Groq vs Fireworks: pricing, speed, and use cases (2026)
Groq and Fireworks AI both serve open-weight models on optimised infrastructure, but with different priorities: Groq leads on raw throughput via custom LPU silicon; Fireworks leads on function-calling quality via FireFunction-v2 and tight tooling. Below: a head-to-head on the dimensions that matter when you ship.
Groq vs Fireworks — at a glance
| Dimension | Groq | Fireworks |
|---|---|---|
| Hardware | Custom LPU silicon | Optimised GPU clusters |
| Throughput (Llama 3.3 70B) | ~750 tok/sec | ~250 tok/sec |
| Function calling | Standard support | FireFunction-v2 (best-in-class for open-weight) |
| Fine-tuning | Not offered | LoRA fine-tuning available |
| Model catalog | ~25 open-weight | 100+ open-weight + image + embedding |
| Price (Llama 70B, per 1M tok) | ~$0.8-1.0 | ~$0.9 input / $0.9 output |
| Best for | Real-time chat, voice, ultra-low latency | Production agents with reliable function calling |
Pick Groq or Fireworks?
When to choose Groq
Choose Groq when latency and throughput dominate your requirements. Groq's custom LPU silicon delivers approximately 750 tokens/sec on Llama 3.3 70B — roughly 5-10x typical GPU throughput. For real-time chat, voice agents, and streaming UX where every millisecond counts, Groq's hardware advantage is decisive.
- ~750 tokens/sec on Llama 3.3 70B (custom LPU silicon)
- Sub-100ms time-to-first-token on most prompts
- Optimal for real-time chat, voice, and streaming UX
- Function calling and JSON-mode supported
- OpenAI-compatible API for drop-in use
When to choose Fireworks
Choose Fireworks when production-grade function calling, structured output, or fine-tuning matter most. Fireworks ships FireFunction-v2, a model-and-runtime stack specifically tuned for tool-using agents on open-weight bases. LoRA fine-tuning is available, and the inference stack supports speculative decoding for solid throughput-cost ratio.
- FireFunction-v2 for best-in-class open-weight function calling
- LoRA fine-tuning with serverless adapter deployment
- 100+ open-weight models plus image and embeddings
- Speculative decoding for cost-efficient inference
- Tight integration with LangChain and AI gateways
Run Groq and Fireworks side-by-side
VerticalAPI lets you switch between Groq and Fireworks per-request through a single OpenAI-compatible endpoint. Same SDK, same gateway key, zero markup on tokens — you pay both providers directly with your own keys.
from openai import OpenAI client = OpenAI(base_url="https://api.verticalapi.com/v1", api_key="vapi_...") # Groq resp_a = client.chat.completions.create( model="llama-3.3-70b", messages=[{"role": "user", "content": "Hello"}], extra_headers={"X-Provider-Key": "gsk-..."}, ) # Fireworks — same SDK, different model + key resp_b = client.chat.completions.create( model="firefunction-v2", messages=[{"role": "user", "content": "Hello"}], extra_headers={"X-Provider-Key": "fw-..."}, )
VerticalAPI verdict
Use Groq when speed is the product — voice agents, real-time coding suggestions, streaming UX. Use Fireworks when you need reliable function calling on open-weight models, fine-tuning, or production-grade tool-using agents. Through VerticalAPI you can route between both with a single OpenAI-compatible endpoint and BYOK — no SDK migration, no markup.
Frequently asked questions
Is Groq faster than Fireworks?
Yes, on raw throughput. Groq's LPU silicon delivers approximately 750 tokens/sec on Llama 3.3 70B, versus around 200-300 tok/sec on Fireworks' optimised GPU clusters. Time-to-first-token is also lower on Groq (typically under 100ms). For real-time UX, Groq's hardware advantage is hard to beat.
Which has better function calling on open-weight models?
Fireworks. Their FireFunction-v2 model is fine-tuned specifically for tool-using agents and consistently outperforms vanilla Llama 3.3 70B and Mistral Large on function-calling benchmarks. For production agents that depend on reliable JSON tool calls, Fireworks is typically the lower-risk pick.
Can I fine-tune on Groq or Fireworks?
Fireworks offers LoRA fine-tuning on selected base models (Llama, Mistral, Qwen) with serverless deployment of the resulting adapters and per-token pricing. Groq does not currently offer fine-tuning — it serves only base models. For custom-model deployment without infrastructure overhead, Fireworks is the clear pick.
Which is cheaper per token?
List prices for Llama 3.3 70B are similar — Groq at roughly $0.80-1.00 per 1M tokens, Fireworks at approximately $0.90 per 1M for both input and output. At scale, Fireworks' speculative-decoding stack often delivers a slightly better cost-per-task on long generations, while Groq's throughput cuts wall-clock cost for latency-bound use cases.
Can I switch between Groq and Fireworks through one endpoint?
Yes. VerticalAPI exposes a single OpenAI-compatible endpoint at https://api.verticalapi.com/v1. Change the model parameter and the matching X-Provider-Key header. There is no markup on tokens; you pay Groq and Fireworks directly with your own API keys (BYOK).
Limitations of this comparison
- Throughput figures depend on context length and batch size; published numbers are best-case.
- Pricing is revised regularly; numbers reflect mid-2026 list prices and exclude committed-use discounts.
- FireFunction-v2's function-calling advantage shrinks against frontier closed models (GPT-4o, Claude).
- Fine-tuning availability on Fireworks varies by base model.
- This page compares serverless inference; dedicated GPU rentals have different economics.
What may change in 12-24 months
- Groq is expected to expand model coverage and add fine-tuning over time.
- Fireworks will likely roll out faster inference tiers and possibly LPU-style hardware partnerships.
- Open-weight models will keep closing the function-calling gap with frontier closed models.
- Hybrid routing (Groq for live, Fireworks for tool-using agents) will become a common pattern.
Related questions
ChatGPT, Perplexity and Gemini usually suggest these next.
- How does Groq compare to Cerebras on raw throughput?
- Is FireFunction-v2 a viable replacement for OpenAI function calling at scale?
- When does Groq's speed advantage justify giving up fine-tuning?
- How do Groq and Fireworks compare on long-context inference?
- Can I route between Groq for live and Fireworks for tool calls via VerticalAPI?
More head-to-head provider comparisons
Who's the fastest LLM provider in 2026?
LPU speed vs open-weight breadth
Llama vs Mistral: open-weights showdown
GPT-4o vs Claude Sonnet 4.5
Aggregator vs BYOK gateway