Groq: Inference & Compute

Founded 2016|Mountain View, CA|201-500 people|Freemium

What is Groq?

Groq is an AI infrastructure company founded in 2016 by former Google engineers, including Jonathan Ross (one of the designers of Google's Tensor Processing Unit) and Douglas Wightman. Headquartered in Mountain View, California, Groq provides specialized AI compute solutions focused on accelerating AI inference workloads using its custom-built Language Processing Unit (LPU) hardware. The company's platform offers some of the most competitive pricing in the AI inference market, with ultra-low latency and exceptional throughput. Groq provides access to models from multiple providers including OpenAI, Anthropic, Google, Cohere, and Mistral through a pay-as-you-go model charging per token consumed. The company offers three billing tiers—Free, Developer, and Enterprise—with additional cost-saving features like Batch API (50% discount) and Prompt Caching (50% discount on cache hits). With offices across North America and Europe, Groq has established itself as a leading alternative to traditional cloud GPU providers, particularly for teams optimizing for inference speed and cost efficiency.

Key features

Core capabilities this platform advertises.

Custom LPU inference chips
Ultra-low latency inference
Fastest tokens-per-second performance
OpenAI-compatible API
Free tier for experimentation

Strengths and tradeoffs

What this tool does well, and the limitations to keep in mind.

Pros

Exceptional inference speed with ultra-low latency using custom LPU hardware
Highly competitive pricing—among the lowest per-token costs in the market
Batch API and Prompt Caching offer significant cost savings (50% discounts)
Linear, predictable pricing model with no hidden costs or idle infrastructure fees

Cons

Limited model fine-tuning options compared to training-focused platforms
Smaller model selection compared to comprehensive cloud providers
Enterprise features like GroqRack require custom contracts and sales engagement
Newer platform with less extensive documentation than established providers

Plans & pricing

What's included in each plan, and how the tiers compare.

Free

Limited usage
Pay-per-token
Access to multiple models
Community support

Developer

Pay-as-you-go

Per token

No minimum spend
Batch API (50% discount)
Prompt Caching (50% off cache hits)
Linear, predictable pricing

Enterprise

Custom

Contact sales for a quote

GroqRack deployment
Custom-tuned models
Dedicated support
Custom contracts

View official pricing page

Common use cases

Developers building real-time AI applications where inference speed is the top priority

Real-time AI applications needing lowest latency
Interactive conversational AI
High-throughput batch inference
Cost-efficient inference for open-source models
Latency-sensitive production deployments

Using Groq with Respan

Groq and Respan provide ultra-fast AI inference with comprehensive cost tracking. Run models on Groq's LPU hardware while monitoring performance and costs with Respan.

Track Groq API costs across different models to optimize spending
Monitor inference latency and throughput for Groq-powered applications
Compare Groq performance against other inference providers using Respan analytics
Optimize batch processing and prompt caching strategies based on Respan insights

Run Groq in production with Respan

10KFree traces/mo

1,000+Models

5 minSetup

Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 1,000+ models through one gateway.

Try Respan free

Best Groq alternatives & competitors

Top companies in Inference & Compute you can use instead of Groq.

NVIDIA

H100 and B200 GPU clusters