Together AI
Infrastructure for open-model inference, fine-tuning, dedicated endpoints, and GPU clusters.
About Together AI
Together AI is infrastructure for teams building applications with open-source and specialized models. A unified API provides serverless inference for text, image, video, code, and audio models. Dedicated endpoints cover workloads that need reserved capacity and steadier performance. The platform also handles fine-tuning and GPU clusters, allowing a team to evaluate models, adapt one to a task, and serve it in production without stitching together a provider for every stage. That range suits developers who want model choice and several deployment options under one account. Pricing is usage based and changes with the selected model or infrastructure, so workload estimates matter. Some library models do not run on the serverless API. Advanced support and dedicated infrastructure also sit in higher tiers or custom arrangements. Together AI fits engineering teams that value open-model access and infrastructure flexibility. It is less suitable for buyers looking for one flat subscription or a no-code application.
Best for
Best use cases
A team compares several supported models through one API before selecting a production model and estimating token costs.
An application needs a domain-adapted model, so the team fine-tunes a supported base model and deploys it on the same platform.
Traffic or latency requirements become less suitable for shared inference, prompting a move to a dedicated endpoint or GPU cluster.
Compare before choosing
Compared with a single-model API, Together AI provides broader open-model choice but requires model-by-model evaluation and cost tracking.
Compared with a flat SaaS subscription, consumption pricing scales with workload but makes the monthly bill less predictable.
Compared with managing raw GPU infrastructure alone, the platform adds managed APIs and fine-tuning paths, while dedicated resources can still require infrastructure planning.
Features
Multi-model application development
Evaluate and call open-source or specialized models for text, image, video, code, and audio through one provider interface.
Task-specific model fine-tuning
Adapt supported open models to domain data or application requirements, then keep training and serving within the same platform.
Production AI infrastructure
Move demanding workloads to dedicated endpoints or GPU clusters when serverless capacity and economics no longer fit the deployment.
Pros
- A unified API reduces integration work when testing supported models across several modalities.
- Multiple deployment paths let teams move from serverless inference to dedicated endpoints or GPU clusters.
- Fine-tuning and serving can stay with one provider, reducing handoffs between training and production workflows.
Cons
- Usage-based, model-specific pricing makes monthly costs harder to predict than a fixed subscription.
- Not every model shown in the library is available through the serverless API, so compatibility must be checked.
- Higher support levels and dedicated infrastructure are tied to enterprise arrangements, commitments, or additional cost.
Pricing Plans
Serverless Inference
- Unified API access to supported models
- Model-specific input and output pricing
- No reserved cluster required
Dedicated Endpoints
- Reserved deployment capacity
- More predictable production performance
- Choice of supported deployment configurations
Fine-Tuning
- Task-specific adaptation of supported models
- Training and serving within Together AI
- Costs depend on model and workload
GPU Clusters
- GPU infrastructure for training or serving
- On-demand cluster options
- Gold support included for GPU Cluster customers
* Pricing information is for reference only. Please refer to the latest announcement on Together AI's official website.