Groq API
The fastest LLM inference API available. Groq's LPU hardware delivers 10-20x faster token generation than GPU-based providers, making it ideal for latency-sensitive applications.
Proprietary
TypeScript / Python
Why Groq API?
You need the lowest latency LLM responses
Real-time applications like voice or live chat
You want OpenAI-compatible API with speed advantage
Signal Breakdown
What drives the Trust Score
Download Trend
Last 12 months
Tradeoffs & Caveats
Know before you commitYou need GPT-4 class reasoning quality
Your app requires fine-tuning or custom models
You need multimodal (vision) capabilities
Pricing
Free tier & paid plans
Free tier with rate limits
Pay-per-token, ~$0.59/1M tokens (Llama 3.3 70B)
Significantly cheaper than OpenAI for same quality
Alternative Tools
Other options worth considering
The most widely used LLM API. Powers GPT-4o and o1 models with best-in-class reasoning, vision, and structured outputs. Largest ecosystem of tutorials, integrations, and community support.
Claude's family of models leads on coding, analysis, and long-context tasks with a 200k token context window. Known for lower hallucination rates and nuanced instruction following.
Often Used Together
Complementary tools that pair well with Groq API
Learning Resources
Docs, videos, tutorials, and courses
Get Started
Repository and installation options
View on GitHub
github.com/groq/groq-typescript
npm install groq-sdkpip install groqQuick Start
Copy and adapt to get going fast
import Groq from 'groq-sdk';
const client = new Groq({ apiKey: process.env.GROQ_API_KEY });
const response = await client.chat.completions.create({
model: 'llama-3.3-70b-versatile',
messages: [{ role: 'user', content: 'Hello!' }],
});
console.log(response.choices[0].message.content);Code Examples
Common usage patterns
Streaming responses
Stream tokens as they're generated
const stream = await client.chat.completions.create({
model: 'llama-3.3-70b-versatile',
messages: [{ role: 'user', content: prompt }],
stream: true,
});
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0]?.delta?.content ?? '');
}JSON mode
Force structured JSON output
const response = await client.chat.completions.create({
model: 'llama-3.3-70b-versatile',
messages: [{ role: 'user', content: 'Return a JSON object with name and age' }],
response_format: { type: 'json_object' },
});Community Notes
Real experiences from developers who've used this tool