skypilot
SkyPilot: Manage all your AI compute.
Decision gist · record as of 2026-08-14
Yes, if you run AI workloads across multiple infrastructure providers or manage shared GPU clusters. The unified interface eliminates vendor lock-in and simplifies multi-cloud scheduling. The active maintenance, permissive license, and large community (10498 stars) make it low-risk. Install friction is low. No security vulnerabilities are known. Best suited for teams with heterogeneous compute environments; less critical if locked into a single cloud or cluster.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires cloud credentials or access to Kubernetes/Slurm clusters to actually provision and run workloads; the package itself installs without system dependencies, but GPU or compute resource access is needed for typical use.
- Low install friction with a pure Python wheel distribution.
- The package carries 50 runtime dependencies including major frameworks (fastapi, pydantic, pandas, networkx) and utilities, but all are standard PyPI packages.
License · maintenance · safety
Apache 2.0 (permissive) — Licensed under Apache 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions. You may use this in proprietary projects provided you include a copy of the license and note any modifications.
last release 2026-07-22 (23 days) · last repo commit 2026-08-14 · 10,498 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,778,062 downloads/mo, #3,570 on PyPI
Alternatives
Verify before relying
pip install 'skypilot[kubernetes,aws,gcp,azure]'
# Define a task in YAML or Python, then launch via CLI
# sky launch my_task.yaml- Whether the 50 runtime dependencies are all required for basic use or if many are optional for specific cloud/infrastructure backends
- Performance characteristics and overhead when scheduling across multiple clouds or clusters simultaneously
- Specific version constraints or compatibility issues with the listed Python versions (3.9–3.13)
What it is and what it does
SkyPilot is a unified control plane that abstracts away differences between cloud providers, Kubernetes clusters, Slurm systems, and on-premises infrastructure. You write your job specification once in YAML or Python, and the system handles finding available resources, provisioning compute, syncing code, running setup commands, and executing your workload—all without vendor lock-in. It's designed for AI teams who need to run training, inference, or development workloads across heterogeneous infrastructure, and for infrastructure teams managing shared clusters who want advanced scheduling, multi-cluster orchestration, and resource utilization optimization.
The package includes job queuing, auto-recovery, gang scheduling for multi-node jobs, intelligent bin-packing on shared clusters, and automatic cleanup of idle resources. It supports GPUs, TPUs, and CPUs across multiple cloud providers and on-premises systems. The core abstraction is a task specification that declares resource needs, setup steps, and commands to run; the system then finds the cheapest or most available infrastructure and handles the rest.
Use it for
- Launch distributed training jobs on the cheapest available GPU infrastructure across multiple clouds without rewriting job code.
- Manage a shared Kubernetes cluster for an AI team with automatic scheduling, multi-node job support, and resource binpacking to maximize utilization.
- Run hyperparameter sweeps or experiment grids in parallel across reserved GPUs, Slurm clusters, and cloud instances from a single command.
- Develop and test AI models locally, then scale to production infrastructure by changing only the resource specification, not the code.
- Unify job submission across on-premises Slurm, internal Kubernetes, and cloud providers so teams use one interface regardless of where compute lives.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you run AI workloads across multiple infrastructure providers or manage shared GPU clusters.
The unified interface eliminates vendor lock-in and simplifies multi-cloud scheduling. The active maintenance, permissive license, and large community (10498 stars) make it low-risk. Install friction is low. No security vulnerabilities are known. Best suited for teams with heterogeneous compute environments; less critical if locked into a single cloud or cluster.
Install
skypilot on PyPI
Before you install
Low install friction with a pure Python wheel distribution. The package carries 50 runtime dependencies including major frameworks (fastapi, pydantic, pandas, networkx) and utilities, but all are standard PyPI packages. Maintenance is active with a recent release 23 days ago and ongoing commits; the repository has 10498 stars and has been maintained since August 2022.
Requires cloud credentials or access to Kubernetes/Slurm clusters to actually provision and run workloads; the package itself installs without system dependencies, but GPU or compute resource access is needed for typical use.
License in practice
Licensed under Apache 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions. You may use this in proprietary projects provided you include a copy of the license and note any modifications.
Quickstart
pip install 'skypilot[kubernetes,aws,gcp,azure]'
# Define a task in YAML or Python, then launch via CLI
# sky launch my_task.yamlVerify before relying
- Whether the 50 runtime dependencies are all required for basic use or if many are optional for specific cloud/infrastructure backends
- Performance characteristics and overhead when scheduling across multiple clouds or clusters simultaneously
- Specific version constraints or compatibility issues with the listed Python versions (3.9–3.13)
Package facts
| License | Apache 2.0 permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 50 packageswheelsetuptoolspipcachetoolsclickcoloramacryptographyjinja2jsonschemanetworkxpandaspendulumPrettyTablepython-dotenvrichtabulatetqdmtyping_extensionsfilelockpackagingpsutilpulppyyamlijsonorjsonrequestsuvicornfastapipydanticpython-multipart |
| Maintenance | Actively maintained 23 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,778,062 / month, #3,570 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9Topic :: Software Development :: Libraries :: Python ModulesTopic :: System :: Distributed Computing |
Evidence: skypilot-0.13.0-py3-none-any.whl
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See also dstack · skypilot-nightly · aws-parallelcluster · torchx · clearml-agent · deepspeed · outerbounds · kcli · mooncake-transfer-engine-cuda13 · prime