Inference comparison
Modal vs Together AI
Serverless GPU against managed inference api. Editorial fit scores help shortlist; live pricing stays on the provider sites.
Editorial scores last reviewed August 7, 2026
Serverless GPU
Modal
Serverless CPU/GPU time
Managed inference API
Together AI
Token / request API pricing
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| Dimension | Modal | Together AI |
|---|---|---|
| Category | Serverless GPU | Managed inference API |
| Billing | Serverless CPU/GPU time | Token / request API pricing |
| GPU choice | Platform-managed GPU classes | Hidden behind the inference API |
| Cold start | Can be noticeable on cold containers | Low for warm endpoints |
| Model access | Package models in images or pull at runtime | Strong open-model catalog + fine-tunes |
| Best for | Developers who want GPU code as functions with autoscaling | Serving open-weight models without operating your own GPU fleet |
| Watch out | Cold starts and platform abstractions matter more than picking a bare metal SKU | You trade GPU-level control for API pricing and catalog coverage |
| GPU choice (1–10) | Winner: 6/10 | 3/10 |
| Time-to-serving (editorial) (1–10) | 7/10 | Winner: 9/10 |
| Price clarity (1–10) | 7/10 | 7/10 |
| Production ops (1–10) | 8/10 | 8/10 |
| Open-model breadth (1–10) | 7/10 | 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 Modal when developers who want gpu code as functions with autoscaling. 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
Modal vs Together AI
Answered from the verified figures on this page rather than general guidance.
How are Modal and Together AI billed?
Modal uses serverless cpu/gpu time; Together AI uses token / request api pricing. 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, Modal or Together AI?
Modal: Platform-managed GPU classes. Together AI: Hidden behind the inference API. Modal scores higher for GPU choice in our editorial fit ratings (6/10 vs 3/10). 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, Modal or Together AI?
Modal: Can be noticeable on cold containers. Together AI: Low for warm endpoints. Our editorial ratings favour Together AI (9/10 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 Modal or Together AI?
Choose Modal when developers who want gpu code as functions with autoscaling. Choose Together AI when serving open-weight models without operating your own gpu fleet. Watch out: Cold starts and platform abstractions matter more than picking a bare metal SKU You trade GPU-level control for API pricing and catalog coverage