Groq vs Together AI: which inference API should you choose?
Use Groq if you need the fastest inference latency for chat models and want a focused product. Use Together AI if you need a broader model marketplace, fine-tuning, and more customization options.
VERDICT
Side-by-side comparison
| Feature | Groq | Together AI | Winner |
|---|---|---|---|
| Inference latency (7B model) | ~50-100ms | ~200-400ms | Groq |
| Throughput (tok/s) | ~300-500 tok/s | ~100-200 tok/s | Groq |
| Available models | ~15 curated models | 200+ models | Together AI |
| Fine-tuning support | No | Yes (LoRA, full) | Together AI |
| Pricing model | Per-request (fixed) | Per-token + per-compute hour | Together AI |
| API compatibility | OpenAI-compatible | OpenAI-compatible | Tie |
| Custom models | No | Yes | Together AI |
| Supported hardware | Groq LPU only | A100, H100, A6000 | Together AI |
Performance benchmarks
Time to first token (7B chat model)
Groq's LPU deterministic latency vs. Together AI's GPU-based variable latency. Measured via official API calls with standard prompts.
Throughput per API instance (tokens/sec, 7B model)
Groq uses custom silicon with predictable batching. Together AI throughput varies by hardware (A100 higher, A6000 lower).
Cost per 1M tokens (7B model, 2026)
Groq pricing is stable per-request regardless of load. Together AI varies by model and hardware tier; can be cheaper for bulk inference.
Model variety (as of April 2026)
Groq curates for speed; Together AI offers breadth including custom-trained and fine-tuned variants.
When to use each
- ✓ Real-time chat applications where latency under 100ms is critical: Groq's LPU hardware delivers predictable sub-100ms TTFT even under load.
- ✓ Competitive benchmarking or latency-sensitive leaderboards: Groq consistently wins speed comparisons and appears in performance rankings.
- ✓ Cost-predictable budgets where per-request pricing is simpler than per-token scaling: Groq's fixed pricing removes surprise bill escalation.
- ✓ High-throughput inference where you can batch requests: Groq's continuous batching and deterministic performance suit load-balanced production.
- ✓ When you're happy with a focused set of models: Groq's curated 15-model selection includes most popular open-source chat models.
- ✓ You need access to 100+ different model options including newer or niche models: Together AI's marketplace covers Hugging Face ecosystem almost entirely.
- ✓ Fine-tuning workflows where you want to adapt models to your domain: Together AI offers LoRA and full fine-tuning without building your own infra.
- ✓ Multi-modal or specialized tasks (vision, code generation, function-calling) where specific models excel: Together AI's breadth covers edge cases.
- ✓ Cost optimization for variable workloads where per-token billing (not fixed per-request) better aligns with usage spikes.
- ✓ Experimentation and model evaluation where you want to test 20 different models in a week without vendor lock-in concerns.
Common misconceptions
Groq
Groq is only good for latency and can't scale throughput.
Groq can handle significant throughput via batching and concurrent requests: the 300-500 tok/s per instance scales horizontally. The real limit is model variety, not capacity.
Groq's pricing is always cheaper because it looks lower per-request.
Groq's per-request pricing can get expensive for low-latency, small-batch workloads. Together AI's per-token model may be better for large-batch jobs where you can amortize overhead.
Groq supports all open-source models.
Groq only serves ~15 hand-picked models optimized for their LPU. If you want Llama 3.3, Qwen, or Phi, Groq has them: but not every variant or new release immediately.
Together AI
Together AI is as fast as Groq if you pay more or use A100s.
Together AI on an A100 is still 3-5x slower to first token than Groq's LPU. GPUs are general-purpose; Groq's LPU is purpose-built for sequence generation latency.
Together AI's per-token pricing is always cheaper at scale.
Together AI charges both per-token AND per-compute-hour for fine-tuning. Long-running or persistent inference can cost more than Groq's fixed per-request pricing.
You can self-host Together AI inference.
Together AI is API-only (managed service). If you need local inference, you'd use vLLM or llama.cpp instead. Together AI's value is managed multi-model inference, not self-hosting.
Code examples
Task: Send a chat message to a Groq model and receive a completion.
import os
from groq import Groq
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
# Groq's LPU hardware delivers sub-100ms latency
response = client.chat.completions.create(
model="mixtral-8x7b-32768",
messages=[
{"role": "user", "content": "Explain quantum computing in one sentence."}
],
temperature=0.7,
max_tokens=100
)
print(response.choices[0].message.content)Groq uses the standard OpenAI SDK interface, making migration trivial. The key difference: Groq's LPU hardware ensures predictable latency regardless of concurrent load: no GPU queueing delays.
import os
import together
together.api_key = os.environ.get("TOGETHER_API_KEY")
# Together AI's broader model selection (OpenAI-compatible endpoint)
response = together.Complete.create(
prompt="Explain quantum computing in one sentence.\n",
model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
max_tokens=100,
temperature=0.7
)
print(response['output']['choices'][0]['text'])Together AI also supports OpenAI-compatible chat completions via their API, but their native SDK uses a different interface. The key difference: Together AI lets you pick from 200+ models in one API, trading latency for flexibility.
Migration path
- Switching between Groq and Together AI:
- Install: `pip install groq` or keep existing `together` SDK.
- Swap API key: change `GROQ_API_KEY` to `TOGETHER_API_KEY`.
- Update model name: Groq models are `mixtral-8x7b-32768`, `llama-3.1-70b-versatile`; Together uses full HuggingFace paths like `meta-llama/Llama-3.3-70B-Instruct-Turbo`.
- If you're using OpenAI-compatible mode on Together, you can use the OpenAI Python SDK with `base_url="https://api.together.xyz/v1"`: no code change needed, just swap endpoint.
- Groq doesn't support fine-tuning, so if you migrate FROM Together's fine-tune, you'll need to either keep Together for that use case or use vLLM locally for your custom model.
- Test latency expectations: Groq users will notice Together AI is 3-5x slower; Together users upgrading to Groq can reduce TTFT from ~300ms to ~75ms but with fewer model options.
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