KServe

KServe is a Kubernetes-native platform for deploying both generative and predictive AI inference at scale, supporting OpenAI-compatible protocols and multi-framework deployment.

Use it when

• •You need a unified platform for both generative AI (LLMs) and predictive AI models.
• •You want OpenAI-compatible inference protocols for LLM deployment.
• •You're deploying models across multiple frameworks (TensorFlow, PyTorch, scikit-learn, etc.).
• •You need enterprise-scale workload handling with Kubernetes-native design.
• •You want intelligent request routing and advanced deployment options like canary deployments.
• •You require model explainability and advanced monitoring capabilities.
• •You need cost-efficient auto-scaling and request-based scaling.
• •You want native integration with Hugging Face models and GPU acceleration.
• •Your team has strong Kubernetes expertise and wants a CNCF-backed solution.

Watch out

• ⚠Large model deployment timeouts: Takes longer than 5 minutes to deploy large models, causing container termination issues.
• ⚠Auto-scaling limitations: Needs additional setup (KEDA) and doesn't support scaling to zero when idle.
• ⚠Model transition issues: InferenceServices can get stuck in "InProgress" status indefinitely.
• ⚠Multi-node/Multi-GPU limitations: Current design is insufficient for multi-node/multi-GPU use cases.
• ⚠Monitoring gaps: Lacks comprehensive built-in model monitoring tools, requiring external solutions.
• ⚠Community support: Only average community support compared to more established platforms.
• ⚠Complex setup requirements: Requires significant Kubernetes expertise.
• ⚠PyTorch engineering effort: Teams running PyTorch models require additional engineering work.
• ⚠Resource management complexity: Scaling behavior issues, especially with Prometheus metrics integration.

Available in stages

Installation

Example stacks