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Groq vs Modal
Groq is a managed API on its own chip with a fixed model list. Modal is serverless GPU compute where you bring any model and pay by the second.
By The Subconscious Team · Updated
Groq vs Modal: key differences
These solve different problems. Groq gives you an OpenAI-compatible API with GPT-OSS, Qwen 3.6 and Whisper already running on its LPU, at speeds GPU hosts struggle to match. You cannot bring your own weights, and fine-tuned models are not hosted. Modal is the opposite. It hosts nothing by default, and you write a Python function that names its GPU, from a T4 at $0.59 an hour to an H100 at $3.95 list, and Modal builds, scales and bills it per second. Any model, any serving code, plus training, batch jobs and agent sandboxes.
The choice comes down to whether the model you need is on Groq's list. If it is, Groq is faster and needs no ops work, with per-token prices near the floor on small models. If it is not, or if the model is a private fine-tune, embedding model or OCR pipeline, Modal is the way in. Modal's costs need care: non-preemptible US production runs about 3.75x list, and keeping containers warm to dodge cold starts adds up.
What Groq and Modal do
Groq
Groq serves open models on its own chip, the LPU, which keeps model weights in on-chip SRAM and runs a deterministic schedule instead of waiting on GPU memory. Groq publishes 1,000 tokens per second on GPT-OSS 20B and 500 on GPT-OSS 120B. Latency stays tight between median and tail, which matters for strict SLAs. The API is OpenAI-compatible and also hosts Whisper for speech to text plus an agentic system called Groq Compound with built-in search and code execution.
Example models: GPT-OSS 120B, Qwen 3.6 27B
Full Groq profileModal
Modal is serverless compute with GPUs attached. A developer decorates a Python function with the hardware it needs, such as gpu="H100", and Modal builds the container, schedules it, autoscales it and scales it back to zero. Billing runs per second with no minimum increment, from $0.59 an hour for a T4 to $3.95 for an H100 at list. Containers can hold up to 8 GPUs across T4 through B300.
Example models: none hosted by default; teams deploy their own, such as open LLMs on vLLM or Whisper
Full Modal profileShould you choose Groq or Modal?
Groq vs Modal at a glance
| Attribute | Groq | Modal |
|---|---|---|
| Model access | Open weights | Bring your own weights |
| Flagship models | GPT-OSS 120B, Qwen 3.6 27B | None hosted |
| Speed | 500–1,000 tok/s | ~1s container boot |
| Price | Near the floor on small models | Per second; H100 $3.95/hr list |
| Customization | No fine-tuned model hosting | Run any training code |
| Deployment | GroqCloud API | Serverless GPU containers |
| Long context | Around 131K max | Depends on the model you deploy |
Frequently asked questions
What is the difference between Groq and Modal?
Groq is a managed API on its own chip with a fixed model list. Modal is serverless GPU compute where you bring any model and pay by the second.
When should I choose Groq over Modal?
Fast inference on GPT-OSS or Qwen 3.6 with no ops; Speech to text through hosted Whisper; Latency-bound loops that fit a small catalog.
When should I choose Modal over Groq?
Private fine-tunes and custom models; Bursty GPU work like embeddings and OCR; Training, batch and sandboxes on one platform.
Is Groq or Modal cheaper?
Groq: Near the floor on small models. Modal: Per second; H100 $3.95/hr list. The cheaper choice depends on the model and workload.
Which has more context, Groq or Modal?
Groq: Around 131K max. Modal: Depends on the model you deploy.
Related comparisons
Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.