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Inference comparison

RunPod vs Together AI

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

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

GPU marketplace

RunPod

Pods: per-second compute and local disk billing; network volumes: hourly storage billing. Serverless Flex/Active workers: per-second lifecycle time.

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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RunPod vs Together AI GPU inference comparison
DimensionRunPodTogether AI
CategoryGPU marketplaceManaged inference API
BillingPods: per-second compute and local disk billing; network volumes: hourly storage billing. Serverless Flex/Active workers: per-second lifecycle time.Serverless per-token; dedicated inference per GPU-minute for ready replicas; GPU clusters and provisioned throughput have separate contracts.
GPU choiceBroad consumer and datacenter SKUsManaged serverless; dedicated deployment profiles with selected hardware; separate GPU clusters for infrastructure control.
Cold startDepends on pod spin-up and image cacheDedicated replicas need provisioning/model loading after restart or zero replicas; ready warm replicas continue billing while idle. No universal startup latency is established.
Model accessBring your own weights or pull from registriesServerless catalog plus supported dedicated models, fine-tunes and uploaded LoRA adapters. Custom deployment profiles must meet Together's supported-runtime requirements.
Best forTeams that want to pick a specific GPU SKU and control the container stackServing open-weight models without operating your own GPU fleet
Watch outPod compute stops when a Pod stops, but persistent volume disks ($0.20/GB-month stopped) and network volumes ($0.07/GB-month under 1TB; $0.05/GB-month above 1TB) continue billing until deleted. Temporary container disk is erased on stop. Serverless worker billing and always-on worker settings differ from Pod billing.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: 9/103/10
Time-to-serving (editorial) (1–10)6/10Winner: 9/10
Price clarity (1–10)Winner: 8/107/10
Production ops (1–10)6/10Winner: 8/10
Open-model breadth (1–10)8/10Winner: 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 RunPod when teams that want to pick a specific gpu sku and control the container stack. 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

RunPod vs Together AI

Answered from the verified figures on this page rather than general guidance.

How are RunPod and Together AI billed?
RunPod uses pods: per-second compute and local disk billing; network volumes: hourly storage billing. serverless flex/active workers: per-second lifecycle time.; 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, RunPod or Together AI?
RunPod: Broad consumer and datacenter SKUs. Together AI: Managed serverless; dedicated deployment profiles with selected hardware; separate GPU clusters for infrastructure control.. RunPod scores higher for GPU choice in our editorial fit ratings (9/10 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, RunPod or Together AI?
RunPod: Depends on pod spin-up and image cache. 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 6/10), 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 RunPod or Together AI?
Choose RunPod when teams that want to pick a specific gpu sku and control the container stack. Choose Together AI when serving open-weight models without operating your own gpu fleet. Watch out: Pod compute stops when a Pod stops, but persistent volume disks ($0.20/GB-month stopped) and network volumes ($0.07/GB-month under 1TB; $0.05/GB-month above 1TB) continue billing until deleted. Temporary container disk is erased on stop. Serverless worker billing and always-on worker settings differ from Pod billing. 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.
Does stopping a rented GPU stop the bill?
Not always. Stopping a pod or instance can leave disks/volumes attached and still billing. Terminate and confirm storage teardown when the experiment ends — especially on marketplace hosts like RunPod and Vast.ai.
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.