Together AI Pricing (2026)

Pure usage-based across every surface — you're paying per token, per GPU-hour, per session, per GiB, depending on what you're touching. The value metric shifts by product: inference is token/output-based, compute is time-based, storage is volume-based, which means your bill is a direct function of how hard you're running the platform.

7 plans usage-based Best Together AI alternatives →
Serverless Inference
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Provisioned Throughput
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Dedicated Inference
From $5.49/GPU
GPU Clusters
From $3.99/GPU
Sandbox
From $0.0446/vCPU
Storage
From $0.16/GiB
Fine-Tuning
Contact Sales
Verified Sep 20, 2026 Official pricing page

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Serverless Inference

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  • Chat models (price per 1M tokens)
  • Vision models (price per 1M tokens)
  • Image generation models (price per image or per mp)
  • Audio / TTS models (price per 1M characters)
  • Video generation models (price per video)
  • Transcription models (price per audio minute)
  • Embeddings models (price per 1M tokens)
  • Moderation models (price per 1M tokens)
  • Batch API pricing available

Provisioned Throughput

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  • Reserved dedicated capacity in throughput units (PTUs)
  • Fixed capacity per PTU with model-dependent tokens-per-minute
  • MiniMax M3 support
  • GLM-5.2 support

Dedicated Inference

From $5.49/GPU
  • Guaranteed performance (no sharing)
  • Support for custom models
  • Autoscaling & traffic spike handling
  • NVIDIA HGX H100 on-demand ($5.49/GPU/hr)
  • NVIDIA HGX B200 on-demand ($8.99/GPU/hr)
  • NVIDIA HGX H200 (contact us)
  • NVIDIA HGX B300 (contact us)
  • NVIDIA GB200 NVL72 (contact us)
  • NVIDIA GB300 NVL72 (contact us)
  • Reserved capacity available

GPU Clusters

From $3.99/GPU
  • On-demand pay-as-you-go GPU capacity (hourly)
  • Reserved capacity (7-30 days, 31-90 days, 91-180 days, 181+ days)
  • NVIDIA HGX H100 on-demand ($3.99/GPU/hr)
  • NVIDIA HGX H200 on-demand ($5.99/GPU/hr)
  • NVIDIA HGX B200 on-demand ($8.19/GPU/hr)
  • NVIDIA GB200 NVL72 (contact us)
  • NVIDIA GB300 NVL72 (contact us)

Sandbox

From $0.0446/vCPU
  • Code Sandbox: VM sandboxes for large development environments ($0.0446/vCPU/hr, $0.0149/GiB RAM/hr)
  • Code Interpreter: Execute LLM-generated code securely via API ($0.03/session)

Storage

From $0.16/GiB
  • Shared Filesystem ($0.16/GiB/month)
  • High-bandwidth, parallel filesystem colocated with compute

Fine-Tuning

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  • Supervised Fine-Tuning (LoRA)
  • Supervised Fine-Tuning (Full Fine-Tuning)
  • Direct Preference Optimization (LoRA)
  • Direct Preference Optimization (Full Fine-Tuning)
  • Standard pricing for models up to 100B parameters
  • Specialized pricing for DeepSeek, GLM, Kimi, Llama 4, Qwen3, and other large models
  • Minimum charge per job: $4.00 (standard)

Does Together AI have a free plan?

No. Together AI lists no free plan or trial on its pricing page. Pricing is custom; you'll need to talk to sales.

Want free? Anthropic, AssemblyAI, and Baseten have free plans: see all Together AI alternatives →

AI Pricing Analysis

Pricing Model

Pure usage-based across every surface — you're paying per token, per GPU-hour, per session, per GiB, depending on what you're touching. The value metric shifts by product: inference is token/output-based, compute is time-based, storage is volume-based, which means your bill is a direct function of how hard you're running the platform.

Tier Strategy

There are no tiers in the traditional sense — it's more of a maturity ladder. Serverless is where you start (low commitment, pay-as-you-go), Provisioned Throughput is the step up when latency and rate limits start hurting, and Dedicated Inference or GPU Clusters are for teams that need guaranteed capacity or want to run custom workloads at scale. The upgrade trigger is almost always hitting a performance or cost-efficiency ceiling on serverless.

Competitive Positioning

Together AI is pitching itself as the serious infrastructure layer for AI builders — not the cheapest (Replicate or self-hosting can undercut on specific models), but more flexible and enterprise-ready than most managed inference APIs. At $5.49/GPU/hr for H100 dedicated, they're competitive with CoreWeave and Lambda Labs while bundling more managed services around it.

Growth Lever

Expansion is entirely consumption-driven — no seats, no feature gates, just more usage. The real pull toward higher spend is the shift from serverless to provisioned or dedicated compute, where teams lock in capacity and the per-unit economics improve but the baseline commitment goes up significantly.

Together AI Pricing FAQ

How much does Together AI cost?
Pricing is custom — you'll need to talk to their sales team for a quote.
Is there a free plan?
No free plan. You'll need to commit to a paid plan to get started.
Can I try it before paying?
Not at the moment. There's no free trial or free plan listed on their pricing page.
How does the pricing work?
Pure usage-based across every surface — you're paying per token, per GPU-hour, per session, per GiB, depending on what you're touching. The value metric shifts by product: inference is token/output-based, compute is time-based, storage is volume-based, which means your bill is a direct function of how hard you're running the platform.
Which plan makes sense for me?
There are no tiers in the traditional sense — it's more of a maturity ladder. Serverless is where you start (low commitment, pay-as-you-go), Provisioned Throughput is the step up when latency and rate limits start hurting, and Dedicated Inference or GPU Clusters are for teams that need guaranteed capacity or want to run custom workloads at scale. The upgrade trigger is almost always hitting a performance or cost-efficiency ceiling on serverless.

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