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Inference comparison

Modal vs Together AI

Serverless GPU against managed inference api. Editorial fit scores help shortlist; live pricing stays on the provider sites.

Modal product facts checked October 2, 2026Modal editorial scores reviewed September 3, 2026Together AI product facts checked October 2, 2026Together AI editorial scores reviewed September 3, 2026

Serverless GPU

Modal

Functions: per-second CPU, memory and GPU resources; Shared Endpoints: per-token. Storage and network egress have separate allowances and rates.

vs

Managed inference API

Together AI

Serverless per-token; dedicated inference per GPU-minute for ready replicas; GPU clusters and provisioned throughput have separate contracts.

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Modal vs Together AI GPU inference comparison
DimensionModalTogether AI
CategoryServerless GPUManaged inference API
BillingFunctions: per-second CPU, memory and GPU resources; Shared Endpoints: per-token. Storage and network egress have separate allowances and rates.Serverless per-token; dedicated inference per GPU-minute for ready replicas; GPU clusters and provisioned throughput have separate contracts.
GPU choiceChoose supported GPU types through the function GPU setting, including T4, L4, A10, L40S, A100, RTX PRO 6000, H100, H200, B200 and B300; allocation and multi-GPU limits vary.Managed serverless; dedicated deployment profiles with selected hardware; separate GPU clusters for infrastructure control.
Cold startCan be noticeable on cold containers; benchmark the real image and model.Dedicated replicas need provisioning/model loading after restart or zero replicas; ready warm replicas continue billing while idle. No universal startup latency is established.
Model accessPackage models in images or pull at runtimeServerless catalog plus supported dedicated models, fine-tunes and uploaded LoRA adapters. Custom deployment profiles must meet Together's supported-runtime requirements.
Best forDevelopers who want GPU code as functions with autoscalingServing open-weight models without operating your own GPU fleet
Watch outYou can choose a specific supported GPU type, but containers and scaling remain managed. Warm idle containers consume billable resources. Starter compute credits do not cover Shared Endpoints tokens.Serverless token APIs, dedicated GPU hours and provisioned throughput have different cost models. Each PTU represents reserved capacity; tokens per minute depend on the model and token type. Together announced $800M in Series C funding on July 1, 2026; this does not guarantee endpoint availability or service levels.
GPU choice (1–10)Winner: 6/103/10
Time-to-serving (editorial) (1–10)7/10Winner: 9/10
Price clarity (1–10)7/107/10
Production ops (1–10)8/108/10
Open-model breadth (1–10)7/10Winner: 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 functions: per-second cpu, memory and gpu resources; shared endpoints: per-token. storage and network egress have separate allowances and rates.; Together AI uses serverless per-token; dedicated inference per gpu-minute for ready replicas; gpu clusters and provisioned throughput have separate contracts.. 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: Choose supported GPU types through the function GPU setting, including T4, L4, A10, L40S, A100, RTX PRO 6000, H100, H200, B200 and B300; allocation and multi-GPU limits vary.. Together AI: Managed serverless; dedicated deployment profiles with selected hardware; separate GPU clusters for infrastructure control.. 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; benchmark the real image and model.. Together AI: Dedicated replicas need provisioning/model loading after restart or zero replicas; ready warm replicas continue billing while idle. No universal startup latency is established.. 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: You can choose a specific supported GPU type, but containers and scaling remain managed. Warm idle containers consume billable resources. Starter compute credits do not cover Shared Endpoints tokens. Serverless token APIs, dedicated GPU hours and provisioned throughput have different cost models. Each PTU represents reserved capacity; tokens per minute depend on the model and token type. Together announced $800M in Series C funding on July 1, 2026; this does not guarantee endpoint availability or service levels.