vLLM vs Windmill
Side-by-side comparison of two AI agent tools
Short answer
- vLLM is growing faster: +2,933 GitHub stars in the last 30 days vs +316 for Windmill.
- Pick vLLM for: a high-throughput and memory-efficient inference and serving engine for LLMs. Pick Windmill for: open-source developer platform to power your entire infra and turn scripts into webhooks, workflows and UIs.
From GitHub data refreshed daily.
vLLMopen-source
A high-throughput and memory-efficient inference and serving engine for LLMs
Windmillopen-source
Open-source developer platform to power your entire infra and turn scripts into webhooks, workflows and UIs. Fastest workflow engine (13x vs Airflow). Open-source alternative to Retool and Temporal.
Metrics
| vLLM | Windmill | |
|---|---|---|
| Stars | 93.1k | 18.1k |
| Star velocity /mo | 2.9k | 316.10526315789474 |
| Commits (90d) | 4.0k | 1.3k |
| Releases (6m) | 10 | 10 |
| Downloads (30d, npm + PyPI) | 1.9M | — |
| Overall score | 0.9233627347430968 | 0.8093821444423039 |
Pros
- +Exceptional serving throughput with PagedAttention memory optimization and continuous batching for production-scale LLM deployment
- +Comprehensive hardware support across NVIDIA, AMD, Intel platforms and specialized accelerators with flexible parallelism options
- +Seamless Hugging Face integration with OpenAI-compatible API server for easy model deployment and switching
- +Multi-language support with automatic UI generation from scripts in Python, TypeScript, Go, Bash, SQL, and more
- +High performance workflow engine claiming 13x faster execution than Airflow
- +Self-hostable open-source solution with AGPLv3 license providing full control and customization
Cons
- -Requires significant GPU memory for optimal performance, limiting accessibility for resource-constrained environments
- -Complex setup and configuration for distributed inference across multiple GPUs or nodes
- -Primary focus on inference means limited support for training or fine-tuning workflows
- -AGPLv3 license may restrict some commercial use cases and require careful compliance consideration
- -Being a comprehensive platform may introduce complexity for simple automation tasks
- -Self-hosting requires infrastructure management and maintenance overhead
Use Cases
- •Production API serving for applications requiring high-throughput LLM inference with multiple concurrent users
- •Research and experimentation with open-source LLMs requiring efficient model switching and testing
- •Enterprise deployment of private LLM services with OpenAI-compatible interfaces for existing applications
- •Building internal APIs and webhooks from existing scripts without additional infrastructure
- •Creating automated workflows for background jobs and data processing pipelines
- •Developing low-code internal applications with custom UIs for non-technical team members
FAQ
- Which is more popular, vLLM or Windmill?
- vLLM has more GitHub stars (93,097 vs 18,092).
- Which is more actively developed, vLLM or Windmill?
- vLLM had more commits in the last 90 days (4,023 vs 1,264).
- Should I use vLLM or Windmill?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.