Together AI
What is Together AI?
Together AI is an AI-native cloud for AI teams that turns model requests, training jobs, and cluster workloads into managed production infrastructure. It supports serverless inference, batch inference, dedicated model inference, GPU clusters, fine-tuning, evaluations, and observability. The platform supports 200+ models and is used by Cursor, Decagon, and Vercept. Pricing starts with usage-based serverless inference tiers such as GLM-5.1 at $1.40, MiniMax M2.7 at $0.30, and Kimi K2.6 at $1.20.
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At a glance
- Together AI is best for AI teams who need production inference, fine-tuning, and GPU compute in one platform.
- Yes — The page advertises self-service GPU clusters accessible via an API endpoint and links to cluster docs.
What does Together AI do?
Together AI runs an AI-native cloud that turns model requests, training jobs, and cluster workloads into managed production infrastructure. Its serverless inference, batch inference, and dedicated inference paths let teams serve chat, vision, audio, transcribe, embeddings, rerank, and moderation workloads without building their own serving stack. The platform also extends into fine-tuning, evaluations, sandbox environments, managed storage, and GPU clusters, so teams can move from experimentation to deployment in one place. At scale, Together AI shows research-backed performance and reliability: it cites up to 4x faster LLM inference, 90% faster training on NVIDIA Blackwell GPUs, 99.9% uptime, and support for 200+ models. Customer stories include Cursor, Decagon, and Vercept, with examples like 11x faster inference and 6x cost reduction per turn vs. Gpt-5 mini. Self-service GPU clusters are available through an API endpoint at https://api.together.ai/clusters.
Why use Together AI?
- Research-led systems work is built into the platform, so teams can benefit from performance gains without assembling their own kernel stack.
- Self-service GPU clusters and API access reduce the friction of moving from prototype workloads to production-scale compute.
- The platform combines inference, fine-tuning, evaluations, and storage, which cuts down on tool sprawl across the AI lifecycle.
- Together AI publishes production reliability details like 99.9% uptime, tenant-level isolation, and SOC 2 Type II encryption in transit and at rest.
- Customer stories show measurable outcomes such as 11x faster inference and 6x cost reduction per turn vs. Gpt-5 mini.
Who is Together AI for?
- ML platform teams who need managed inference and cluster infrastructure for production workloads.
- Applied AI engineers who want to ship chat, vision, audio, and embedding features quickly.
- Research teams who need large-scale GPU compute for training and model shaping.
- Product teams building voice or agent experiences that need low-latency serving.
- Enterprise AI teams that need reliability, observability, and security controls.
What are Together AI's key features?
Serverless Inference
Run 200+ models on demand with serverless inference, including gpt-oss-120B and Qwen3.5 9B, so teams avoid managing capacity.
Batch Inference
Process large jobs in batches for models like MiniMax M2.7 and DeepSeek V4 Pro, which helps cut per-request overhead for offline workloads.
Dedicated Model Inference
Deploy dedicated model endpoints for predictable latency on large models such as 120B and 403.4B parameter systems, useful for steady production traffic.
GPU Clusters
Provision self-service GPU clusters through the API at api.together.ai/clusters, with Kubernetes and Slurm support for managed scaling and orchestration.
Fine-Tuning
Train and adapt models with fine-tuning for vision and tool-calling, backed by support for 100B+ param models and trillion-token inference.
Evaluations
Score, compare, and classify model outputs with evaluation workflows, helping teams measure quality before shipping changes to production.
Voice Agents
Build real-time voice agents with audio, transcribe, and real-time model support, which matters for low-latency conversational apps.
Observability
Track inference and training behavior with observability, health checks, and automated remediation, reducing downtime across managed infrastructure.
What does Together AI integrate with?
- Kubernetes
- Slurm
- Slack
- Discord
- Hugging Face Hub
What are Together AI's use cases?
Applied AI features ship faster
Applied AI engineers use Together AI to launch chat, vision, audio, and embedding features without building serving from scratch. They lean on Serverless Inference and Embeddings to get production responses quickly, then switch to Dedicated Model Inference when a customer workflow needs steadier latency.
Voice agents with low latency
Product teams building voice or agent experiences use Together AI to keep interactions responsive under load. Voice Agents and Real-time serving help them prototype and ship conversational flows, while Observability makes it easier to catch latency spikes before users notice them.
Production inference for platform teams
ML platform teams use Together AI to run managed inference and cluster infrastructure for production workloads. They combine GPU Clusters with Dedicated Container Inference to standardize deployment, and use Production reliability & security plus Health checks to keep services stable.
Research training at larger scale
Research teams use Together AI to shape and train models on large GPU capacity without managing the full stack themselves. GPU Clusters and Fine-Tuning support iterative experiments, while Evaluations help them compare runs and choose the model that performs best.
How does Together AI work?
- Connect your first workload to Serverless Inference or GPU Clusters, then choose the model or cluster shape that matches your latency, throughput, or training needs.
- Route requests through the API and configure Dedicated Model Inference or Dedicated Container Inference when you need steadier performance for production traffic.
- Add Fine-Tuning or Batch Inference for model shaping jobs, then use Evaluations to compare outputs and pick the best-performing version.
- Turn on Observability and Health checks to watch latency, errors, and throughput, so your team can spot regressions before they affect users.
- Scale into Voice Agents, Managed Storage, or Kubernetes and Slurm integrations as workloads grow, keeping deployment and operations in one workflow.
Frequently asked questions
What is Together AI?
Together AI is an AI-native cloud for AI teams that turns model requests, training jobs, and cluster workloads into managed production infrastructure. It supports serverless inference, batch inference, dedicated model inference, GPU clusters, fine-tuning, evaluations, and observability. The platform supports 200+ models and is used by Cursor, Decagon, and Vercept. Pricing starts with usage-based serverless inference tiers such as GLM-5.1 at $1.40, MiniMax M2.7 at $0.30, and Kimi K2.6 at $1.20.
How much does Together AI cost? Is it free?
Together AI has 66 paid plans: Serverless Inference: GLM-5.1 at $1.40, Serverless Inference: MiniMax M2.7 at $0.30, Serverless Inference: Kimi K2.6 at $1.20.
What is Together AI used for? Who is it for?
Together AI is used for Serverless Inference, Batch Inference, and Dedicated Model Inference. It's built for ML platform teams, Applied AI engineers, and Research teams.
Does Together AI have an API and what does it integrate with?
The page advertises self-service GPU clusters accessible via an API endpoint and links to cluster docs. It integrates with Kubernetes, Slurm, Slack, Discord, Hugging Face Hub.
Editor's read
Check the usage-based inference pricing against your expected request mix, especially if you plan to rely on higher-priced models like GLM-5.1 or Kimi K2.6. The per-request cost can vary sharply by model and cached-input usage.
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