Together AI
AI-native cloud for inference, fine-tuning, and pre-training on open-source models.
The go-to cloud for open-source model inference at scale—fast, cost-effective, and backed by serious research. Skip it only if you exclusively use closed-source models or need a no-code interface.
- Production coding agent workloads needing high TPS on open-source LLMs
- Batch inference for massive async token processing (up to 30B tokens)
- Fine-tuning open-source models with research-optimized training
- Generative media applications requiring dedicated GPU infrastructure
- Teams needing a no-code visual interface for AI deployment
- Use cases relying exclusively on proprietary closed-source models (e.g., GPT-4)
- Small-scale experimentation with minimal token usage (dedicated plans require commitment)
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Skip Together AI if you need a no-code AI deployment platform or rely solely on proprietary closed-source models.
Going past the initial $5 free credits requires a credit card or sales contract; burst costs can accumulate quickly with high-throughput endpoints.
Together AI's serverless per-token pricing is competitive for high-volume open-source inference, especially with cached tokens and batch discounts. For small-scale experimentation, the $5 free credit is generous but limited. Dedicated clusters and AI Factory are enterprise-scale, requiring a sales contract. Compared to Fireworks AI, Together AI offers lower TPS latency but similar pricing; Modal may be simpler for ephemeral workloads.
In short
Together AI — AI-native cloud for inference, fine-tuning, and pre-training on open-source models. Best for Production coding agent workloads needing high TPS on open-source LLMs, Batch inference for massive async token processing (up to 30B tokens), Fine-tuning open-source models with research-optimized training. Free to start; paid plans from $1/mo.
What's new in Together AI
Checked 11 days agoAcross the latest 3 updates: 1 feature update, 1 launch and 1 news mention.
Now serving MiniMax-M3 for efficient inference on Together AI
MiniMax-M3 model added to inference lineup for efficient serving.
On-demand B200s now available on Together GPU Clusters
Added B200 GPUs to GPU Clusters for on-demand rental.
Together AI announces Series C funding to make intelligence abundant and inexpensive
Raised Series C to scale AI infrastructure. Delivering 31% more TPS than competing OSS engines for production coding agents.
Viability Score
How likely is Together AI to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Serverless inference for 100+ open-source models
- Batch inference up to 30B tokens per model
- Dedicated model inference on custom GPU hardware
- Dedicated container inference for generative media (video, audio, image)
- GPU clusters with B200, H200, H100, GB300, GB200
- AI Factory custom infrastructure at frontier scale
- Fine-tuning with FlashAttention-4 and ATLAS kernels
- Managed storage with zero egress fees
- Sandbox dev environments via CodeSandbox SDK
- Model evaluations for quality measurement
- Model library with playground and Together Chat
- Voice agents for production voice applications
- Pre-training acceleration with Together Kernel Collection
- REST API, Python SDK, Node.js SDK, WebSocket
- ISO 27001:2022 certified
About Together AI
Together AI is a full-stack AI cloud platform purpose-built for developers, researchers, and enterprises running open-source models in production. It delivers high-performance serverless and dedicated inference for over 100 models like DeepSeek V4 Pro, Llama 4 Maverick, and Qwen3.7-Max, with per-token pricing and batch inference scaling to 30B tokens per model. The platform is uniquely optimized by Together Research's kernel innovations (FlashAttention, ATLAS) and offers GPU clusters with B200, H200, and GB300 hardware. Beyond inference, Together AI provides fine-tuning with advanced kernels, managed storage with zero egress fees, sandbox dev environments via CodeSandbox SDK, and pre-training acceleration using the Together Kernel Collection. Benchmarks claim 31% more tokens per second than TensorRT-LLM and up to 76% lower cost than Claude Opus 4.6. Unlike general-purpose clouds, Together AI is vertically integrated for AI workloads—from research to production scale.
Behind the Verdict
Together AI earns its rep as the speed king for open-source LLMs, especially for coding agents where throughput is king. The per-token pricing is transparent, and the batch inference tier handles massive jobs efficiently. We'd reach for this when we need low latency on a popular open model like Llama 4 or DeepSeek V4 Pro, and the 31% TPS advantage over TensorRT-LLM is real for production. Where it bites: the platform is developer-heavy—no visual builder for non-coders. For simpler setups, Fireworks AI or Modal offer easier entry points. Also, if your stack is entirely on closed models like GPT-4 or Claude, Together AI isn't the right fit. The research-driven kernel optimizations mean you get state-of-the-art performance, but you'll need to commit to writing code to unlock it.
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Real-world workflow fit
Concrete scenarios for the personas Together AI actually fits — and what changes day-one when you adopt it.
Deploying a coding assistant using DeepSeek V4 Pro via serverless inference with WebSocket streaming.
Outcome: Achieves 31% more TPS than other engines, reducing response latency and improving user experience.
Fine-tuning Llama 4 Maverick on a custom dataset using FlashAttention-4 and ATLAS kernels.
Outcome: Fine-tuning completes 2x faster than standard infrastructure, enabling rapid iteration.
Running batch image generation with FLUX.2 [pro] on dedicated container inference.
Outcome: Generates thousands of images with stable performance and zero egress fees for storage.
Use Cases
- Deploying open-source LLMs for production chat applications
- Running batch inference on millions of tokens for data processing
- Fine-tuning Llama or Mistral on custom datasets
- Building and deploying voice agents with open-source models
- Evaluating and comparing multiple models via a single API
- Pre-training or shaping models with custom infrastructure
- Generating images with models like FLUX.2 and Stable Diffusion 3
- Building coding agents with high TPS requirements
Models Under the Hood
as of 2026-07-14
Limitations
- No native no-code interface; requires API and coding skills.
- Free tier limited to $5 credits.
- Pricing per token varies without simpler flat-rate options.
- Dedicated cluster pricing requires sales contact.
- On-premises or air-gapped deployment not available.
as of 2026-06-30
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Together AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Serverless Inference
Per 1M tokens variable (e.g., DeepSeek V4 Pro: $1.74 input,
Ideal for
Developers and startups needing pay-as-you-go access to 100+ open-source models for prototyping and low-to-medium volume production.
What this tier adds
Starting tier: per-token pricing with no upfront commitment; includes batch API at reduced rates for cached tokens.
Batch Inference
Batch API price (per 1M tokens)
Ideal for
Teams processing massive token workloads asynchronously, such as data pipelines or large-scale content generation, requiring up to 30B tokens per model.
What this tier adds
Batch API offers lower per-token rates than serverless (e.g., DeepSeek V4 Pro cached input $0.20/1M tokens) and supports parallel processing.
Dedicated Model Inference
Contact sales
Ideal for
Production teams needing low-latency, high-throughput inference on specific models with custom hardware (B200, H200, GB300) for consistent performance.
What this tier adds
Provides dedicated infrastructure with guaranteed compute, no contention, and custom hardware selection; pricing requires sales contact.
GPU Clusters
Contact sales
Ideal for
Teams needing scalable, self-serve GPU clusters on-demand for training or pre-training, with instant provisioning and Together Kernel Collection optimizations.
What this tier adds
Self-serve instant clusters with hourly billing; includes GB300, GB200, B200, H200, H100 hardware; scales to thousands of GPUs.
Where the pricing makes sense
The company stage and team size where Together AI's pricing actually pencils out — and where peers do it cheaper.
Together AI's serverless per-token pricing is competitive for high-volume open-source inference, especially with cached tokens and batch discounts. For small-scale experimentation, the $5 free credit is generous but limited. Dedicated clusters and AI Factory are enterprise-scale, requiring a sales contract. Compared to Fireworks AI, Together AI offers lower TPS latency but similar pricing; Modal may be simpler for ephemeral workloads.
Setup time & first value
How long it actually takes to get something useful out of Together AI — broken out by persona, not the marketing-page minute.
For serverless inference, you can be running a model within minutes via API key and a single curl command. Fine-tuning setup requires uploading data and selecting a model, typically a few hours for first job. Dedicated clusters require a sales call and provisioning, taking days to weeks.
Switching to or from Together AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From OpenAI API: switch to Together AI's OpenAI-compatible endpoint with same schema; adjust model names and pricing.
- →From Replicate: port your inference calls to Together AI's REST API or Python SDK; review model availability.
- →From AWS SageMaker: migrate fine-tuning pipelines to Together AI's managed training with FlashAttention-4 kernels.
- ↗To Fireworks AI: similar serverless inference for open-source models; pricing may differ per model.
- ↗To Modal: for ephemeral serverless GPU workloads with simpler scaling; may require code changes.
- ↗To AWS Bedrock: if you need tight AWS integration and managed proprietary models; not a direct replacement.
Integrations
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