Inference comparison
Groq vs Modal
Managed inference API against serverless gpu. Editorial fit scores help shortlist; live pricing stays on the provider sites.
Editorial scores last reviewed August 7, 2026
Managed inference API
Groq
Token / request API pricing
Serverless GPU
Modal
Serverless CPU/GPU time
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| Dimension | Groq | Modal |
|---|---|---|
| Category | Managed inference API | Serverless GPU |
| Billing | Token / request API pricing | Serverless CPU/GPU time |
| GPU choice | Fixed LPU-backed serving — no SKU selection | Platform-managed GPU classes |
| Cold start | Typically warm API endpoints | Can be noticeable on cold containers |
| Model access | Curated high-speed model set on Groq Cloud | Package models in images or pull at runtime |
| Best for | Latency-sensitive chat, agents, and batch inference on Groq’s supported model set | Developers who want GPU code as functions with autoscaling |
| Watch out | Model catalog and deployment options are narrower than general GPU rental; verify your model is supported before committing architecture | Cold starts and platform abstractions matter more than picking a bare metal SKU |
| GPU choice (1–10) | 2/10 | Winner: 6/10 |
| Time-to-serving (editorial) (1–10) | Winner: 10/10 | 7/10 |
| Price clarity (1–10) | 6/10 | Winner: 7/10 |
| Production ops (1–10) | 8/10 | 8/10 |
| Open-model breadth (1–10) | 6/10 | Winner: 7/10 |
Chart
Fit scores on the decision axes
Editorial 1–10 ratings for this pair only — not live pricing or latency benchmarks.
Scores are editorial planning ratings (1–10) for product shape — not published $/hour quotes or vendor SLAs. Verify current pricing on each provider site.
How to decide
Prefer Groq when latency-sensitive chat, agents, and batch inference on groq’s supported model set. Prefer Modal when developers who want gpu code as functions with autoscaling. If those statements both feel true, rent a GPU for control and keep a managed API for peak traffic — do not force one product to do both jobs.
Common questions
Groq vs Modal
Answered from the verified figures on this page rather than general guidance.
How are Groq and Modal billed?
Groq uses token / request api pricing; Modal uses serverless cpu/gpu time. Marketplace hourly spend tracks GPU uptime; serverless and managed APIs bill for what you invoke — different failure modes if you forget to shut things down.
Which gives more control over the GPU, Groq or Modal?
Groq: Fixed LPU-backed serving — no SKU selection. Modal: Platform-managed GPU classes. Modal scores higher for GPU choice (6/10 vs 2/10). Pick a marketplace when you need a specific SKU; pick serverless or managed APIs when you want the platform to handle hardware.
Which has faster cold starts, Groq or Modal?
Groq: Typically warm API endpoints. Modal: Can be noticeable on cold containers. Our editorial ratings favour Groq for warm API speed (10/10 vs 7/10), but real latency depends on model size, region, and whether endpoints are kept warm. Those 1–10 scores are editorial rankings, not measured milliseconds — record your own TTFT/p95 on a warm endpoint in the target region before you buy on latency.
Should I use Groq or Modal?
Choose Groq when latency-sensitive chat, agents, and batch inference on groq’s supported model set. Choose Modal when developers who want gpu code as functions with autoscaling. Watch out: Model catalog and deployment options are narrower than general GPU rental; verify your model is supported before committing architecture Cold starts and platform abstractions matter more than picking a bare metal SKU
When should I use a managed API like Groq instead of renting a GPU?
Prefer a managed inference API when you want tokens quickly without CUDA ops and the model is already on the catalog. Prefer GPU rental when you need a specific SKU, custom serving stack, or sustained utilization that beats token pricing — size VRAM on /gpus first.