Together AI Review (2026): Features, Pricing, Pros & Cons

Together AI Overview

This Together AI review covers the inference cloud that became one of the biggest names in open-weight AI by 2026. Together AI lets you run more than 200 open models — Llama, Qwen, DeepSeek, Mistral — through a fast, OpenAI-compatible API, without provisioning a single GPU. For teams that want to avoid proprietary lock-in, it is the platform to know.

What is Together AI?

Together AI is an AI-native cloud for open and open-weight model inference at scale. The default product is serverless inference: you call an API, you pay per token, and Together handles the GPUs. Rates vary by model size — small turbo models start around $0.10 per million tokens, mid-range 70B-class models run roughly $0.60 to $1.04 per million, and large 70B+ models land in the $2 to $5 per million range.

Beyond serverless, there are dedicated endpoints (from around $0.85/hr for smaller GPUs, with a dedicated H100 at about $6.49/hr) for guaranteed capacity and latency, plus on-demand GPU clusters for teams that want raw compute. Batch inference discounts serverless prices by up to 50% for non-real-time workloads.

How Together AI Works

You point your existing OpenAI SDK at Together’s endpoint with your API key, pick from a curated catalog of production-recommended models, and start sending requests. Switching models is a parameter change, not a redeploy — handy for A/B testing or cost optimization across providers.

The fine-tuning story is a real differentiator. Together supports LoRA and full fine-tuning across Llama, Mistral, Qwen, and DeepSeek bases, and your fine-tuned model deploys straight to serverless or dedicated inference with no export step. It now also serves image (FLUX), video (Veo, Sora), and voice models, plus dedicated container inference for teams bringing their own runtime.

Who Should Use Together AI?

Together AI fits teams running high-volume open-source LLM inference who want production tooling without GPU operations. It is especially strong for fine-tuned custom models and for companies with an explicit open-weights strategy or data-portability requirements. SOC 2 Type II compliance makes it viable for regulated workloads too.

It is the wrong tool if your app depends on proprietary frontier models — you cannot get Claude or GPT here, so multi-model pipelines need a second integration. And if you are chasing the absolute cheapest per-token price on a specific model, compare against the model lab’s own API and rivals like Fireworks, Groq, and DeepInfra, since Together is competitive but not always cheapest.

Our Verdict on Together AI

Together AI is the most complete open-model inference platform in 2026: deep catalog, serious fine-tuning, and per-token pricing that kills idle GPU costs. The open-weights-only catalog is a feature for lock-in-averse teams and a dealbreaker for everyone else.

The bottom line of this Together AI review: if your roadmap is open models, this is the platform to build on — just price-shop your heaviest models before committing. Explore more options in our AI Developer Tools category.

Key Features

Serverless per-token inference across 200+ open-weight models
OpenAI-compatible API endpoints for drop-in integration
LoRA and full fine-tuning with direct deployment to inference
Dedicated endpoints from $0.85/hr for guaranteed capacity and latency
Batch inference with up to 50% discount for non-real-time workloads
Image, video, and voice models alongside the text catalog
Dedicated container inference for bring-your-own-runtime workloads

Together AI Pricing

Plan	Price
Serverless (Turbo)	from $0.10/M tokens
Serverless (70B)	~$1.04/M tokens
Serverless (70B+)	$2-$5/M tokens
Dedicated H100	$6.49/hr
Enterprise	Custom

Pricing checked on November 2, 2026 — always confirm on the official site.

Together AI Pros & Cons

✓ Pros

One of the deepest open-model catalogs with production-ready curation
Per-token pricing eliminates idle GPU costs for variable workloads
Best-in-class fine-tuning pipeline that deploys straight to inference
OpenAI-compatible API makes migration nearly frictionless
No proprietary lock-in — open models stay portable

✕ Cons

Open-weight models only — no Claude, GPT, or Gemini, so multi-model apps need a second provider
Per-token pricing is not always the cheapest; some models cost notably more than the lab's own API
No automatic failover between regions, so infrastructure issues become your downtime
EU data residency and region pinning are gated behind the higher tiers

Together AI FAQs

How much does Together AI cost in 2026?

Serverless inference is per-token and varies by model: small models from about $0.10 per million tokens, 70B-class models around $0.60-$1.04 per million, and large 70B+ models $2-$5 per million. Batch inference cuts those rates by up to 50%, and a dedicated H100 endpoint runs about $6.49/hr.

Does Together AI have a free tier?

There is no advertised free tier on the public pricing page. New users have historically received a small introductory credit, but it is not published as a standing offer, so treat any credit as a bonus rather than a plan.

Can I fine-tune models on Together AI?

Yes. Together offers LoRA and full fine-tuning across Llama, Mistral, Qwen, and DeepSeek bases, billed per training token scaled by model size and method. Fine-tuned models deploy directly to serverless or dedicated inference with no export step, though hosting runs on a dedicated endpoint.

Does Together AI offer Claude or GPT models?

No. Together AI serves open and open-weight models only — no proprietary frontier models like Claude, GPT, or Gemini. Multi-model apps that need those must integrate a second provider alongside Together.

What are the best Together AI alternatives?

Fireworks AI competes directly on open-model inference with similar pricing, Groq is the speed leader on custom hardware, DeepInfra is often cheaper on specific models, and OpenRouter aggregates both open and proprietary models behind one API.

Best Together AI Alternatives

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