Amazon SageMaker
Fully managed machine learning service to build, train, and deploy ML models at scale
KServe
Unified AI inference platform for generative and predictive workloads on Kubernetes
KServe delivers scalable, multi‑framework AI inference on Kubernetes, supporting LLMs, GPU acceleration, model caching, autoscaling, explainability, and cost‑efficient serverless deployments.
Overview
Overview
KServe is a Kubernetes‑native platform that consolidates generative and predictive AI inference into a single service. It enables data‑science and MLOps teams to deploy large language models, TensorFlow, PyTorch, XGBoost, ONNX and other frameworks with a consistent API, while leveraging Kubernetes and Knative for reliability and scalability.
Capabilities
The system offers GPU‑accelerated serving, KV‑cache offloading, intelligent model caching, and request‑based autoscaling that can scale to zero for cost savings. Advanced routing lets you compose predictors, transformers, and explainers into inference pipelines, support canary rollouts, and monitor drift or adversarial inputs. Integration with Hugging Face and OpenAI‑compatible endpoints simplifies LLM deployment, and built‑in explainability tools provide feature attribution for predictive models.
Deployment
KServe can be installed as a lightweight standalone component, with Knative for serverless features, or alongside ModelMesh for high‑density, high‑scale serving. It is an incubating CNCF project and integrates tightly with Kubeflow, making it suitable for both cloud and on‑premise Kubernetes clusters.
Highlights
LLM‑optimized inference with OpenAI‑compatible API
GPU‑accelerated serving with KV‑cache offloading
Multi‑framework support and intelligent routing
Request‑based autoscaling with scale‑to‑zero
Pros
- Unified platform for generative and predictive AI
- Native Kubernetes and Knative integration
- Extensive framework and model support
- Built‑in autoscaling and cost‑saving features
Considerations
- Complexity may increase for small‑scale deployments
- Serverless features require Knative installation
- Advanced capabilities need deeper Kubernetes expertise
- Limited support for non‑Kubernetes environments
Managed products teams compare with
When teams consider KServe, these hosted platforms usually appear on the same shortlist.
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Fit guide
Great for
- Enterprises needing scalable AI inference across multiple frameworks
- Teams deploying large language models with GPU acceleration
- MLOps pipelines requiring model explainability and traffic management
- Organizations aiming to reduce inference costs via scale‑to‑zero
Not ideal when
- Simple, single‑model deployments on bare‑metal without Kubernetes
- Projects lacking Kubernetes or Knative expertise
- Use cases requiring on‑premise inference outside container orchestration
- Environments where licensing restrictions prevent Apache‑2.0 usage
How teams use it
Real‑time LLM chat service
Delivers low‑latency responses with GPU acceleration, KV‑cache offloading, and autoscaling to handle variable traffic.
Batch predictive scoring for fraud detection
Scales to zero when idle, routes requests through explainability components, and integrates TensorFlow and XGBoost models.
A/B testing of model versions
Uses canary rollouts and InferenceGraph to compare predictions while minimizing risk.
Multi‑tenant model marketplace
Leverages ModelMesh for high‑density serving of many models with intelligent caching and resource isolation.
Tech snapshot
Shell56%
Python22%
Go21%
Dockerfile1%
Makefile1%
Smarty1%
Frequently asked questions
Does KServe require Knative?
Knative is installed by default for serverless deployments; a lightweight standalone mode is also available without Knative, but it lacks canary and scale‑to‑zero features.
Which machine‑learning frameworks are supported?
KServe supports TensorFlow, PyTorch, scikit‑learn, XGBoost, ONNX, and additional frameworks via custom containers.
How does autoscaling work for generative models?
KServe provides request‑based autoscaling that monitors GPU utilization and can scale pods up or down, including scale‑to‑zero for predictive workloads.
Can I use Hugging Face models directly?
Yes, KServe includes native support for Hugging Face model formats, simplifying deployment with a single InferenceService definition.
Is model explainability available out of the box?
KServe includes built‑in explainer components that generate feature attributions and other explanations for supported model types.
Project at a glance
Active- Stars
- 5,021
- Watchers
- 5,021
- Forks
- 1,346
LicenseApache-2.0
Repo age6 years old
Last commit23 hours ago
Primary languageGo
Last synced 12 hours ago