Groq API
Groq
FreemiumvLLM
vLLM Project (UC Berkeley / community)
FreeGroq 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.
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
FreeAPI Access
Groq API
Not availablevLLM
AvailablePlatforms
Groq API
WebvLLM
Linux (CUDA/ROCm), AWS, GCP, Azure, On-premiseIntegrations
Groq API
—vLLM
6 integrationsVendor
Groq API
GroqvLLM
vLLM Project (UC Berkeley / community)Category
Groq API
APIsvLLM
InfrastructureLaunch
Groq API
—vLLM
Jun 2023| Feature | Groq API | vLLM |
|---|---|---|
| Pricing Model | Freemium | Free |
| API Access | Not available | Available |
| Platforms | Web | Linux (CUDA/ROCm), AWS, GCP, Azure, On-premise |
| Integrations | — | 6 integrations |
| Vendor | Groq | vLLM Project (UC Berkeley / community) |
| Category | APIs | Infrastructure |
| Launch | — | 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
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
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.
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Data sourced from public vendor documentation. Pricing, features, and availability may change. Always verify on official vendor websites before making purchasing decisions. AIHub is not affiliated with any of the listed vendors.