Groq is not a model provider; it is a hardware company effectively giving away compute to prove a point. By running open-weights models on their custom Language Processing Units (LPUs) rather than standard GPUs, they achieve time-to-first-token (TTFT) and generation speeds that make traditional cloud providers look broken. We are talking about 280+ tokens per second for Llama 3.3 70B and 500+ for 8B models. For context, reading this sentence takes longer than Groq takes to write a small essay.
For developers building voice agents, real-time translators, or complex multi-step agentic workflows, Groq is currently the only viable option. The latency is so low that the awkward "thinking" pause in voice conversations disappears. The pricing is equally aggressive: Llama 3.1 8B costs $0.05 per million input tokens, which is effectively free for most startups. Even the massive Llama 3.3 70B is under $0.60/1M input, undercutting OpenAI’s GPT-4o mini while outperforming it on open benchmarks.
However, the hardware architecture that enables this speed is also its constraint. LPUs rely on SRAM, which is fast but expensive and limited in capacity compared to the HBM stacked on Nvidia GPUs. This means Groq is strictly for inference, not training, and they are slower to add massive context windows or memory-hungry mixture-of-experts models compared to peers like Together AI or Fireworks. You are also limited to the models they choose to host—mostly the Llama family and occasionally Mistral or Google’s Gemma.
The service has matured from a tech demo to a production-grade API, but it still feels like a utility rather than a platform. You won't find the rich tooling ecosystem of OpenAI or the fine-tuning flexibility of Fireworks here. It is a raw, blazing-fast pipe for intelligence.
Use Groq if your application’s UX depends on speed. If you are building a customer service voice bot or a code-completion tool, the difference is visceral. If you are processing bulk documents overnight where latency doesn’t matter, or if you need proprietary models like Claude or GPT-4, look elsewhere.
Pricing
Groq's pricing is a race to the bottom in the best way. The free tier is genuinely usable for development, offering ~30 requests per minute on smaller models like Llama 3.1 8B without a credit card. Paid tiers are commodity-priced: Llama 3.1 8B is $0.05/$0.08 (input/output per 1M tokens), and Llama 3.3 70B is $0.59/$0.79.
This is significantly cheaper than GPT-4o Mini ($0.15/$0.60) for a model (70B) that often reasons better. The only 'cost cliff' is the rate limit structure; you hit strict RPM (requests per minute) caps before you hit cost issues. Enterprise throughput guarantees require a custom contract, which is where they make their real money.
Technical Verdict
The API is fully OpenAI-compatible, meaning migration is often just changing the base_url and api_key. Latency is the standout metric: consistently sub-200ms TTFT and stable high throughput. Documentation is sparse but sufficient given the standard API shape. Reliability has improved, but model availability (especially non-Llama models like DeepSeek) can fluctuate. Python and JS SDKs are thin wrappers around the standard libraries.
Quick Start
# pip install groq
import os
from groq import Groq
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
chat = client.chat.completions.create(
messages=[{"role": "user", "content": "Explain quantum gravity in one sentence."}],
model="llama-3.3-70b-versatile",
)
print(chat.choices[0].message.content)Watch Out
- Model selection is narrow; if Llama 3 isn't good enough for your use case, you're out of luck.
- Rate limits (RPM) on the free/starter tiers are strict and will break production apps if not monitored.
- No server-side caching or stateful context management yet; you pay to send the full prompt every time.
- DeepSeek and other non-Llama models have appeared and disappeared; treat non-Llama support as experimental.