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.
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AI Editor ApprovedApproved and published by our AI Editor-in-Chief after full panel analysis.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.
Fine-tunes open-source models for production workloads using the latest research techniques to improve accuracy, reduce hallucinations, and control behavior without managing training infrastructure.
A collection of GPU kernels that enables up to 90% faster pre-training and optimized performance across compute workloads.
Scales from self-serve instant clusters to thousands of GPUs, optimized for better performance using the Together Kernel Collection.
Processes massive workloads asynchronously at scale up to 30 billion tokens per model with any serverless model or private deployment.
Provides GPU infrastructure purpose-built for generative media workloads, supporting video, audio, and image model deployment with performance acceleration.
Deploys models on dedicated infrastructure purpose-built for teams who need speed, control, and optimized economics.
Offers high-performance managed object storage and parallel filesystems optimized for AI-native workloads with zero egress fees.
Provides fast, secure code sandboxes at scale for setting up full-scale development environments for AI apps and agents.
Runs open-source models on demand with no infrastructure to manage and no long-term commitments, powered by cutting-edge inference research.
Applies workload-specific optimizations to reduce infrastructure costs by up to 60% compared to standard deployments.
Pay-per-token API access to hosted models. Most teams start here.
Single-tenant GPU instances for teams needing guaranteed performance and custom models.
Pay-as-you-go GPU cluster capacity billed hourly.
Reserved GPU cluster capacity for 6+ days with discounted rates.
VM sandboxes and secure code interpreter for LLM-generated code execution.
Train open-source models up to 100B parameters using LoRA or full fine-tuning.
Fine-tuning for large specialized models like DeepSeek, Llama 4, Qwen3, Kimi K2, and others.
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 $0.48 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 $0.02 per million input tokens for some 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 $0.02-per-million-token serverless to $3.49/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 on-demand at $3.49/hr, Llama-class inference from $0.02 per million 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 reserved drops from $3.49 to $2.55/hr if you commit four months — Together's discount curve is honest.
“Together publishes every tier on its pricing page, from $0.02 per million tokens for inference to $2.55/hr for an H100 on a 4-6 month reservation. The catch is Specialized Fine-Tuning — minimums up to $60 per job mean small experiments aren't free.”
Pricing is fully published. Every tier, every GPU, every per-token rate. Inference starts at $0.02 per 1M input tokens. H100 on-demand at $3.49/hr — same shape as Lambda Labs, cheaper than AWS Bedrock provisioned throughput. Procurement won't push back.
The reserved-GPU curve is where the math gets honest. H100 drops to $2.99/hr at one week, $2.55/hr at 4-6 months. Six-day minimum. One H100 reserved four months runs about $7,300 — versus $10,000 on-demand. 27% saving compounds across a fleet, but the 6-day floor punishes spiky workloads.
Two line items matter. Specialized Fine-Tuning carries minimum charges — $20 for DeepSeek-R1 LoRA, up to $60 for GLM-5. Small experiments aren't free. However, Managed Storage charges zero egress, which offsets a year of S3 transfer for inference-heavy teams. Read the contract, not the marketing.
Usage-based with web checkout for serverless removes most procurement friction.
On-demand and reserved are both available; only the 6-day reserved minimum limits flexibility.
Every tier and GPU rate is published; serverless and on-demand require no sales call.
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 inference and want every rate public before signing.
Teams who need spiky GPU access without committing to a six-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 to 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 lists every number on one screen, and that small thing tells you a lot.
“The playground works without a signup, the pricing page lists every number, and the OpenAI-shaped endpoint means your client code just works. The catch is the docs lag the model catalog by a release.”
The playground at api.together.xyz/playground is the small thing the team got right. No credit card to sign in, 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 on-demand at $3.49 an hour, Llama-class inference from $0.02 per million input tokens, Code Interpreter at $0.03 per 60-minute session — every number on one page. Modal makes you log in to see GPU hourly.
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 every number on one screen and the playground works without a signup.
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.
No credit card to reach the playground, 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.
The evidence holds up. $305M Series B at $3.3B in February 2025, led by General Catalyst and Prosperity7. Serverless Inference from $0.02 per million input tokens. H100 reserved at $2.55/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 lists every GPU tier, per-token rate, and minimum charge — claims are quantified, not aspirational.
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
For models up to 16B, Supervised Fine-Tuning costs $0.48/1M tokens for LoRA vs $0.54/1M tokens for Full Fine-Tuning, and Direct Preference Optimization costs $1.20/1M tokens for LoRA vs $1.35/1M tokens for Full Fine-Tuning. The standard pricing table for up to 16B models does not list a minimum charge; minimum charges appear only in the Specialized pricing section for specific models.
Yes, Dedicated Container Inference is described as 'GPU infrastructure purpose-built for generative media workloads' that supports deploying 'video, audio, and image models with performance acceleration powered by Together Research.' However, the pricing page only lists hourly hardware options under Dedicated Inference (not Dedicated Container Inference specifically): 1x H100 80GB at $3.99/hr, 1x H200 141GB at $5.49/hr, and 1x B200 180GB at $9.95/hr.
The content describes Code Sandboxes as 'fast, secure code sandboxes' but does not specify single-tenant isolation. Pricing is structured as $0.0446 per vCPU/hour and $0.0149 per GiB RAM/hour for compute costs, plus a Code Interpreter option priced at $0.03 per 60-minute session.
Serverless Inference is described as 'the fastest way to run open-source models on demand' with 'no infrastructure to manage, no long-term commitments.' You can get started immediately through the platform without any setup or commitment requirements.
Yes, Batch Inference explicitly supports 'any serverless model or private deployment' and can 'scale to 30 billion tokens per model.'
Company
Together AIFounded
2022Pricing
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