Together AI’s official platform lists its current models and infrastructure services.
What does Together AI do?
Together AI acts as an inference provider for many open weight models and offers training or customization services. It separates model selection from the work of provisioning and operating model servers. That can shorten delivery time while preserving more model choice than a single-model API.
Hosted access still needs workload testing because throughput, latency, and regional support vary.
When should a company evaluate Together AI?
It is relevant when a team wants open model choice without owning the serving stack. Compare representative task quality, concurrency, reliability, data controls, and total cost. If customization is required, evaluate whether fine-tuning produces enough measurable benefit to justify the added lifecycle. Keep a multi-provider AI design only when a second provider solves a concrete reliability or capability need.
How should open-model serving be compared?
Pin the exact model, revision, precision, and serving configuration before benchmarking. Measure accepted-output cost, queue time, token throughput, and behavior under realistic concurrency, not only a single fast request. If a model is customized, maintain a separate evaluation record and rollback path for every release. A managed service removes infrastructure work, but the team still owns model selection and application quality. Compare Together AI with self-hosted inference and another inference provider using the same traffic profile and data requirements.