Together AI Review: Features, Pricing and Who It Suits Best
Together AI review: 200+ open models behind an OpenAI-compatible API, cheap serverless pricing, fine-tuning and GPU clusters, with the trade-offs that matter.
By Galactic News Editorial · Published 2026-11-10T07:24:18+00:00 · Updated 2026-11-10T07:24:18+00:00
Together AI is a cloud platform for running open-weight models at scale. It offers serverless inference, dedicated endpoints, GPU clusters, and fine-tuning behind one OpenAI-compatible API. It hosts more than 200 open models, from Llama and DeepSeek to Qwen, Mistral, and Kimi. This review covers what it does, what it costs, and who it suits best, based on the official pricing page and documentation.
The short version: Together AI suits developers who want open models with a simple API and a clear upgrade path. Serverless use is cheap and fast to start. Heavy production workloads move to dedicated endpoints or raw GPU clusters. The main weakness is serverless reliability. Shared capacity can throttle under load. Models can also leave the serverless list on short notice.
What Together AI is
Together AI calls itself an AI-native cloud. It runs open-weight models so developers do not have to manage GPUs. The core product is serverless inference. You send a request to the API. Together AI picks the hardware. You pay per token. The API follows the OpenAI format, so existing code can switch by changing the base URL to api.together.ai/v1.
The platform also sells dedicated inference. A model runs on GPUs reserved for one customer. That brings steady latency and no shared-queue throttling. GPU clusters rent raw NVIDIA hardware by the hour, from H100s to B300s. Fine-tuning trains open models on your data with LoRA or full fine-tuning. A code sandbox runs LLM-generated code in a managed environment.
Getting started takes minutes. The docs quickstart covers three steps. Create an account and an API key. Install the Python or TypeScript SDK. Send a chat completion request. The SDK reads the TOGETHER_API_KEY environment variable. The OpenAI SDK also works when pointed at the Together base URL.
Together AI review: features that matter
The model library is the main draw. The pricing page lists around 30 serverless chat models, plus image, video, audio, embedding, and transcription models. Open-weight families include Llama, DeepSeek, Qwen, Mistral, Gemma, GLM, Kimi, and MiniMax. New models arrive fast. The list changes often, which is both a strength and a risk.
OpenAI compatibility is the second strength. Chat completions, streaming, structured JSON output, and image input all work through the familiar format. Developers can test a model on Together AI without rewriting their client code. That lowers the cost of trying it.
Fine-tuning is the third strength. Supervised fine-tuning and direct preference optimization are both offered. Prices start at $0.34 per million training tokens for small models like Llama 3.1 8B. Larger models cost more. Llama 3.3 70B costs $2.03 per million. DeepSeek-V3.1 costs $7.00. Each job has a minimum charge.
The sandbox and code interpreter add a fourth layer. Sandboxes bill $0.0446 per vCPU per hour and $0.0149 per GiB of RAM per hour. A 60-minute code interpreter session costs $0.03. These tools suit agents that run code as part of their work.
Pricing: what it costs in practice
Serverless pricing is pay-per-token with no subscription. Small models are nearly free. Ternary Bonsai 27B is listed at $0.00. The gpt-oss-120B model costs $0.15 per million input tokens and $0.60 output. DeepSeek V4.1 Flash costs $0.30 input and $1.20 output. At the top, Kimi K3 costs $3.00 input and $15.00 output. Cached input costs less on many models.
A small app sending 10 million input tokens and 2 million output tokens per month pays $2.70 on gpt-oss-120B. The same traffic on Kimi K3 costs $60.00. Model choice dominates the bill. Test the cheapest model that meets your quality bar first.
Dedicated inference costs $5.49 per H100 hour and $8.99 per B200 hour. GPU clusters cost $3.99 per H100 hour on demand, $5.99 for H200, $8.19 for B200, and $9.99 for B300. Preemptible H100 nodes cost $1.99 per hour. Reserved terms of 91 to 180 days drop the H100 cluster rate to $3.19 per hour.
Image generation bills per image. FLUX.2 [pro] costs $0.03 per image. GPT Image 2 costs $0.053. SD XL costs $0.0019. Speech bills per million characters. Kokoro-82M TTS costs $4.00. Cartesia Sonic-3 costs $65.00. Transcription with Whisper Large v3 costs $0.0015 per audio minute.
How Together AI compares with the alternatives
OpenAI and Anthropic sell their own frontier models. Together AI sells open models instead. That difference shapes the whole comparison. Together AI cannot offer GPT or Claude. It can offer many open models at lower prices, with fine-tuning and GPU rental in the same account.
| Attribute | Together AI | OpenAI / Anthropic | Replicate |
|---|---|---|---|
| Models | 200+ open-weight models | Own frontier models only | Open and hosted models |
| API format | OpenAI-compatible | Own API format | Own API format |
| Serverless billing | Per million tokens | Per million tokens | Per second or per run |
| Dedicated H100 | $5.49 per hour | Not sold as raw GPU | $5.49 per hour |
| GPU clusters | Yes, from $3.99 per H100 hour | No | Limited |
| Fine-tuning | Yes, LoRA and full | Yes, own models only | Yes, selected models |
Against other open-model hosts, Together AI stands out for breadth. It combines inference, fine-tuning, and GPU rental in one platform. A team can prototype on serverless, fine-tune a model, then move it to a dedicated endpoint without changing vendors. That single-vendor path is the product's real advantage.
The trade-off is serverless reliability. Shared capacity can throttle with 429 or 503 errors under load. Models leave the serverless list on two to three weeks' notice. A production system needs a fallback model and a plan to move to dedicated capacity.
Strengths and weaknesses
The strengths are clear. The model library is broad and current. The API is OpenAI-compatible, so switching costs are low. Pricing is transparent and published per model. Fine-tuning, dedicated endpoints, and GPU clusters give a real growth path. Small models are very cheap.
The weaknesses matter for production use. Serverless is best-effort and can throttle. Model removals force migrations. Pricing changes often, so a rate quoted today may differ next month. Support for the self-serve tier is limited compared with enterprise contracts.
Data handling deserves a check before use. Review the privacy and security terms for your workload. Confirm how prompts and outputs are stored. Confirm whether data is used for training. These terms matter most for sensitive or regulated data.
Who it suits best
Together AI suits developers building on open models. It suits teams that want to test many models quickly through one API. It suits startups that need cheap inference for a prototype. It suits teams that plan to fine-tune an open model and then serve it themselves.
It suits production workloads less well on the serverless tier alone. A production system needs the dedicated tier or a fallback provider. Teams that need a single frontier model with the highest capability should compare the model itself, not the platform. Open models keep improving, but the best closed models still lead on some tasks.
Enterprises with strict compliance needs should talk to sales before committing. The self-serve tier is built for speed. The enterprise tier adds the controls, support, and capacity guarantees that regulated work needs.
Bottom line
Together AI is a strong choice for developers working with open-weight models. The serverless tier is cheap, the API is familiar, and the path from prototype to dedicated GPUs is clear. Budget for the dedicated tier if your workload is production-critical. Keep a fallback model ready, because serverless listings change. For open-model development, it is one of the most complete platforms available.