Together AI
Serverless inference and GPU cloud for open-source LLMs at scale
Together AI is the pick for teams that need open-source models at scale without the price tag or latency of bigger clouds. It's fast, cost-effective, and deeply technical. Dedicated GPU clusters and batch inference handle massive workloads, and the YC partnership eases early-stage infrastructure costs. Skip it if you're non-technical or want a configurable managed UI — this is a developer-first platform.
Verified 4d ago · liveness 88/100 · cite: rightaichoice.com/tools/together-ai
- Production coding agents and high-throughput applications on open-source LLMs like DeepSeek V4 Pro
- Batch inference jobs processing massive token volumes (up to 30B per model)
- Fine-tuning open models for accuracy and control in enterprise workflows
- Generative media workloads needing dedicated GPU infrastructure for video, audio, image
- Teams that require a no-code visual interface for AI deployment
- Organizations committed exclusively to closed-source models (e.g., GPT-5.6)
- Small-scale experiments with minimal token usage where serverless pricing may not be cost-effective
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Together AI if you need a no-code visual builder or a turnkey managed UI, if you're committed exclusively to closed-source models, or if you're a non-technical user without API skills.
Going past the $5 free credit requires paying per token, and high-traffic usage can add up quickly.
Together AI's serverless pricing is competitive for high-throughput open-model inference, with models like DeepSeek V4 Pro at $1.32/M input and cached discounts. For teams with steady usage, Provisioned Throughput offers a 99% SLA. Compared to closed-model clouds like OpenAI, you avoid per-token surcharges on open models, but for low-volume users, simpler flat-rate plans elsewhere might be cheaper.
In short
Together AI — Serverless inference and GPU cloud for open-source LLMs at scale. Best for Production coding agents and high-throughput applications on open-source LLMs like DeepSeek V4 Pro, Batch inference jobs processing massive token volumes (up to 30B per model), Fine-tuning open models for accuracy and control in enterprise workflows. Free to start; paid plans from $1/mo.
What's new in Together AI
Checked 4 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.
- +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: September 2026
How we score →Key Features
- Serverless inference for 100+ open-source models
- Batch inference scaling to 30 billion tokens per model
- Provisioned Throughput with 99% uptime SLA
- Dedicated Model Inference on custom GPU hardware
- Dedicated Container Inference for video/audio/image models
- On-demand NVIDIA B200 GPUs on clusters
- GPU clusters with GB300, GB200, H200, H100
- AI Factory custom infrastructure
- Fine-tuning with FlashAttention and ATLAS kernels
- Custom training with reinforcement learning support
- Production Voice Agents via API
- Model evaluations for quality measurement
- Managed Storage with zero egress fees
- Sandbox environments via CodeSandbox SDK
- REST API, Python SDK, Node.js SDK, WebSocket support
About Together AI
Together AI is a full-stack AI cloud built for developers and enterprises that run open-source models in production. It pairs high-throughput serverless inference with GPU clusters, fine-tuning, and research-optimized kernels, so you can move from experimentation to scale without managing your own infrastructure. The platform is designed for API-first teams—there's no no-code visual builder, but you get REST API, Python SDK, Node.js SDK, and WebSocket support to integrate models into your stack. For inference, Together AI offers multiple options: serverless endpoints with per-token pricing across 100+ models—including DeepSeek V4 Pro, MiniMax M3, Gemma 4 31B, GLM-5.2, and Qwen3.7-Max—plus batch inference that scales to 30 billion tokens per model, provisioned throughput with a 99% uptime SLA, dedicated model inference on custom hardware, and dedicated container inference for video, audio, and image generation workloads. Cached input discounts are available on many serverless models, cutting costs for high-hit-rate traffic. On the compute side, GPU clusters give you self-serve instant access to thousands of GPUs, with hardware like GB300, GB200, B200 (now available on-demand), H200, and H100. Together Kernel Collection claims 2x faster inference, 60% lower cost, and 90% faster pre-training on its optimized stack. For tailoring models, you can fine-tune or do custom training with research kernels like FlashAttention and ATLAS. Managed storage provides object storage and parallel filesystems with zero egress fees, and sandbox environments via CodeSandbox let you build and test AI apps. Recent additions include MiniMax-M3 for efficient inference and an on-demand B200 option on GPU clusters. Together AI also partnered with Y Combinator to power a dedicated GPU cluster for YC startups, lowering infrastructure costs for early-stage teams. The platform is ISO 27001:2022 certified and integrates with Hugging Face, LangChain, Weights & Biases, and LlamaIndex. If you want open-source models at scale with minimal latency and cost, Together AI is a strong contender.
Behind the Verdict
Together AI stands out in the crowded AI cloud space for its research-backed performance claims and open-source focus. If you're a developer or an enterprise that wants to run open models like DeepSeek V4 Pro or Llama 4 without managing your own GPU infrastructure, this is a solid fit. The serverless inference is priced per token with cached input discounts, which can cut costs significantly for high-hit-rate traffic. Batch inference scaling to 30 billion tokens per model is a differentiator for large-scale data processing jobs. GPU clusters with on-demand B200s give you flexibility for compute-heavy workloads, and the Together Kernel Collection touts 2x faster inference and 60% lower cost. The Y Combinator partnership provides a dedicated cluster for YC startups, a nice touch for early-stage teams. However, the platform is developer-only—no no-code UI, so if you're not comfortable with APIs and SDKs, it's not for you. The free tier is just $5 in credits, which is thin for real experimentation. Dedicated and provisioned tiers require sales contact, which adds friction. Also, the model library is open-source only; if you're wedded to closed models like GPT-5.6, you'll need to look elsewhere. For a technical audience, the trade-offs are acceptable.
Researching Together AI? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Together AI actually fits — and what changes day-one when you adopt it.
You need to deploy an open-source LLM with low latency and scale. You sign up for Together AI, pick DeepSeek V4 Pro from the model library, and use the REST API to integrate it into your app. You start with serverless inference, monitor costs, then move to Provisioned Throughput once usage stabilizes.
Outcome: You get a production-grade chat endpoint in minutes, with per-token pricing and the ability to scale without managing GPUs.
You have a large dataset of millions of documents to process. You use Together AI's Batch Inference API to run the job asynchronously, scaling to 30 billion tokens per model. You use the Python SDK to submit the job and monitor progress.
Outcome: The batch job completes efficiently, saving time and cost compared to running on your own infrastructure.
You have a custom dataset and want to fine-tune a Llama or Mistral model. You use Together AI's Fine-Tuning service with FlashAttention kernels, then deploy the fine-tuned model as a serverless endpoint.
Outcome: You get a model tailored to your data, with optimized inference performance, without managing GPU clusters.
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 models 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 throughput requirements
Models Under the Hood
as of 2026-08-31
Limitations
- Together AI is a developer-focused platform, requiring API integration and coding skills.
- Pricing is per token for serverless inference, and dedicated cluster pricing requires sales contact.
- Specific usage limits are not detailed on the scraped pages.
as of 2026-08-29
Verification history
We have re-verified Together AI 18 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-checked, vendor evidence unchanged
- — 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 18 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.
Free
$0
Ideal for
Developers exploring open-source models with minimal usage, getting started with the API and testing a few calls.
What this tier adds
Starting tier with $5 free credit and access to select serverless models; no commitment.
Serverless Inference
Per 1M tokens
Ideal for
Teams running production workloads on open models with variable traffic, paying per token.
What this tier adds
Adds pay-as-you-go per-token pricing, cached input discounts, and access to 100+ models; no fixed cost.
Provisioned Throughput
Contact sales
Ideal for
Teams with steady, high-volume inference needs requiring reserved capacity and a 99% uptime SLA.
What this tier adds
Committed inference capacity with token-based pricing and reserved throughput; requires sales contact, unlike serverless.
Dedicated Inference
Contact sales
Ideal for
Enterprises that need dedicated hardware for specific models, with custom performance tuning.
What this tier adds
Dedicated hardware and custom tuning; higher cost than serverless but with full control and predictable performance.
GPU Clusters
Contact sales
Ideal for
Teams running fine-tuning, custom training, or batch jobs that need flexible GPU compute at scale.
What this tier adds
Adds self-serve instant GPU clusters with various hardware options (GB300, B200, H100) and Together Kernel Collection optimization.
Fine-Tuning & Custom Training
Contact sales
Ideal for
ML teams that need to tailor open-source models to specific domains or tasks, with custom training support.
What this tier adds
Adds fine-tuning and custom training capabilities using FlashAttention and ATLAS kernels, plus reinforcement learning support.
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 pricing is competitive for high-throughput open-model inference, with models like DeepSeek V4 Pro at $1.32/M input and cached discounts. For teams with steady usage, Provisioned Throughput offers a 99% SLA. Compared to closed-model clouds like OpenAI, you avoid per-token surcharges on open models, but for low-volume users, simpler flat-rate plans elsewhere might be cheaper.
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.
Sign-up and API key issuance takes minutes. First serverless inference call can be made within 10-15 minutes after reading the docs. Batch jobs require a little more setup but can be running within an hour. Fine-tuning may take a few hours for training, but the setup is straightforward.
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: If you're using GPT-4 and want open models, switch your API calls to Together AI's serverless endpoints with a similar OpenAI-compatible interface.
- →From AWS SageMaker: For open-model inference, export your fine-tuned model weights and deploy them on Together AI's Dedicated Inference or GPU clusters.
- →From Hugging Face Inference Endpoints: Recreate your endpoints on Together AI for potentially lower cost and higher throughput.
- ↗To AWS SageMaker: Export your fine-tuned model artifacts and deploy on SageMaker with custom inference containers.
- ↗To Azure ML: Migrate your fine-tuned models to Azure ML for managed inference with your existing Azure ecosystem.
- ↗To a self-hosted solution: Download model weights and run on your own GPU cluster if you prefer full control.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Together AI
Common stack mates teams adopt alongside Together AI, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Fireworks Ai vs Together Ai
If you need the absolute lowest latency and earliest access to frontier open-weight models for real-time coding assistants, Fireworks AI is the clear winner — especially with its newer models like GLM 5.2 and MiniMax M3. However, if you want a broader model library, a freemium entry point, and enterprise-ready certifications without vendor lock-in, Together AI's zero-egress storage and ISO 27001 compliance make it a safer bet for compliance-heavy teams.
Baseten vs Together Ai
Choose Baseten if you need ultra-low latency inference (sub-300ms) for custom models or real-time voice agents, and you value multi-cloud high availability and model monetization. Choose Together AI if you rely on a broad library of open-source models, need batch inference at scale, or want a full-stack cloud for fine-tuning and pre-training. Both offer strong performance, but their sweet spots differ by workload and model ownership.
Groq vs Together Ai
If you need real-time responsiveness under 200ms — chatbots, voice assistants, agentic systems — Groq's LPU is the clear winner, with day-zero model access and a dead-simple switch from OpenAI. But if your workloads are batch-heavy, require fine-tuning, or need massive async token throughput (up to 30B tokens), Together AI's full-stack cloud — from sandbox to AI Factory — offers more flexibility and training depth. Choose Groq for speed, Together AI for scale and customization.
Modal vs Together Ai
For teams that need a curated library of 100+ open-source models with high-performance serverless inference and fine-tuning via a managed API, Together AI is the stronger choice. However, if you require sub-second cold starts, instant autoscaling to thousands of GPUs, and full control over your containerized stack (with Python SDK primitives), Modal's infrastructure is more flexible for bursty, unpredictable workloads and multi-node training. Your pick depends on whether you value model selection and out-of-the-box APIs (Together) versus extreme scaling and cold-start performance (Modal).
Alternatives to Together AI
View all
Together Compute
AI-native cloud for high-throughput open-source model inference and GPU compute at scale.
Popular in GPU Cloud & Model Inference
Frequently Asked Questions
Categories
Topics
Used Together AI? Help shape our editorial sentiment research.