Trade Groq's speed for 50-70% lower costs
Groq is the speed king—their custom LPUs deliver unmatched latency. Fleek is 50-70% cheaper on comparable models. Choose Groq for real-time chat and latency-critical apps. Choose Fleek for batch processing, cost-sensitive workloads, and models Groq doesn't support.
Groq made waves with their custom LPU (Language Processing Unit) hardware. They're genuinely fast—often 10x faster than GPU-based inference on time-to-first-token. For latency-critical applications, that speed is hard to beat.
But speed comes at a cost. Groq's per-token pricing reflects their hardware investment, and they have a more limited model selection. Fleek takes the opposite approach: optimize for cost efficiency on standard GPU hardware.
This comparison helps you understand when Groq's speed premium is worth it, and when Fleek's cost savings make more sense.
| Model | Fleek | Groq | Savings |
|---|---|---|---|
| Llama 3.1 70BGroq is faster but more expensive | ~$0.20/M tokens | $0.59/M tokens | 66% |
| Llama 3.1 8B | ~$0.03/M tokens | $0.05/M tokens | 40% |
| Mixtral 8x7B | ~$0.08/M tokens | $0.24/M tokens | 67% |
| Gemma 2 9B | ~$0.04/M tokens | $0.10/M tokens | 60% |
Groq has limited model selection compared to Fleek. DeepSeek R1 and many other models aren't available on Groq.
Fleek runs on NVIDIA Blackwell GPUs with custom optimization. We focus on maximizing tokens per GPU-second through FP4/FP8 precision, efficient batching, and continuous optimization. The result is competitive latency at significantly lower cost.
We support a wider range of models than Groq, including custom deployments at the same $0.0025/GPU-sec rate.
Groq built custom silicon (LPUs) specifically for transformer inference. Their architecture eliminates the memory bottleneck that limits GPU performance, achieving 10x+ faster time-to-first-token on supported models.
The tradeoff: limited model selection, higher prices, and no custom model support. You're paying a premium for that speed.
Both support OpenAI-compatible APIs. Migration is straightforward for supported models. The main consideration is latency—test your application's UX with Fleek's response times.
Groq's custom LPUs can deliver 5-10x faster time-to-first-token than GPU-based inference. For total generation time on longer outputs, the gap narrows. Fleek is still fast—just not Groq-fast.
As of January 2026, Groq doesn't support DeepSeek R1 or most MoE models. Their LPU architecture works best with dense transformer models. Fleek supports DeepSeek R1 at ~$0.67/M tokens.
For real-time applications where latency directly impacts UX—voice AI, chatbots, real-time agents—Groq's speed can be worth 2-3x the cost. For batch processing, background tasks, or cost-sensitive applications, Fleek's savings make more sense.
Yes, many teams use Groq for latency-critical paths (user-facing chat) and Fleek for everything else (background processing, batch jobs, evaluation). The compatible APIs make routing straightforward.
Coming soon. We're building support for any model—not just the open-source ones we showcase. Upload your fine-tuned weights or proprietary model, and we'll apply the same optimization. Same $0.0025/GPU-sec pricing, no custom model premium. Launching in the coming weeks.
Groq and Fleek optimize for different things. Groq is unmatched on speed—their custom hardware delivers latency that GPUs can't touch. Fleek is unmatched on cost—50-70% cheaper with a wider model selection.
For real-time chat, voice AI, and applications where users feel every millisecond, Groq's premium is often worth it. For everything else—batch processing, background tasks, high-volume inference—Fleek's cost savings compound quickly.
The smart play: use Groq where speed matters, Fleek where it doesn't.
Note: Fleek is actively expanding model support with new models added regularly. Features where competitors currently have an edge may become available on Fleek over time. Our goal is universal model optimization—supporting any model from any source at the lowest possible cost.
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