Together AI serves and fine-tunes open models. You can call a hosted model through an API priced per token, rent GPU clusters by the hour, or deploy on dedicated hardware. Runpod rents GPU compute across development, inference and training, and leaves the model choices to you.
Both are credible answers for running open models. They differ most at the two ends: what happens when you want no infrastructure at all, and what happens when your workload does not need an H100.
Together AI rates below were read from their own pricing page on 21 August 2026.
| Capability | Together AI | Runpod |
|---|---|---|
| Per-token serverless inference | Large hosted model library | Public Endpoints, smaller catalog |
| Managed fine-tuning | Priced per training token | You run your own job |
| Development environment | Sandbox, VM-based, per vCPU and GiB | Pods, with a shell and a disk |
| Custom model serving | Dedicated Container Inference | Any standard Docker image |
| Cheapest published GPU | HGX H100 at $3.99/hr | RTX A5000 from {{gpu:rtx-a5000:community}}/hr |
| GPU models published | 3 with rates, 3 contact-sales | 24, including AMD MI300X |
| Billing granularity | Per hour on clusters, or per token | Per second |
| Reserved discount ladder | Published, 7 days to 181+ days | Savings plans at 6 months and 1 year |
Together AI capabilities and rates were read from together.ai/pricing on 21 August 2026 and may change.
Pricing on GPU clusters
Together AI publishes three GPU models with rates on their cluster product, all per GPU per hour, with a reserved ladder that shortens as the commitment lengthens.
| Card | Together AI on-demand | Together AI 91-180 days | Runpod Secure |
|---|---|---|---|
| HGX H100 | $3.99/hr | $3.19/hr | {{gpu:h100-sxm}}/hr |
| HGX H200 | $5.99/hr | $3.99/hr | {{gpu:h200}}/hr |
| HGX B200 | $8.19/hr | $6.79/hr | {{gpu:b200}}/hr |
Together AI rates were read from together.ai/pricing on 21 August 2026 and change without notice. GB200 NVL72, GB300 NVL72 and HGX B300 are contact-sales on their cluster product. Their separate Dedicated Inference product lists an HGX H100 at $5.49/hr and an HGX B200 at $8.99/hr on demand. Runpod rates pull live.
The structural point matters more than the per-card gap. Together AI publishes nothing below the H100. If your workload fits comfortably on a 24GB or 48GB card, there is no economical option on their cluster product. Runpod publishes ten cards at or under {{gpu:l40s}}/hr, starting at {{gpu:rtx-a5000:community}}/hr.
What Together AI does well
Serverless inference with no infrastructure at all. Their per-token API covers a large library of open and proprietary models across chat, vision, image, audio, video, transcription, embeddings and reranking, with no deployment step, no container and no GPU decision. Runpod has no equivalent at that breadth. For a team that wants to call a model and move on, this is a real advantage.
Managed fine-tuning. You send data and get a tuned model back, served on the same platform, priced per training token with LoRA and full fine-tuning tiers by model size. Runpod gives you the GPUs to run your own training job, which is a different product even though it satisfies the same sentence.
Self-serve clusters with a published reserved ladder. Rates step down from on-demand through 7-30, 31-90 and 91-180 day commitments, all published rather than negotiated. An H100 at $3.19 on a 91-180 day commitment is competitive for steady load.
More platform than a rate card suggests. They also publish Provisioned Throughput in reserved capacity units, batch inference, evaluations, managed storage at $0.16 per GiB per month, and a Sandbox product for development environments and secure code execution.
Where Together AI stops
No tier below the H100. The single biggest constraint. Development, small fine-tunes and modest inference all get charged at data center rates or happen somewhere else.
Hourly billing on clusters. Their GPU cluster rates are quoted per hour. Runpod bills per second on Pods and Serverless, which matters on short or bursty work.
Their development environment is a sandbox, not a GPU workstation. Together AI's Sandbox is priced per vCPU and per GiB of RAM for VM-based development environments and code execution. It is not the same as renting a GPU box with a shell and a persistent disk to work on a model interactively.
The model library shapes the experience. Custom models are supported through Dedicated Container Inference, but the platform is built around their served library. If you want full control of the container and the serving stack as the default rather than the exception, that is a different product.
Where Runpod fits
Runpod covers three workload shapes on one account with the same container images. Pods for development and long-running jobs. Serverless for inference that scales to zero between requests, with sub-200ms cold starts via FlashBoot. Instant Clusters for multi-node training, self-serve.
The lineup runs 24 GPU models from an RTX A5000 through to a B300, including MI300X. Billing is per second with no minimum, there are no ingress or egress fees, and the platform is SOC 2 Type II across 31 global regions, independently verified for HIPAA and GDPR.
The practical difference is cost shape across a project. Prototyping on a small card, fine-tuning on an A100, serving on Serverless and training on a cluster are four different profiles. On a platform whose floor is an H100, they are one profile.
Which one should you choose: Together AI or Runpod?
Choose Together AI if you want to call open models through an API with no infrastructure, if you want fine-tuning managed for you, or if your workload genuinely needs H100s and runs continuously enough to use their reserved ladder.
Choose Runpod if any part of your work fits on a card smaller than an H100, if you need a GPU environment to develop in rather than only serve from, if you want full container control by default, or if you want per-second billing rather than hourly.
The honest split: Together AI sells inference as a service, with a genuinely deep platform around it. Runpod sells the infrastructure to run inference, training and development yourself. If you want fine-tuning as a service, they are the better answer. If you want it as a capability you control, on hardware sized to the job, that is the case for Runpod.
Frequently asked questions
Is Together AI expensive?
Not for what it does, but it has no cheap tier. Their lowest published GPU-hour on clusters is an HGX H100 at $3.99, with H200 at $5.99 and B200 at $8.19. Reserved commitments bring the H100 to $3.19 at 91 to 180 days. If your workload needs an H100 that is competitive. If it does not, you are paying H100 rates for a job that could run on a 24GB card.
Is Runpod cheaper than Together AI?
On the cards both offer, yes. Runpod's H100 SXM is {{gpu:h100-sxm}}/hr on Secure Cloud against $3.99, the H200 is {{gpu:h200}}/hr against $5.99, and the B200 is {{gpu:b200}}/hr against $8.19. The larger gap is below the H100, where Together AI publishes nothing and Runpod starts at {{gpu:rtx-a5000:community}}/hr.
Does Runpod offer per-token inference like Together AI?
Runpod Public Endpoints price hosted models per token or per request, but the catalog is smaller than Together AI's library. Runpod Serverless scales to zero between requests, though you still deploy a worker. If a broad per-token model library is what you want, that is a genuine advantage Together AI holds.
Does Together AI have a development environment?
They offer Sandbox, which provides VM-based development environments and secure code execution priced per vCPU and per GiB of RAM. It is not a GPU workstation with a persistent disk. Runpod Pods give you exactly that, from {{gpu:rtx-a5000:community}}/hr on Community Cloud.
Can I fine-tune on both?
Yes, differently. Together AI offers managed fine-tuning priced per training token, with LoRA and full fine-tuning tiers by model size and a minimum charge per job. Runpod gives you Pods and Instant Clusters to run your own training job, which means you control the framework, checkpoints and hyperparameters, and there is no per-token training tariff.
Which has more GPU models: Together AI or Runpod?
Runpod, with 24 published models from an RTX A5000 through to a B300, including AMD MI300X. Together AI publishes three with rates on its cluster product, plus GB200 NVL72, GB300 NVL72 and HGX B300 available by contacting sales.
