Modal vs vLLM

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Modal Fine-tuning	vLLM Fine-tuning
Tagline	Serverless GPUs and infra for training & serving ML.	Open-source high-throughput inference engine for serving LLMs with PagedAttention and continuous batching.
Category	Fine-tuning	Fine-tuning
Pricing	Freemium· $30/mo free credits; pay-as-you-go GPU rates	Free· Free and open-source (Apache 2.0); self-hosted infrastructure costs apply
Model	Infrastructure (any model you can host)	Multi-model (open-weight LLMs: Llama, Qwen, DeepSeek, Mistral, Gemma, Phi, etc.)
Editorial score	8.7 / 10	—
Use cases	serverless GPUfine-tuningbatch inference	llm-servingself-hosted-inferenceopenai-api-replacementhigh-throughput-batchingmulti-gpu-deployment
Pros	Zero-ops GPU access Python-native Auto-scaling Honest pay-per-second pricing	PagedAttention delivers industry-leading throughput on the same hardware Drop-in OpenAI-compatible API makes migration from hosted models trivial Broad hardware support spanning NVIDIA, AMD, Intel, TPU, and Neuron Apache-2.0, no per-token cost, no vendor lock-in Backed by Berkeley + major-cloud sponsors with very active release cadence
Cons	Cold start latency on big models Bills can surprise at scale	You provide and operate the GPUs; no managed offering Steep learning curve for tuning parallelism, quantization, and KV cache Bleeding-edge model support sometimes lags the model's release by days Multi-node deployment requires Ray or Kubernetes plumbing
Website	modal.com	vllm.ai

Pick Modal if

Pick vLLM if