Side-by-Side Comparison · 2026

Groq API vs vLLM: Full Comparison (2026)

Groq API is ultra-fast llm inference - 1,000+ tokens/sec via lpu chips. vLLM is high-throughput open-source llm inference with pagedattention. Use the breakdown below to find the right fit for your needs.

Groq API

Groq

Freemium
vs

vLLM

vLLM Project (UC Berkeley / community)

Free
This page presents factual information sourced from publicly available vendor documentation and product pages. AIHub does not endorse either product. The right tool depends on your specific use case, team, and requirements — we recommend evaluating both tools directly before making a decision.

Side-by-Side Overview

Pricing Model
Groq API: FreemiumvLLM: Free
API Access
Groq API: Not availablevLLM: Available
Platforms
Groq API: WebvLLM: Linux (CUDA/ROCm), AWS, GCP, Azure, On-premise
Integrations
Groq API: —vLLM: 6 integrations
Vendor
Groq API: GroqvLLM: vLLM Project (UC Berkeley / community)
Category
Groq API: APIsvLLM: Infrastructure
Launch
Groq API: —vLLM: Jun 2023

About Groq API

Groq provides LLM inference at 10-30× the speed of GPU alternatives using proprietary Language Processing Units (LPUs). Offers Llama 4 Scout, Llama 3.3 70B, Mixtral, and Gemma 3 at speeds exceeding 1,000 tokens/second with sub-100ms TTFT. Raised $6.9B and signed $20B chip deal with Nvidia in 2025.

Designed For

  • Low-latency AI applications
  • Real-time AI agents
  • Voice AI
  • Chatbots
Full Groq API details

About vLLM

vLLM is an open-source, high-throughput and memory-efficient inference engine for large language models, built by UC Berkeley. Its PagedAttention algorithm manages GPU memory like an OS manages RAM, enabling 2-4× more throughput than standard HuggingFace inference. Provides an OpenAI-compatible server for drop-in deployment.

Designed For

  • Self-hosted LLM serving
  • High-throughput inference
  • Production LLM deployment
  • Multi-GPU serving
Full vLLM details

Strengths & Limitations

Groq API

Strengths

  • Fastest available inference (1000+ tps)
  • Sub-100ms TTFT
  • Competitive pricing
  • Llama 4 support

Limitations

  • Limited model selection vs cloud providers
  • Not for custom model training

vLLM

Strengths

  • 2-4× throughput vs HuggingFace
  • OpenAI-compatible API
  • Continuous batching
  • Multi-GPU support
  • All major open models

Limitations

  • Requires ML expertise
  • GPU hardware needed
  • No GUI
  • Setup complexity

Frequently Asked Questions

What is the difference between Groq API and vLLM?

Groq API is ultra-fast llm inference - 1,000+ tokens/sec via lpu chips, while vLLM is high-throughput open-source llm inference with pagedattention. Groq API is designed for APIs; vLLM is designed for MLOps engineers, Platform teams. The right fit depends on your specific requirements.

How do the pricing models compare?

Groq API is available under a Freemium model. vLLM is available under a Free model. vLLM's entry tier starts at $0. Always verify pricing on each vendor's official website as it may change.

What integrations does each tool support?

Groq API integrates with various tools. vLLM integrates with Hugging Face, LangChain, LlamaIndex, Kubernetes. Check each vendor's documentation for the full and current list.

How do I choose between Groq API and vLLM?

Consider your team's technical requirements, budget, existing tooling, and use case before deciding. We recommend signing up for free trials or demos of both tools where available, and consulting each vendor's documentation. AIHub provides this comparison for informational purposes only.