Groq and Fireworks AI: When Fast Inference Is Worth the Price

Speed vs cost: a different tradeoff

Most AI API discussions focus on model quality and price-per-token. But for real-time applications — live chat, voice assistants, interactive tools — latency matters as much as cost. A response that takes 5 seconds may be technically better, but a 200ms response creates a fundamentally different user experience.

Groq and Fireworks AI specialize in ultra-fast inference, serving open-source models at speeds that rival or exceed proprietary APIs.

Groq: LPU-powered speed

Groq uses custom Language Processing Units (LPUs) instead of GPUs, achieving inference speeds of 500–800 tokens per second for models like Llama 3.1 and Mixtral. Key points:

• Pricing — Competitive with other inference providers. Llama 3.1 70B runs at roughly $0.59/1M input, $0.79/1M output tokens.
• Speed — Time-to-first-token under 100ms for most models. Total generation time is significantly faster than GPU-based inference.
• Model selection — Primarily open-source models: Llama 3.1 (8B, 70B), Mixtral 8x7B, and Gemma. No proprietary models.
• Best for — Real-time chat, voice applications, any use case where response latency directly affects user experience.

Fireworks AI: flexible and fast

Fireworks AI optimizes GPU-based inference with custom serving infrastructure. They offer both pre-hosted models and the ability to deploy fine-tuned models:

• Pricing — Similar to Groq for popular models. Llama 3.1 70B at around $0.90/1M input, $0.90/1M output tokens.
• Speed — Not quite Groq-level but still significantly faster than standard GPU inference. Typical time-to-first-token under 200ms.
• Model selection — Broader model library including Llama, Mixtral, and various specialized models. Supports custom fine-tuned deployments.
• Best for — Teams that need fast inference but also want model customization and fine-tuning support.

When to choose speed over raw capability

Fast inference providers make sense when:

• You're building a conversational UI where users expect instant responses
• Your application makes chained API calls (agent loops) where total latency compounds
• You're running open-source models anyway and want faster, hosted inference
• Your workload is latency-sensitive but doesn't require GPT-4o or Claude Sonnet quality

They don't make sense when you need frontier-model quality (GPT-4o, Claude Opus) or specific proprietary features like OpenAI's vision or Anthropic's tool use.

Cost monitoring for inference providers

Groq and Fireworks don't offer usage APIs for pulling cost data. The best way to track spend is through a proxy-based approach that logs every request. MeterFox supports both Groq and Fireworks through its proxy gateway, giving you the same per-model cost breakdowns and budget alerts you'd get with any other provider.