories directly as your SkyPilot workdir (docs). SkyPilot handles cloning and syncing automatically:

You can also use --git-url and --git-ref options with sky serve up. View commit hashes directly in the Dashboard.

Autostop based on SSH sessions

Configure autostop/autodown to wait for active SSH sessions in addition to running jobs (docs):

Distributed LLM examples

We released high-performance distributed training examples with checkpointing support:

Kimi-K2 multi-node serving (docs, example)
Torchtitan framework support (docs, example)
Llama 4 Maverick 400B training on 16+ H200 GPUs (docs, example)
OpenAI GPT-OSS pretraining and finetuning (docs, example)
VeRL for agentic reinforcement learning (docs, example)

Other improvements #

Air-gapped deployments supported via private container registries in Helm charts
Resolved OOM issues with the API server
Make Pod status visible during SkyPilot cluster launching on k8s
Post-provision commands on Kubernetes with post_provision_runcmd for custom pod initialization
Fixed --retry-until-up to properly retry failed launches across all cloud zones
GCP B200 spot instances now supported for cost-effective access to latest NVIDIA GPUs
AWS Trainium & Inferentia dynamic accelerator detection via AWS API
Together AI instant cluster support for fast GPU access (docs)
Seeweb cloud provider with Docker image support (docs)

Many more improvements and fixes in the full release notes.

Get started today #

SkyPilot 0.11 makes running AI workloads across clouds and Kubernetes more efficient than ever. With Pools for batch inference, faster managed jobs, and enterprise-scale improvements, you can focus on building AI while SkyPilot handles the infrastructure.

Install SkyPilot 0.11: