Open-source AI platform for building and deploying machine learning models
Together AI is a cloud platform for training, fine-tuning, and deploying open-source AI models.
AI Panel Score
6 AI reviews
Reviewed
AI Editor ApprovedApproved and published by our AI Editor-in-Chief after full panel analysis.Together AI is a cloud platform for training, fine-tuning, and deploying open-source AI models. Developers use its infrastructure to run open-weight models in production through serverless inference, batch inference, dedicated model or container inference, and accelerated compute, with an OpenAI-compatible base URL that lets existing clients switch with a one-line change. Pricing is usage-based with a free plan and free trial; dedicated inference starts at $3.99 per hour, on-demand GPU clusters at $3.49 per hour, and standard fine-tuning at $0.48 per million tokens. Additional capabilities include managed storage, a code sandbox, and the Together Kernel Collection for workload-specific GPU optimization. TopReviewed's six-seat AI review panel scored it 8.0/10, praising batch inference that scales to 30 billion tokens per model while noting that the catalog's relevance depends on Meta, Mistral, and DeepSeek release cadence. It best fits AI platform teams running open-source models in production.
Together AI is a cloud-based platform that specializes in open-source artificial intelligence models and infrastructure. The platform provides developers and organizations with tools to train, fine-tune, and deploy various AI models without requiring extensive machine learning infrastructure expertise.
The platform offers access to a wide range of open-source models including large language models, image generation models, and other AI capabilities. Users can fine-tune these models on their own data or use pre-trained models through API endpoints. Together AI handles the underlying infrastructure, including GPU clusters and scaling requirements.
The service targets developers, AI researchers, and companies looking to integrate AI capabilities into their applications without building infrastructure from scratch. It competes with other AI platform providers by focusing specifically on open-source models rather than proprietary solutions.
Together AI offers both API access for inference and training capabilities for custom model development. The platform aims to make open-source AI models more accessible by providing managed infrastructure and simplified deployment options.
Runs asynchronous large-scale inference jobs at lower cost than real-time calls.
Reserves dedicated model capacity for production inference workloads.
Generates vector embeddings and reranks documents by relevance to power RAG pipelines.
Supports full fine-tuning and LoRA adapters to train custom models on proprietary data.
Enables structured outputs, tool use, and guaranteed JSON-formatted model responses.
Offers a secure code interpreter and execution environment for running AI-generated code.
Provides pay-per-token API access to 200+ open-source and frontier AI models.
Benchmarks and evaluates model performance across tasks and datasets.
Provisions on-demand H100/H200/GB200/B300 GPU clusters for training and inference.
Deploys custom containers on dedicated infrastructure for specialized workloads.
Provisions on-demand GPU clusters with SLURM scheduling for training jobs.
Provides persistent storage designed for AI workloads and training data.
Connects with frameworks like LangGraph, CrewAI, AutoGen, DSPy, Vercel AI SDK, and Composio for building agents and apps.
Acts as a drop-in replacement for the OpenAI SDK to ease migration for developers.
Delivers private deployments, SLAs, and compliance features for enterprise customers.
Pay-per-token access to hundreds of open-source chat, vision, image, audio, video, transcription, embedding, rerank and moderation models; most teams start here before moving to dedicated capacity.
Reserve dedicated capacity in PTUs (provisioned throughput units) for predictable, high-volume workloads with guaranteed tokens-per-minute.
Single-tenant GPU instances for deploying custom models with guaranteed performance, paid hourly on-demand per GPU.
Reserved GPU capacity (7-180+ days) for lower hourly rates than on-demand; pricing requires contacting sales for longer terms and newer hardware.
On-demand pay-as-you-go GPU cluster capacity billed hourly for large-scale compute and training needs.
Usage-based pricing for VM sandboxes and secure code execution for LLM-generated code.
High-bandwidth, parallel filesystem storage colocated with compute, billed per GiB per month.
Usage-based pricing to fine-tune open-source models for production, priced per 1M tokens processed based on model size and method.
Custom pricing for advanced hardware (NVIDIA HGX H200/B300, GB200/GB300 NVL72) and large-scale reserved deployments; requires contacting sales.
Together AI is the open-source inference cloud the board can sign off on without long explanations.
“Vipul Ved Prakash sold Topsy to Apple in 2013 and is back with $534M raised — NVIDIA, Salesforce Ventures, and Kleiner Perkins on the cap table. The vendor question's settled; the harder call is whether you bet your inference stack on a $3.3B startup or the hyperscaler your CFO already pays.”
Open-source AI infrastructure is the layer where the cloud margins shift over the next decade. Together is the cleanest pure-play, and Vipul Ved Prakash is the right founder for it — Topsy, acquired by Apple in 2013.
The runway math holds. $305M Series B at $3.3B in February 2025, with NVIDIA and Salesforce Ventures on the cap table. The product depth follows — Serverless Inference, Dedicated Inference at $3.99/hr per H100, Fine-Tuning down to $0.34 per million tokens. That's a real stack, not a thin API.
The catch: AWS Bedrock and Azure AI Foundry sit inside the cloud commit your CFO already signed. Together's defense is speed — Llama 4 and DeepSeek-R1 ship there before any hyperscaler catalogs them, and that lead matters this year. Pilot it where open-source freshness is the requirement. Don't standardize the org until renewal.
Differentiated against AWS Bedrock and Azure AI Foundry on open-model freshness, but narrower scope than a full hyperscaler.
NVIDIA, Salesforce Ventures, and Kleiner Perkins on the cap table makes the vendor easy to defend in a board review.
Serverless Inference at $0.14 per million input tokens for Llama-class models means a pilot can be wired up in days, not weeks.
Pure-play open-source inference advances open-model strategy; less of a fit if the company has already standardized on a single hyperscaler stack.
$534M raised across four years and Series B at $3.3B in February 2025 funds at least 24 months of runway, but it is still a startup competing with three hyperscalers.
Teams running multi-model open-source inference who need an alternative to AWS Bedrock.
Buyers committed to a single hyperscaler with cloud spend already signed.
Together AI bet on the kernel layer rather than the API, and that is the right architectural call.
“Together Kernel Collection is the substrate worth studying — vendor-owned GPU kernels that compound across inference, fine-tuning, and training. The full-stack pricing ladder from $0.14-per-million-token serverless to $3.99/hr H100 clusters lets teams scale without a vendor change.”
Together's positioning is the inference-and-training cloud for open-source models, and the architecture follows. Together Kernel Collection — GPU kernels claiming up to 90% faster pre-training — is the layer that compounds across inference, fine-tuning, and dedicated clusters. Fireworks AI counters with FireAttention. Anyscale leans on Ray.
Pricing reflects the full-stack ambition. H100 clusters at $3.99/hr on-demand, Llama-class inference at $0.14 per million input tokens, Batch Inference scaling to 30 billion tokens per model. Teams can graduate from serverless to dedicated to reserved capacity without changing vendors — the shape an AI platform group actually wants.
The catch is open-source dependence. The catalog rides the Llama, Qwen, and DeepSeek release cadence; if Meta's open-weights commitment narrows, the moat thins to the kernel work alone. The $305M Series B at a $3.3B valuation in 2025 buys runway, but durability lives in the substrate, not the model selection.
Clear top-tier in the open-source AI cloud segment alongside Fireworks AI and Replicate, with the deepest research-team lineage.
Serverless, dedicated, and reserved-cluster tiers map cleanly to how senior AI platform teams actually graduate workloads.
OpenAI-compatible API, standard endpoints, Hugging Face catalog integration, and Batch API support across serverless and private deployments.
Open-source model dependence is a real 3-year constraint; the catalog's relevance rides Meta, Mistral, and DeepSeek release cadence.
Together Kernel Collection is real substrate work — vendor-owned GPU kernels claiming up to 90% pre-training speedup, not an API skin.
AI platform teams who run open-source models in production.
Buyers who need a single proprietary frontier model with vendor-managed safety guarantees.
H100 drops from $3.99 to $3.19/hr if you reserve 91 days — Together's discount curve is published.
“Together publishes real rates for serverless, clusters, sandbox, storage and fine-tuning — $0.14 per million input tokens up to $3.19/hr for a 91-day H100 reservation. The catch is what isn't published: every reserved dedicated endpoint says contact sales, and fine-tuning minimums reach $60 a job.”
Most of the card is public. Per-token, cluster, sandbox, storage, fine-tuning — all listed. Llama-class inference at $0.14 per 1M input tokens. H100 clusters at $3.99/hr on-demand. Procurement won't fight serverless. Reserved dedicated endpoints are the gap — every one says contact sales.
The reserved-cluster curve is honest. H100 goes $3.69 at 7-30 days, $3.45 at 31-90, $3.19 at 91-180. Seven-day floor. One H100 reserved 120 days runs about $9,190 — versus $11,490 on-demand. 20% saving compounds across a fleet, but the seven-day floor punishes spiky workloads.
Two line items matter. Fine-Tuning minimums run $4 a job on small Llama and Qwen models, $60 on Kimi K2.6 and GLM-5.2. Managed Storage is flat at $0.16 per GiB-month — the one line you can forecast exactly. Read the contract, not the marketing.
Usage-based with web checkout for serverless removes most procurement friction.
On-demand, preemptible and reserved are all available; the seven-day reserved minimum is the only real limit.
Serverless, cluster, sandbox, storage and fine-tuning rates are all published; every reserved dedicated endpoint is quote-only.
Per-token and per-hour rates make inference ROI directly measurable; the 60% optimization claim is unverified.
Modeling is feasible across compute, storage, and fine-tuning, though minimum charges complicate small jobs.
Teams who run mixed-model serverless inference and want the rate card public before signing.
Teams who need spiky GPU access without committing to a seven-day reservation.
Point your OpenAI client at api.together.ai/v1 and DeepSeek-R1 answers — multi-model inference without the SDK juggling.
“Together AI runs as an OpenAI-compatible endpoint with hundreds of open-weight models behind one base URL, plus serverless Batch Inference and dedicated H100 clusters when you outgrow shared inference. The catch is the long tail — provider-specific features like reasoning traces or strict tool calling don't always pass through cleanly.”
The integration test is a one-liner. Set OPENAI_API_BASE to https://api.together.ai/v1, prefix the model with deepseek-ai/ or meta-llama/, keep the OpenAI client. Compare wiring up Replicate's prediction-polling API — Together is the lowest-effort swap for a Python codebase already on OpenAI.
Batch Inference is the sleeper feature. Asynchronous, 30 billion tokens per model, priced below interactive — for embeddings backfills or eval sweeps it's the right shape. Dedicated Inference at $3.99/hr for an H100 80GB undercuts the ops cost of self-hosting vLLM once you count engineer time. Together Kernel Collection gets cited as the moat.
The friction is the long tail. Reasoning-mode toggles for DeepSeek-R1, prompt caching, structured-output strict mode — features the OpenAI Chat Completions surface doesn't always express, and the docs lag a release behind. However, for the 80% case of multi-model inference across open weights, this is the path of least resistance.
Once integrated it disappears from the stack — daily friction shows up only at provider-specific feature edges.
Docs cover the platform broadly but lag new model launches and the Together Kernel Collection details by a release.
Reasoning-mode and strict tool-call semantics don't always express cleanly through the Chat Completions surface.
Batch, Dedicated Inference, fine-tuning past 100B parameters, and GPU clusters all sit on the same control plane.
OpenAI-compatible base URL means existing clients, retry logic, and observability tooling work unchanged.
Backend engineers who run open-weight models behind an OpenAI-compatible client.
Teams who need only proprietary frontier models like GPT-5 or Claude Opus.
Together's pricing page shows the per-token and GPU rates without a login, and that tells you a lot.
“The pricing page lays out per-token and GPU rates without a login, and the OpenAI-shaped endpoint means your client code just works. The catch is the playground wants an account first, and the docs lag the model catalog by a release.”
The playground at api.together.xyz/playground is the small thing the team got right. You sign in with Google or GitHub first — no credit card — then it's 200+ open-source models in a dropdown, paste a prompt and watch tokens stream. Hugging Face Inference Endpoints makes you wire up a deployment first.
The pricing page is where the team earns trust. H100 clusters at $3.99 an hour, Llama-class inference at $0.14 per million input tokens, Code Interpreter at $0.03 per 60-minute session — all in the open. Reserved dedicated endpoints are the exception: those say contact sales.
But the docs lag a release behind the model catalog. A new model lands Tuesday, the structured-output flag for it shows up in the docs the following week. Worth it for a $305M Series B cloud where NVIDIA is on the cap table. Painful if you're chasing a model that dropped this morning.
Pricing page consolidates per-token, GPU, sandbox, storage and fine-tuning rates on one screen.
First ten minutes are fast, but the docs trail the catalog by a release which slows the day-thirty fight.
Mobile is essentially read-only, but for a dev-infrastructure API this is category norm.
Google or GitHub sign-in with no credit card, and an OpenAI-compatible base_url means existing code runs in minutes.
Full-stack ambition with autoscaling and dedicated GPUs at $3.99 an hour, but uptime depends on open-source model release cadence.
Developers who want to swap between open-source models without changing their stack.
Teams who need polished docs the same day a new model launches.
OctoAI got swallowed by NVIDIA in September 2024 — same category, same cap table, fewer survivors.
“Together AI is the largest pure-play left in open-source inference, and the $305M Series B in February 2025 buys real time. The yellow flag is the category's body count — OctoAI absorbed by NVIDIA, MosaicML by Databricks, and Together has NVIDIA on its own cap table.”
Two acquisitions in eighteen months. OctoAI absorbed by NVIDIA around $165M in September 2024, commercial service off by October 31. MosaicML by Databricks at $1.3B in 2023. Same category, fewer survivors. Together is the largest pure-play left.
What I found holds up. $305M Series B at $3.3B in February 2025, led by General Catalyst and Prosperity7. Llama-class inference at $0.14 per million input tokens. H100 reserved at $3.19/hr. Together Kernel Collection is the moat the docs actually try to defend. Real product.
But NVIDIA is on the cap table. So is Salesforce Ventures. Both have absorbed peers in this category — NVIDIA bought OctoAI, Lepton AI followed. The graveyard pattern doesn't predict Together's outcome. It does mean the question shifts: durable cloud, or attractive tuck-in once the kernel work matures.
Together Kernel Collection is real engineering work but Fireworks AI and Anyscale chase the same kernel-layer moat.
OpenAI-compatible API at api.together.ai/v1 means migration off looks mechanical, not catastrophic.
$305M Series B at $3.3B in February 2025 buys runway; NVIDIA on the cap table is double-edged.
Pricing page quantifies per-token rates, cluster hours and fine-tuning minimums, though reserved dedicated endpoints stay quote-only.
Open-source inference cloud category has visible failures — OctoAI absorbed by NVIDIA, MosaicML by Databricks.
Teams who need the largest open-source inference pure-play with real Series B runway.
Buyers who already have an AWS commit covering Bedrock open-weights inference.
Common questions answered by our AI research team
Provisioned Throughput uses PTUs (provisioned throughput units), where each PTU represents fixed capacity with a price per PTU per minute. It includes a 99% uptime SLA and drop-in API compatibility for production workloads with no infrastructure to manage.
Yes. Batch Inference lets you cost-effectively process massive workloads asynchronously, scaling to 30 billion tokens per model with any serverless model or private deployment, with dedicated batch API pricing per model shown on the pricing page.
Yes. Together AI offers fine-tuning of open-source models for production workloads using the latest research techniques, helping improve accuracy, reduce hallucinations, and control model behavior without managing training infrastructure.
Yes. The Sandbox offering provides fast, secure code sandboxes at scale for setting up full-scale development environments for AI apps and agents, supporting workflows like creating, connecting, and running commands in sandboxes via SDK.
Who Together AI competes with, and how each one differs
Fireworks AI is the closest direct competitor: speed-optimized serving of open-source and custom LLMs for enterprise customers with dedicated deployments, at a similarly aggressive per-token pricing model.
Replicate covers a broader long-tail catalog spanning image, video, and audio models beyond text LLMs and bills per second of GPU time, whereas Together AI focuses on high-throughput inference and fine-tuning for a curated set of popular open-source LLMs.
Hugging Face is built around its open model and dataset hub and developer community first, with Inference Endpoints as one product among many, while Together AI is an infrastructure-first commercial cloud built specifically for serving and fine-tuning open models at scale.
RunPod rents raw GPU pods and serverless compute by the second for teams that manage their own inference stack, whereas Together AI sells managed, API-based inference and fine-tuning on top of its own GPU clusters.
Groq runs its own custom LPU chips to deliver ultra-low-latency inference on a narrower set of popular open models, competing with Together AI primarily on raw token-generation speed rather than breadth of models or fine-tuning tooling.
OpenRouter is a routing and billing layer that proxies requests to many third-party model providers, including Together AI itself, under one API key, rather than operating its own inference infrastructure.
Company
Together AIFounded
2022Pricing
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