Together Compute
Together Compute is an AI-native cloud for running open-source models — serverless inference, batch jobs, model shaping, and GPU clusters in one platform.
If your team ships open-weight models and cares about cost per token and GPU access more than a polished console, Together Compute is hard to beat — published serverless rates like GLM-5.3-Flash at $0.15 per 1M input tokens and DeepSeek V4 Flash 0731 at $0.14 are genuinely aggressive, and on-demand B200s remove the reservation headache. It is API-first, so budget real engineering time, and the dedicated tiers (Provisioned Throughput, GPU Clusters, AI Factory) are quote-based. If you want a no-code app builder or AWS-grade managed services end to end, look at a hyperscaler's model garden instead.
Verified 23h ago · liveness 82/100 · cite: rightaichoice.com/tools/together-compute
- Development teams serving open-weight models who need published per-token pricing and high throughput
- Companies running large asynchronous jobs — synthetic data, evals, document processing — via Batch Inference
- Startups that need on-demand B200s or H100 clusters without a long-term GPU reservation
- Teams building voice agents or generative media pipelines on dedicated container inference
- Non-technical users who want a no-code AI app builder
- Teams that need one vendor to cover IAM, billing, and managed services end to end
- Projects with low inference volume where per-token savings never offset integration work
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Skip Together Compute if you need a no-code interface or a single vendor to own identity, billing, and managed services end to end rather than an API-first inference platform you integrate yourself.
Batch pricing only applies to asynchronous jobs — anything you send through the standard serverless endpoint bills at the full per-token rate, so mis-routed bulk work quietly costs more.
Serverless is pay-per-token with no monthly minimum, which suits seed-stage teams and anyone testing open models before committing. Published rates like GLM-5.3-Flash at $0.15 and DeepSeek V4 Flash 0731 at $0.14 per 1M input tokens undercut the closed-model APIs you may be leaving. Provisioned Throughput and GPU Clusters are quote-based and land closer to hyperscaler reserved-capacity pricing — that tier is for funded teams with steady load, not for experimentation budgets.
In short
Together Compute — Together Compute is an AI-native cloud for running open-source models — serverless inference, batch jobs, model shaping, and GPU clusters in one platform. Best for Development teams serving open-weight models who need published per-token pricing and high throughput, Companies running large asynchronous jobs — synthetic data, evals, document processing — via Batch Inference, Startups that need on-demand B200s or H100 clusters without a long-term GPU reservation. Free to use.
What's new in Together Compute
Checked todayAcross the latest 3 updates: 1 feature update, 1 launch and 1 news mention.
On-demand B200s now available on Together GPU Clusters
B200 GPUs can now be provisioned on-demand in Together GPU Clusters, giving access to the newest hardware without a reservation.
Now serving MiniMax-M3 for efficient inference
MiniMax-M3 was added to the Together AI model library for efficient inference, listed on the pricing page at $0.30 per 1M input tokens and $1.20 output.
DeepSeek V4 Pro 0813 vs. GPT-5.6 Sol on DeepSWE
A published benchmark comparison of DeepSeek V4 Pro 0813 against GPT-5.6 Sol on the DeepSWE coding benchmark.
What people actually say about Together Compute — is it worth it?
We scanned public community sources for Together Compute on Aug 21, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Together Compute? 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: September 2026
How we score →Key Features
- Serverless inference across chat, vision, image, audio, video, transcription, embeddings, rerank, and moderation
- OpenAI-compatible API for drop-in model swaps
- Batch Inference scaling to 30 billion tokens per model
- Batch API pricing at a discount to standard serverless rates
- Cached input pricing as low as $0.006 per 1M tokens
- Provisioned Throughput with reserved token capacity and a 99% uptime SLA
- Dedicated Model Inference deployed on custom hardware
- Dedicated Container Inference for video, audio, and image models
- GPU Clusters on GB300, GB200, B200, H200, and H100
- On-demand B200 GPU provisioning without a reservation
- Fine-tuning open-source models on your own data
- Custom training from reinforcement learning to full control
- Evaluations to measure model quality before shipping
- Sandbox development environments via the CodeSandbox SDK
- Managed Storage for weights and data with zero egress fees
About Together Compute
Together Compute is an API-first cloud for teams that want to serve open-weight models in production without building the serving stack themselves. Serverless inference covers chat, vision, image, audio, video, transcription, embeddings, rerank, and moderation through an OpenAI-compatible API, with published per-1M-token rates: MiniMax M3 at $0.30 input / $1.20 output, GLM-5.3-Flash at $0.15 / $0.50, DeepSeek V4 Pro 0813 at $1.32 / $3.96, Gemma 4 31B at $0.39 / $0.97, and gpt-oss-120B at $0.15 / $0.60. Cached input drops as low as $0.006 per 1M tokens on DeepSeek V4.1 Flash. Batch Inference handles asynchronous jobs up to 30 billion tokens per model, and Provisioned Throughput adds reserved token capacity behind a 99% uptime SLA. When serverless stops fitting, the platform scales into dedicated territory: Dedicated Model Inference on custom hardware, Dedicated Container Inference tuned for generative media like video, audio, and image, and GPU Clusters spanning GB300, GB200, B200, H200, and H100 — with on-demand B200s now provisionable without a reservation. Model shaping rounds it out: fine-tuning on your own data, custom training from reinforcement learning through full control, and evaluations before you ship. The research arm is not decoration — FlashAttention, the Together Kernel Collection, ThunderKittens, and ATLAS sit underneath the serving layer, and Together claims 2x faster inference, 60% lower cost, and 90% faster pre-training. Sandbox environments via the CodeSandbox SDK, Voice Agents tooling, and Managed Storage with zero egress fees cover the agent and voice side. This is infrastructure for people who write code: versus AWS SageMaker or a hyperscaler model garden you trade hand-holding for a wider open-model roster, published token rates, and no long-term commitment on serverless.
Behind the Verdict
Together Compute sits in the same lane as Fireworks AI, Groq, and the hyperscaler model gardens, and the differentiator is breadth plus published economics. On pricing page evidence alone, the serverless roster is unusually wide: chat, vision, image, audio, video, transcription, embeddings, rerank, and moderation all live behind one OpenAI-compatible endpoint, so a model swap is a string change rather than a re-integration. Rates are on the page — MiniMax M3 at $0.30 input / $1.20 output per 1M tokens, Qwen3.8 Flash at $0.09 / $0.28, GLM-5.3-Flash at $0.15 / $0.50, Ternary Bonsai 27B listed at $0.00 — and cached-input discounts run to 90% off on some models (DeepSeek V4.1 Flash at $0.006 cached). Batch Inference is the underrated piece: up to 30 billion tokens per model for async work like synthetic data generation, evals, and document processing, and the Batch API column on the pricing page shows real savings against standard rates. Strengths: the open-weight library turns over fast and Together keeps pace (MiniMax M3, Kimi K3, GLM-5.3, DeepSeek V4 Pro 0813, Gemma 4 31B all listed), the research stack (FlashAttention, ThunderKittens, Together Kernel Collection, ATLAS) is a real engineering moat rather than a marketing slide, and GPU Clusters cover GB300 through H100 with on-demand B200s now available — notable because B200 capacity usually means a reservation. Managed Storage carries zero egress fees, which matters more than it sounds once you are shuffling weights and datasets. Weaknesses: this is a build-it-yourself platform. There is no no-code builder, no end-to-end IAM/billing/managed-services story the way a hyperscaler offers, and the dedicated tiers route through sales rather than self-serve checkout. The open-model library also churns, so you will pin versions and re-run evaluations — the platform ships Evaluations for exactly that, but it is still your job. Where it fits: teams with an ML engineer or two who are already comfortable with Python, Docker, and Kubernetes, and who measure value in cost per token and GPU-hours. Where it does not: solo builders who want a chat UI, or enterprises that need one vendor to own the whole stack including identity and compliance workflows.
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Real-world workflow fit
Concrete scenarios for the personas Together Compute actually fits — and what changes day-one when you adopt it.
They point their OpenAI-compatible client at Together Compute, swap the model string to MiniMax M3 or GLM-5.3-Flash, and route bulk backfill jobs to the Batch Inference API.
Outcome: Published per-token rates let them forecast spend before month one, and cached input on repeated system prompts cuts the effective input cost substantially.
They queue millions of transcription and extraction jobs through Batch Inference, scaling to 30 billion tokens per model, then store inputs and outputs in Managed Storage.
Outcome: Async batch pricing reduces cost against the standard endpoint, and zero egress fees keep the storage side of the pipeline cheap.
They fine-tune on domain data via Model Shaping, run Evaluations to pick a checkpoint, then provision GPU Clusters or Dedicated Model Inference for serving.
Outcome: One platform covers training through serving, so they avoid stitching a separate inference vendor onto their GPU provider.
Use Cases
- Serve a high-throughput chat API on MiniMax M3 or GLM-5.3-Flash with pay-per-token billing and no monthly minimum.
- Run batch transcription on millions of audio hours through the Batch Inference API at discounted async rates.
- Fine-tune Llama 4 Maverick or Gemma 4 31B on domain data using Model Shaping, then check quality with Evaluations.
- Train a custom vision-language model from scratch on GPU clusters with GB200 or GB300 accelerators.
- Build a production voice agent using Voice Agents tooling on top of dedicated inference.
- Provision an on-demand B200 for a short training run without signing a GPU reservation.
- Scale a coding agent on Kimi K2.7 Code or gpt-oss-120B behind a single OpenAI-compatible endpoint.
Models Under the Hood
as of 2026-09-21
Limitations
- Together Compute is developer-focused infrastructure: meaningful use requires engineering time to configure inference, training, and GPU resources, and there is no no-code path.
- The dedicated tiers — Provisioned Throughput, Dedicated Model Inference, Dedicated Container Inference, GPU Clusters, and AI Factory — are quote-based rather than self-serve, so you will talk to sales before you can scale beyond serverless.
- The open-model library turns over quickly (MiniMax M3, Kimi K3, GLM-5.3, DeepSeek V4 Pro 0813 all current), which means version pinning and re-running Evaluations whenever you move models.
- Pricing on the page covers serverless token rates; dedicated and reserved capacity are negotiated.
- You also manage your own surrounding stack — identity, orchestration, and monitoring are yours to wire up, with no end-to-end managed-services layer the way a hyperscaler provides.
as of 2026-09-29
Verification history
We have re-verified Together Compute 19 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-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 19 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 Compute 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
Pay per token
Ideal for
Seed-stage teams and individual engineers testing open models or running variable production traffic without a commitment.
What this tier adds
Starting tier — pay per token with no monthly minimum and published per-1M-token rates across chat, vision, image, audio, video, transcription, embeddings, rerank, and moderation.
Provisioned Throughput
Contact sales
Ideal for
Production teams with steady, predictable token volume that need guaranteed capacity rather than best-effort serverless.
What this tier adds
Adds committed token-based capacity, reserved throughput, and a 99% uptime SLA on top of drop-in API compatibility.
Dedicated Model Inference
Contact sales
Ideal for
Teams optimizing for latency and unit economics who are ready to move their busiest models off shared serverless capacity.
What this tier adds
Moves models onto dedicated hardware with control over the serving environment.
Dedicated Container Inference
Contact sales
Ideal for
Media and voice teams deploying video, audio, or image generation models that need tuned GPU infrastructure.
What this tier adds
GPU infrastructure purpose-built for generative media workloads, with acceleration from Together Research.
GPU Clusters
Contact sales
Ideal for
AI labs and startups that need GB300, GB200, B200, H200, or H100 capacity without a long-term reservation.
What this tier adds
Scales from self-serve instant clusters to thousands of GPUs, including on-demand B200s, optimized with the Together Kernel Collection.
AI Factory
Contact sales
Ideal for
Large organizations running frontier-scale training and pre-training deployments that need custom infrastructure.
What this tier adds
Custom infrastructure at frontier scale, above the standard GPU cluster offering.
Where the pricing makes sense
The company stage and team size where Together Compute's pricing actually pencils out — and where peers do it cheaper.
Serverless is pay-per-token with no monthly minimum, which suits seed-stage teams and anyone testing open models before committing. Published rates like GLM-5.3-Flash at $0.15 and DeepSeek V4 Flash 0731 at $0.14 per 1M input tokens undercut the closed-model APIs you may be leaving. Provisioned Throughput and GPU Clusters are quote-based and land closer to hyperscaler reserved-capacity pricing — that tier is for funded teams with steady load, not for experimentation budgets.
Setup time & first value
How long it actually takes to get something useful out of Together Compute — broken out by persona, not the marketing-page minute.
A developer with an existing OpenAI-compatible client can get first tokens in under 30 minutes — swap the base URL and key. A data pipeline moving to Batch Inference typically takes a day or two including storage wiring. Teams standing up GPU Clusters, Provisioned Throughput, or a custom training run should plan for a sales conversation plus a week or more of environment work.
Switching to or from Together Compute
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: change the base URL and model string — the API is OpenAI-compatible, so most client code needs no rewrite.
- →From a hyperscaler model garden: move serverless traffic first to compare per-token cost, then evaluate dedicated tiers once volume justifies it.
- →From self-hosted vLLM: point clients at Together serverless, keeping your own GPUs for training or workloads that need private hardware.
- →From Hugging Face Inference Endpoints: re-point requests at the OpenAI-compatible endpoint and move weights and datasets into Managed Storage.
- ↗To a self-hosted vLLM or TGI stack: export your fine-tuned weights from Model Shaping and stand up serving on your own GPUs.
- ↗To a hyperscaler managed service: re-implement the API layer against their SDK, accepting a narrower open-model roster in exchange for managed identity and billing.
- ↗To another inference provider: because the API is OpenAI-compatible, switching is largely a base-URL and model-name change, but re-run Evaluations on the new host.
Integrations
Resources & Guides
- Resourcedocs.together.ai
Overview - Together AI docs
Run, train, and serve open-source AI models on Together AI.
- Resourcetogether.ai
Cookbooks | Together AI
Helpful link from together.ai
- Resourcetogether.ai
Demos | Together AI
Helpful link from together.ai
- Resourcetogether.ai
Support | Together AI
Helpful link from together.ai
- Resourcetogether.ai
Blog | Together AI
Helpful link from together.ai
- Resourcetogether.ai
Events | Together AI
Helpful link from together.ai
Tutorials & Learning
YouTube returned 6 videos for “Together Compute”, and we withheld 5: 5 did not mention Together Compute. Showing the 1 we can prove is about Together Compute.
Official links
Tools that pair well with Together Compute
Common stack mates teams adopt alongside Together Compute, with the specific reason each pairing earns its keep.
DeepInfra
DeepInfra is a serverless inference cloud serving 100+ open models — DeepSeek-V4-Flash-0731 at $0.06 per 1M input tokens — through one OpenAI-compatible API
SambaNova Cloud
Custom RDU hardware for fast inference on open models, sold as racks and cloud capacity.
LFM
LFM2.5 is Liquid AI's open-weight on-device AI family, running native text, vision, and audio models locally on CPU, GPU, or NPU.
Alternatives to Together Compute
View all
DeepInfra
DeepInfra is a serverless inference cloud serving 100+ open models — DeepSeek-V4-Flash-0731 at $0.06 per 1M input tokens — through one OpenAI-compatible API
SambaNova Cloud
Custom RDU hardware for fast inference on open models, sold as racks and cloud capacity.
Frequently Asked Questions
Best-of guides
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