Groq
Run Llama, Qwen, and open GPT-OSS models on custom LPU chips for very low-latency, high-throughput inference at a fraction of typical GPU token costs. Reports of a $20B Nvidia asset acquisition surfaced in 2026, though Groq continues operating independently.
The verdict on Groq: Developers building applications where response speed matters more than model selection breadth Groq's actual differentiator is hardware: custom LPU chips designed specifically for token generation produce 300 to 800 tokens per second on popular models, which the data puts at 10 to 25 times faster than typical GPU-based inference. Pricing: Free tier / pay-as-you-go from $0.05/M tokens.
Best For
Developers building applications where response speed matters more than model selection breadth
Standout Feature
Custom inference chips that generate tokens 10 to 25 times faster than typical GPU-based inference
TL;DR
A genuine speed advantage worth building around, the model selection is narrower than a general-purpose API.
Alternatives
Fit Score: use-case reference
How this is scoredGroq scored 1 to 5 on five dimensions for the named use cases below. Part of our catalog-wide use-case reference, not a product-quality review rating.
An application where response speed matters more than model selection breadth
3.8/5- Capability
- 5/5
- Reliability
- 4/5
- Value
- 4/5
- Ease
- 4/5
- Compatibility
- 2/5
Groq's bestFor and standout both describe exactly this use case, custom hardware built for token-generation speed, confirmed by pros' 'blazing fast responses.' Compatibility is capped because cons flags 'limited model selection.'
Needing the widest possible model catalog for varied workloads
2.4/5- Capability
- 2/5
- Reliability
- 2/5
- Value
- 3/5
- Ease
- 3/5
- Compatibility
- 2/5
Cons states directly 'limited model selection' and 'capacity constraints at peak,' and ourTake confirms the catalog is 'narrower than a general-purpose inference API'; a mismatch for varied-workload needs.
Editorial judgment grounded in this tool's own published pricing, features, and our existing pros and cons, not a certification or a measured benchmark. Product quality is assessed separately under our review methodology.
Overview
Not yet reviewedProduct quality is assessed from original evidence. How reviews work.
Groq is an AI inference company that built custom Language Processing Units (LPUs), silicon designed specifically for fast sequential token generation, enabling output speeds of 300–800 tokens per second on popular frontier models, which is 10–25x faster than typical GPU-based inference at comparable cost. This inference speed has practical implications beyond raw benchmarks: at 300+ tokens/second, a 1,000-token response generates in under 4 seconds versus 30–60 seconds on slower providers, which changes the interaction model from 'wait for the response' to near-instantaneous generation. Groq's API is OpenAI-compatible, making it a drop-in speed upgrade for applications currently using OpenAI's endpoints by changing one URL. Available models include Meta's Llama family, Mistral, Gemma, and OpenAI's open gpt-oss models through Groq's API, each provided under its respective vendor's license.
Groq announced in June 2026 that llama-3.1-8b-instant and llama-3.3-70b-versatile are being deprecated for free and standard developer usage, pointing users toward gpt-oss-20b, gpt-oss-120b, or qwen3.6-27b as current alternatives; committed-spend Enterprise customers are exempt from this change. Free tier provides 14,400 to 30,000 requests per day depending on model. Paid plans provide higher rate limits and priority access. Groq's speed advantage is most material for applications where generation latency directly affects user experience, voice AI applications needing sub-second response, real-time code completion, interactive multi-turn conversations, and agentic workflows where the model is called dozens of times per task and total wall-clock time compounds.
For batch processing where latency doesn't matter, the speed advantage is less meaningful than for user-facing applications.
The source of the legacy catalog rating and review count has not been substantiated. Neither is presented here as a verified review rating or included in review structured data.
Workflow Fit Scores are separate, use-case-specific editorial judgments, not user-review averages. Read the Fit Score methodology.
- • Blazing fast responses
- • Easy drop-in API
- • Cost-effective
- • Limited model selection
- • Capacity constraints at peak
Key Features
- Very low-latency inference
- OpenAI-compatible API
- Popular open models hosted
- Generous free tier
Trust & Data
Verified 2026-09
- Training on your data
- Not publishedCustomer Data (GroqCloud, GroqChat, API usage) is explicitly governed by Groq's Services Agreement and Data Processing Addendum rather than the public privacy policy, which scopes itself to website-visitor data only.
- Compliance
- Not published
- Retention
- Not published
- Export
- Not published
Sources: groq.com/privacy policy, groq.com/security
As published by the vendor. Verify independently before purchase decisions.
Our Take
If your application's user experience depends on response speed, that gap is a real architectural advantage worth building around. The tradeoff is model selection breadth: the catalog is narrower than a general-purpose inference API, and capacity constraints can appear at peak. Pick Groq when latency is the primary constraint and the available model set covers your actual use case.