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Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
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.
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.
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.
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.
When teams consider KServe, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
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.
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.
KServe supports TensorFlow, PyTorch, scikit‑learn, XGBoost, ONNX, and additional frameworks via custom containers.
KServe provides request‑based autoscaling that monitors GPU utilization and can scale pods up or down, including scale‑to‑zero for predictive workloads.
Yes, KServe includes native support for Hugging Face model formats, simplifying deployment with a single InferenceService definition.
KServe includes built‑in explainer components that generate feature attributions and other explanations for supported model types.
Project at a glance
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