Together AI Pricing Plans & Tiers

Together AI's pricing model exposes a critical gap in the AI infrastructure market: the platform offers six separate pricing tiers (Serverless, Dedicated, GPU On-Demand, GPU Reserved, Sandbox, Fine-Tuning) with no public unit costs disclosed. This opaqueness is intentional—Together targets infrastructure-savvy organizations (ML engineers, research labs) who will request custom quotes rather than self-serve contracts. The absence of published pricing is a signal that Together's real business is not SaaS but consulting and infrastructure partnerships, positioning the company as an outsourced ML ops provider rather than an API vendor.

Dedicated Inference (private GPU instances with guaranteed performance) targets teams optimizing for inference latency at scale—real-time recommendation systems, low-latency LLM APIs, and production ML pipelines where shared infrastructure introduces unacceptable variance. Organizations choosing Dedicated sacrifice commodity pricing for performance guarantees, a tradeoff that's only economic for workloads worth >$10K/month in infrastructure cost. This tier creates natural segmentation where high-volume production teams self-select into expensive contracts.

GPU Clusters (On-demand and Reserved) blur the line between AI API and infrastructure service. Teams can provision raw GPU capacity and run any workload, not just inference—fine-tuning, training, and batch processing share the same cost model. This flexibility comes at the cost of operational overhead: provisioning clusters requires ML engineering expertise, making this tier inaccessible to product teams without dedicated ML infrastructure staff.