KServe vs LMDeploy
On the evidence we track, KServe leads this comparison with a composite score of 68/100. Scores are only directly comparable because these tools share a category; the full breakdown and every source is below.
Capabilities
Feature-by-feature on the axes that matter for model serving. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
KServe
LeaderAn open-source platform designed to simplify deployment of machine learning models on Kubernetes while supporting complex enterprise-scale inference with multiple frameworks and advanced optimization features.
LMDeploy
LMDeploy is a comprehensive toolkit enabling efficient compression, deployment, and serving of large language models across multiple machines and GPUs with advanced optimization techniques.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
KServe
Leader- Hugging Face
- TensorFlow
- PyTorch
- scikit-learn
- XGBoost
- ONNX
- vLLM
- llm-d
- Istio
- Knative Serving
- Envoy
- OpenTelemetry
- Prometheus
- HuggingFace
- MLflow
- KEDA
- Gateway API
LMDeploy
- llm-compressor
- OpenCompass
KServe or LMDeploy: which one depends on you
A composite score cannot know your constraints. Describe them and both get re-weighted against what you actually need, with the evidence behind every position.
Free to run, no account needed to start. How the evaluation works
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.