Together AI
Together AI is the AI cloud for open-weight inference — serverless APIs, batch jobs, provisioned throughput, and GPU clusters on one bill.
Together AI makes sense when open-weight models are the plan and cost per token is the number your team argues about. Serverless entry pricing is genuinely low — DeepSeek V4 Flash 0731 at $0.14 input, GLM-5.3-Flash at $0.15 — and the ladder up to Provisioned Throughput means you don't buy GPUs on day one. Skip it if you need a drag-and-drop builder or you've standardized on closed frontier models.
Verified 2d ago · liveness 88/100 · cite: rightaichoice.com/tools/together-ai
- Engineering teams running open-weight models in production
- AI startups moving from serverless trials to provisioned capacity
- Data teams processing very large token volumes asynchronously
- Media pipelines needing dedicated GPUs for video or audio models
- Teams that need a no-code visual builder or drag-and-drop agent designer
- Organizations standardized on closed-source frontier models
- Non-technical users who want a turnkey app without writing API code
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Skip Together AI if you want a visual agent builder or an end-user app rather than API code, or if your stack is committed to closed models like GPT-5.6 Sol.
Kimi K3's $3.00 input / $15.00 output rate is promotional and valid only until 10/11/2026 — budget for the increase after that date.
Together AI fits engineering teams spending roughly $50–$5,000/mo on tokens: serverless entry points like Ternary Bonsai 27B at $0.00 and GLM-5.3-Flash at $0.15 per 1M input tokens sit well under Fireworks or Azure OpenAI list rates for comparable open-weight models. Above that, Provisioned Throughput, Dedicated Model Inference, and GPU Clusters are sales-quoted, so mid-market teams comparing against AWS Bedrock's published endpoints should expect a scoping conversation rather than a self-serve
In short
Together AI — Together AI is the AI cloud for open-weight inference — serverless APIs, batch jobs, provisioned throughput, and GPU clusters on one bill. Best for Engineering teams running open-weight models in production, AI startups moving from serverless trials to provisioned capacity, Data teams processing very large token volumes asynchronously. Free to use.
What's new in Together AI
Checked 8 days agoAcross the latest 4 updates: 1 feature update, 1 launch and 2 news mentions.
Now serving MiniMax-M3 for efficient inference
MiniMax-M3 is now available on Together AI's serverless inference platform, optimized for efficiency.
On-demand B200s now available on Together GPU Clusters
Together AI adds on-demand B200 GPUs to its GPU cluster offerings, expanding compute options.
Together AI announces partnership with Y Combinator to deliver first dedicated YC GPU cluster
Together AI and Y Combinator partner to provide the first dedicated GPU cluster for YC startups.
DeepSeek V4 Pro 0813 vs. GPT-5.6 Sol on DeepSWE
Together AI publishes a benchmark comparison of DeepSeek V4 Pro and GPT-5.6 Sol on the DeepSWE benchmark.
What people actually say about Together AI — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
75 mentions across 4 sources (Hacker News, Bluesky, Stack Overflow, Lemmy) · researched Jul 6, 2026.
Average across the 4 sources that answered — each source counts once, not each post.
- +Supports 100+ open-source models with easy API integration.
- +Offers per-token pricing that is cheaper than Claude Opus by 76%.
- +Provides 31% more tokens per second than TensorRT-LLM.
- +Includes free $25 credit for new users to test models.
- +Managed storage with zero egress fees simplifies data handling.
- −Pricing may be VC-subsidized and could increase drastically.
- −Limited community feedback on support quality and uptime.
- −No ongoing free tier beyond initial trial credits.
- −Primarily benefits developers already comfortable with open-weight models.
- −Less brand recognition than major providers like OpenAI or Anthropic.
- • Per-token rates may rise if subsidies end
- • Custom hardware clusters require minimum commitments
Viability Score
How well maintained and how widely used is Together AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Serverless inference APIs across a large catalog of open-source models
- Chat, vision, image, audio, video, transcribe, embeddings, rerank, moderation endpoints
- Batch Inference scaling to 30 billion tokens per model
- Cached input discounts (MiniMax M3 at $0.06 per 1M cached tokens)
- Provisioned Throughput with reserved capacity and 99% uptime SLA
- Drop-in API compatibility moving from serverless to dedicated
- Dedicated Model Inference on hardware you control
- Dedicated Container Inference for video, audio, and image models
- GPU Clusters with GB300, GB200, B200, H200, and H100
- On-demand NVIDIA B200 GPUs on GPU Clusters
- Dedicated Y Combinator GPU cluster for YC startups
- Voice Agents for building production voice agents via API
- Together Link: run open models inside an existing agent harness
- Fine-tuning with FlashAttention and ATLAS research kernels
- Custom training and post-training with reinforcement learning
About Together AI
Together AI runs open-weight models as an API for engineering teams. Serverless inference covers chat, vision, image, audio, video, transcribe, embeddings, rerank, and moderation endpoints, billed per million tokens instead of per seat. The current rate card spans a wide range: Ternary Bonsai 27B at $0.00 in and out, GLM-5.3-Flash at $0.15 input / $0.50 output, DeepSeek V4 Flash 0731 at $0.14 / $0.28, MiniMax M3 at $0.30 input / $0.06 cached / $1.20 output, and Kimi K3 at $3.00 input / $0.30 cached / $15.00 output. Muse Spark Open is free through 10/18/2026 under a promo, and most models carry discounted cached-input rates. Batch Inference runs the same catalog asynchronously and scales to 30 billion tokens per model at lower batch rates — the cheapest path for offline labeling, synthetic data, and bulk generation. When per-token pricing stops making sense, you move up the stack: Provisioned Throughput buys committed token capacity with a 99% uptime SLA and drop-in API compatibility, Dedicated Model Inference puts models on hardware you control, Dedicated Container Inference targets video, audio, and image workloads, and GPU Clusters give self-serve access to GB300, GB200, B200, H200, and H100 — including on-demand B200s added in August 2026 and a dedicated Y Combinator cluster. Model Shaping covers fine-tuning and custom training with reinforcement learning, plus Evaluations to check whether the tuned model actually improved. Sandbox spins up secure code environments for agents, and Managed Storage adds object storage and parallel filesystems with zero egress fees. Together Link lets you run open models inside an agent harness you already use. The comparison that matters: against AWS Bedrock or Azure AI, Together leans on open-weight coverage and kernel-level research (FlashAttention, ATLAS, ThunderKittens, Together Kernel Collection) rather than managed-service breadth. Against Fireworks or OpenRouter you give up some routing convenience in exchange for direct
Behind the Verdict
The honest reason to pick Together AI over a pure router is the ladder. You start on serverless, benchmark against your own evals, then move to Provisioned Throughput with its 99% SLA without rewriting API calls. That drop-in compatibility is the part teams underrate — it turns a cost decision into a config change. Where it earns its keep: high-volume async work. Batch Inference scaling to 30 billion tokens per model at discounted rates is the cheapest way to run synthetic data generation, bulk classification, or offline evals across a large corpus. Media pipelines get Dedicated Container Inference for video and audio models that most inference APIs won't host at all. In practice, the breadth cuts both ways. Serverless covers text, image, video, audio, code, and voice, but the model list churns fast — DeepSeek V4 Flash 0731, GLM-5.3-Flash, MiniMax M3, Muse Spark Open all landed recently, and cached-input pricing varies per model. If your cost model assumes one flat rate, you'll be surprised. Check the per-model cached rate before you commit to a volume forecast. We'd reach for this when a team is already comfortable writing API code and wants open weights in production without owning GPUs. The Y Combinator cluster and on-demand B200s make it a reasonable home for startups that expect to scale compute quickly. Where it bites: no visual builder, no turnkey app. Non-technical buyers will bounce off the docs-and-cookbooks approach. Teams standardized on closed frontier models won't find a reason to migrate unless open weights matter to them for cost, control, or licensing. Closest alternative is Fireworks if you want similar serverless breadth with more routing polish; OpenRouter if you only need a switchboard across many providers. Together's differentiator is that
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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.
Prototype a chat feature against three open models using the serverless chat endpoint, then swap the winner into production once volume justifies committed capacity.
Outcome: Ships on one API key and one bill, with a clear cost-per-token number to take to the next board meeting.
Submit a multi-hundred-million-token extraction job through Batch Inference instead of hammering the real-time endpoint.
Outcome: Processes asynchronous work at batch rates, up to the 30-billion-token-per-model ceiling, without provisioning GPUs.
Deploy video and image models on Dedicated Container Inference with GPU infrastructure purpose-built for generative media.
Outcome: Runs generative media workloads on dedicated hardware with performance acceleration from Together Research.
Use Cases
- Serving open-source LLMs in production chat apps via a single API
- Running batch inference on millions of tokens asynchronously
- Fine-tuning DeepSeek, Llama, or Qwen models on your own data
- Building production voice agents with open models
- Benchmarking several models through one endpoint before committing
- Generating images and video on dedicated GPU containers
- Pre-training or RL post-training a model on your own cluster
- Scaling coding agents that need high sustained throughput
Models Under the Hood
as of 2026-10-16
Limitations
- Together AI is a developer cloud: open-source models are consumed through serverless or batch inference APIs, provisioned throughput, dedicated model/container inference, or harnesses like Together Link rather than an end-user application.
- Serverless and batch inference are priced per token, while Provisioned Throughput, Dedicated Model Inference, Dedicated Container Inference, and GPU Clusters are arranged through sales, so dedicated capacity cannot be costed from the site alone.
- Compute options are tiered across GB300, GB200, B200, H200, and H100 clusters, with the B200 offering an on-demand option.
- Model availability and pricing on the serverless platform change frequently and cached-input rates apply only on cache hits.
as of 2026-10-09
Verification history
We have re-verified Together AI 20 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
Showing the 6 most recent of 20 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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)
From $0.00/mo
Batch Inference (per 1M tokens)
Batch rates
Provisioned Throughput
Contact sales
Ideal for
Production teams whose sustained token volume makes shared serverless capacity unpredictable or expensive.
What this tier adds
Adds committed capacity with reserved throughput, a 99% uptime SLA, and drop-in API compatibility from serverless; quoted through sales.
Dedicated Model Inference
Contact sales
Ideal for
Teams that have outgrown shared serverless capacity and need speed, control, and predictable economics on specific models.
What this tier adds
Runs models on dedicated infrastructure instead of a shared multi-tenant pool; quoted through sales.
Dedicated Container Inference
Contact sales
Ideal for
Media and generative pipelines deploying video, audio, and image models on purpose-built GPU infrastructure.
What this tier adds
Specializes the dedicated tier for generative media rather than text models, with performance acceleration from Together Research.
GPU Clusters
Contact sales
Ideal for
Teams training or serving at a scale where renting bare GPU capacity beats per-token pricing.
What this tier adds
Adds self-serve instant clusters scaling to thousands of GPUs across GB300, GB200, B200, H200, and H100, with on-demand B200s available; quoted through sales.
Fine-Tuning & Custom Training
Contact sales
Ideal for
ML engineers who have proprietary data and need a model shaped to it rather than a general-purpose endpoint.
What this tier adds
Adds fine-tuning and custom training including reinforcement learning, with Evaluations to measure whether quality actually improved.
Managed Storage & Sandbox
Contact sales
Ideal for
Teams that want model weights, datasets, and agent code sitting next to the inference endpoint instead of in a separate cloud bucket.
What this tier adds
Adds object storage and parallel filesystems with zero egress fees, plus fast, secure code sandboxes for AI apps and agents.
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 fits engineering teams spending roughly $50–$5,000/mo on tokens: serverless entry points like Ternary Bonsai 27B at $0.00 and GLM-5.3-Flash at $0.15 per 1M input tokens sit well under Fireworks or Azure OpenAI list rates for comparable open-weight models. Above that, Provisioned Throughput, Dedicated Model Inference, and GPU Clusters are sales-quoted, so mid-market teams comparing against AWS Bedrock's published endpoints should expect a scoping conversation rather than a self-serve
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.
Serverless inference: an API key and a first call in minutes, since there's no infrastructure to manage or long-term commitment. Batch Inference: add an async job pattern to an existing integration, typically an afternoon. Production voice agents: longer, since you're wiring real-time audio plus a model endpoint. Fine-tuning and Evaluations: days rather than hours once your dataset is staged.
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 Fireworks AI: port model calls to Together's OpenAI-compatible serverless endpoints, then keep routing decisions in your own code instead of a hosted router.
- →From OpenRouter: replace the routing layer with a direct serverless endpoint for the model you actually use, and gain access to GPU clusters and training on the same account.
- →From AWS Bedrock or Azure AI: move open-weight workloads across model by model, keeping Bedrock for closed-model calls you still need.
- →From a self-hosted vLLM or TGI deployment: point clients at the serverless endpoint first to benchmark cost per token before deciding whether Provisioned Throughput is cheaper.
- →From OpenAI: re-point SDK calls at an open model such as gpt-oss-120B or DeepSeek V4 Pro 0813 and re-test prompts, since outputs do not transfer cleanly.
- ↗To OpenRouter: if you want multi-vendor routing and price arbitrage across providers rather than direct cluster access.
- ↗To Fireworks AI: if you prefer a routing-first platform with published dedicated rates over sales-quoted capacity.
- ↗To AWS Bedrock or Azure AI: if procurement already runs through a cloud marketplace and managed-service breadth matters more than open-weight depth.
- ↗To a self-hosted cluster: if your token volume is high enough that owning GPUs beats renting them, exporting weights and fine-tunes from Together.
- ↗To a closed-model API such as GPT-5.6 Sol: if quality on a specific task outweighs open-weight cost economics.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Together AI”, and we withheld 6: 6 could not be judged, because “Together AI” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Together AI.
Official links
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