vLLM is an Apache-2.0 LLM inference and serving engine focused on high-throughput self-hosted model APIs. It combines PagedAttention, continuous batching, prefix caching, quantization options, OpenAI-compatible serving, structured outputs, metrics, Docker/Kubernetes deployment guidance and integrations with agent and LLM frameworks.
Alternatives to DeepInfer
2 editor-selected alternatives · DeepInfer overview →
source: tools.alternatives · stored order · active records only; review scores are annotations and never change membership or order
A directional evidence panel appears only when the substitute rationale, trade-offs, sources, and verification date have been recorded. Older selections without that panel remain visible but are unclassified under the new evidence contract.
SGLang is an open-source serving framework for large language and vision-language models, designed for low latency and high throughput. It features RadixAttention for automatic KV cache reuse, compressed finite state machines for fast structured output generation, continuous batching, and tensor parallelism. With over 25,000 GitHub stars, it supports models like LLaMA, Mistral, Qwen, and Gemma on NVIDIA and AMD GPUs.
Open-source DeepInfer alternatives
vLLM, SGLang — see all open-source developer tools.
More Model Providers tools
same category, not editor-selected alternatives — see how DeepInfer compares →
FAQ
Which DeepInfer alternative is listed first?
vLLM is first in the editor-selected list of 2 DeepInfer alternatives and carries an editorial review score of 91/100. The stored order is editorial; review scores do not determine membership or position.
Are there open-source DeepInfer alternatives?
Yes — vLLM, SGLang are open source.