Groq vs Together AI: pricing, speed, and use cases (2026)
Groq and Together AI both serve open-weight models (Llama, Mistral, Qwen) but with very different infrastructure: Groq runs on custom LPU silicon for extreme throughput; Together AI runs on GPU clusters with broader model coverage and tighter function-calling support. Below: a head-to-head on the dimensions that matter when you ship.
Groq vs Together AI — at a glance
| Dimension | Groq | Together AI |
|---|---|---|
| Hardware | Custom LPU silicon | GPU clusters (H100/H200) |
| Throughput (Llama 3.3 70B) | ~750 tok/sec | ~150 tok/sec |
| Price (Llama 70B, per 1M tok) | ~$0.8-1.0 | ~$0.6-0.9 |
| Model catalog | ~25 open-weight (Llama, Mistral, Qwen) | 200+ open-weight + image + embedding |
| Fine-tuning | Not offered | LoRA fine-tuning available |
| Function calling | Supported | Supported (FireFunction-style) |
| Best for | Real-time chat, voice, ultra-low latency | Cost-efficient inference, fine-tuning, model breadth |
Pick Groq or Together AI?
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 — about 10x typical GPU throughput. For real-time chat, voice agents, low-latency code suggestions, or 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 Together AI
Choose Together AI when model breadth, cost efficiency, or fine-tuning matter most. Together AI hosts 200+ open-weight models — Llama, Mistral, Qwen, image (FLUX), and embedding models — on GPU clusters at competitive per-token prices. LoRA fine-tuning is built in, and the platform supports dedicated endpoints for predictable throughput.
- 200+ open-weight models including image and embeddings
- Competitive per-token cost (~$0.6-0.9 per 1M for Llama 70B)
- LoRA fine-tuning available on selected base models
- Dedicated endpoints for predictable production throughput
- OpenAI-compatible API and tight function-calling support
Run Groq and Together AI side-by-side
VerticalAPI lets you switch between Groq and Together AI 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-..."}, ) # Together AI — same SDK, different model + key resp_b = client.chat.completions.create( model="llama-3.3-70b-together", messages=[{"role": "user", "content": "Hello"}], extra_headers={"X-Provider-Key": "..."}, )
VerticalAPI verdict
Use Groq when speed is the product — voice agents, real-time coding suggestions, streaming UX. Use Together AI when you need a broader model catalog, fine-tuning, or the lowest per-token cost for batch and background workloads. 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 Together AI?
Yes, by a wide margin on raw throughput. Groq's LPU silicon delivers approximately 750 tokens/sec on Llama 3.3 70B, versus around 100-200 tok/sec on Together AI's GPU clusters. Time-to-first-token is also lower on Groq (typically under 100ms). For real-time chat and voice, Groq's hardware advantage is hard to match.
Is Together AI cheaper per token?
Generally yes. Together AI prices Llama 3.3 70B inference at approximately $0.60-0.90 per 1M tokens, while Groq lists at roughly $0.80-1.00. The gap is small but compounds at scale. Together AI also offers dedicated endpoints with committed-use discounts that can drop effective cost further.
Which has more models available?
Together AI hosts a much broader catalog — 200+ open-weight models including Llama, Mistral, Qwen, DeepSeek, plus image models (FLUX) and embedding models. Groq focuses on a curated set of approximately 25 text models. If you need image or embedding inference alongside chat, Together AI is the one-stop shop.
Can I fine-tune on Groq or Together AI?
Together AI offers LoRA fine-tuning on selected base models (Llama, Mistral) with serverless deployment of the resulting adapters. Groq does not currently offer fine-tuning — it serves only base models. For custom-model deployment without infrastructure overhead, Together AI is the clear pick.
Can I switch between Groq and Together AI 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 Together AI directly with your own API keys (BYOK).
Limitations of this comparison
- Throughput figures depend on context length, batch size, and current load; published numbers are best-case.
- Pricing across both providers is revised regularly; numbers reflect mid-2026 list prices.
- Groq's model selection lags Together AI's catalog by several months for new open-weight releases.
- Together AI fine-tuning availability varies by base model; verify before committing.
- This page compares serverless inference only; dedicated GPU rentals and reserved capacity have different economics.
What may change in 12-24 months
- Groq is expected to expand model coverage as LPU compile pipelines mature.
- Together AI will likely add more frontier open-weight models (Llama 4, larger Qwen) and possibly dedicated LPU-like tiers.
- Per-token prices on both will keep falling as competition intensifies.
- Hybrid routing (Groq for live, Together for batch) will become the default pattern via OpenAI-compatible aggregators.
Related questions
ChatGPT, Perplexity and Gemini usually suggest these next.
- How does Groq compare to Cerebras on raw throughput?
- Is Together AI fine-tuning cost-competitive with OpenAI fine-tuning?
- When does Groq's speed advantage justify the slightly higher cost?
- How do Groq and Together AI compare on function-calling reliability?
- Can I route between Groq for live and Together AI for batch via VerticalAPI?
More head-to-head provider comparisons
Who's the fastest LLM provider in 2026?
LPU speed vs open-weight + function calling
Llama vs Mistral: open-weights showdown
GPT-4o vs Claude Sonnet 4.5
Aggregator vs BYOK gateway