Inference comparison
Groq vs Together AI
Managed inference API against managed inference api. 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
Managed inference API
Together AI
Token / request API pricing
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| Dimension | Groq | Together AI |
|---|---|---|
| Category | Managed inference API | Managed inference API |
| Billing | Token / request API pricing | Token / request API pricing |
| GPU choice | Fixed LPU-backed serving — no SKU selection | Hidden behind the inference API |
| Cold start | Typically warm API endpoints | Low for warm endpoints |
| Model access | Curated high-speed model set on Groq Cloud | Strong open-model catalog + fine-tunes |
| Best for | Latency-sensitive chat, agents, and batch inference on Groq’s supported model set | Serving open-weight models without operating your own GPU fleet |
| Watch out | Model catalog and deployment options are narrower than general GPU rental; verify your model is supported before committing architecture | You trade GPU-level control for API pricing and catalog coverage |
| GPU choice (1–10) | 2/10 | Winner: 3/10 |
| Time-to-serving (editorial) (1–10) | Winner: 10/10 | 9/10 |
| 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: 9/10 |
Chart
Fit scores on the decision axes
Editorial 1–10 ratings for this pair only — not live pricing or latency benchmarks.
GPU choice
Pick exact GPUs
Time-to-serving (editorial)
Warm-path fit
Price clarity
Easy to forecast
Production ops
Less DIY ops
Open-model breadth
Catalog depth
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 Together AI when serving open-weight models without operating your own gpu fleet. 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 Together AI
Answered from the verified figures on this page rather than general guidance.
How are Groq and Together AI billed?
Both bill as token / request api pricing. Cost still depends on GPU class, region, and whether instances idle — verify live rates on each site before budgeting.
Which gives more control over the GPU, Groq or Together AI?
Groq: Fixed LPU-backed serving — no SKU selection. Together AI: Hidden behind the inference API. Together AI scores higher for GPU choice (3/10 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 Together AI?
Groq: Typically warm API endpoints. Together AI: Low for warm endpoints. Our editorial ratings favour Groq for warm API speed (10/10 vs 9/10), 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 Together AI?
Choose Groq when latency-sensitive chat, agents, and batch inference on groq’s supported model set. Choose Together AI when serving open-weight models without operating your own gpu fleet. Watch out: Model catalog and deployment options are narrower than general GPU rental; verify your model is supported before committing architecture You trade GPU-level control for API pricing and catalog coverage
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.