Kong vs vLLM
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 +110 for Kong.
- Pick Kong for: the API and AI Gateway. Pick vLLM for: a high-throughput and memory-efficient inference and serving engine for LLMs.
From GitHub data refreshed daily.
K
Kongopen-source
π¦ The API and AI Gateway
vLLMopen-source
A high-throughput and memory-efficient inference and serving engine for LLMs
Metrics
| Kong | vLLM | |
|---|---|---|
| Stars | 44.2k | 93.1k |
| Star velocity /mo | 110 | 2.9k |
| Commits (90d) | 10 | 4.0k |
| Releases (6m) | 2 | 10 |
| Downloads (30d, npm + PyPI) | β | 1.9M |
| Overall score | 0.5318560078693034 | 0.9233627347430968 |
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
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
Use Cases
- β’Production API serving for applications requiring high-throughput LLM inference with multiple concurrent users
- β’Research and experimentation with open-source LLMs requiring efficient model switching and testing
- β’Enterprise deployment of private LLM services with OpenAI-compatible interfaces for existing applications
FAQ
- Which is more popular, Kong or vLLM?
- vLLM has more GitHub stars (93,097 vs 44,236).
- Which is more actively developed, Kong or vLLM?
- vLLM had more commits in the last 90 days (4,023 vs 10).
- Should I use Kong or vLLM?
- 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.