What is
Together AI
?
Most platform initiatives fail the same way: they start too big, get funded like projects instead of products, build for too long without testing with users, and confuse renaming an infrastructure team with building a platform team. Instead, start small, learn fast and scale deliberately.
At a glance
Attribute | Details |
|---|---|
Primary use case | Run, deploy, and fine-tune open-source generative AI models at scale |
Deployment type | Managed cloud platform and API with serverless and dedicated infrastructure |
Pricing model | Usage-based pricing across inference, fine-tuning, and GPU clusters; token-based provisioned throughput; custom enterprise quotes |
API compatibility | Drop-in API compatibility for provisioned throughput; target API not specified |
Supported integrations | CodeSandbox SDK, LobeHub |
Source type | Closed |
Ideal team size | Small teams to enterprise |
Required skills | Programming and API integration; model deployment and fine-tuning knowledge for advanced workloads |
AEP branch | Agent Infrastructure |
AEP category | Models |
AEP subcategory | Inference Endpoints |
AEP detail | N/A |
Profile
Inference Endpoints are services for running models through APIs or managed deployments. Together AI provides serverless, batch, provisioned-throughput, and dedicated model inference, alongside fine-tuning, GPU clusters, managed storage, and code sandboxes.
Serverless inference runs open-source models on demand without infrastructure to manage or long-term commitments. Batch inference processes workloads asynchronously and supports serverless models or private deployments. Provisioned throughput offers reserved inference capacity with token-based pricing and a 99% uptime SLA.
Focus
Together AI serves teams building with generative AI models, from experimentation to large-scale workloads. Its services address the operational needs of running inference, processing high-volume batch workloads, reserving inference capacity, deploying models on dedicated infrastructure, and fine-tuning models without managing training infrastructure.
Background
Together AI presents its platform as a full-stack AI cloud powered by research and describes its research as spanning production AI systems. The supplied company-background research does not establish the company’s founding date, funding history, or company-owned open-source contributions.
Features
Serverless inference
Run open-source models on demand without managing infrastructure or making long-term commitments. Together AI describes this as its serverless inference service.
Batch inference
Process large workloads asynchronously and cost-effectively. The service supports any serverless model or private deployment and scales to 30 billion tokens per model.
Provisioned throughput
Reserve committed inference capacity with token-based pricing and a 99% uptime SLA. Together AI states that the service has drop-in API compatibility for production workloads and requires no infrastructure management.
Dedicated model inference
Deploy models on dedicated infrastructure for workloads requiring dedicated resources. The platform describes this option as offering speed, control, and economics for teams deploying models.
Dedicated container inference
Deploy video, audio, and image models on GPU infrastructure designed for generative media workloads. Together AI says performance acceleration is powered by Together Research.
Fine-tuning
Adapt open-source models for production workloads to improve accuracy, reduce hallucinations, and control behavior. The service uses current research techniques and does not require teams to manage training infrastructure.
Accelerated compute
Run GPU workloads on clusters ranging from self-serve instant clusters to deployments with thousands of GPUs. Together AI says its Kernel Collection is used to optimize performance and claims up to 90% faster pre-training.
Research-optimized inference
Together AI says its platform uses inference research and workload-specific optimization. The vendor claims 2x faster inference and 60% lower cost.
Pros and cons
Pros
Offers serverless, batch, provisioned-throughput, dedicated-model, and dedicated-container inference options.
Batch inference supports up to 30 billion tokens per model and can run serverless models or private deployments.
Provisioned throughput includes token-based pricing, reserved capacity, drop-in API compatibility, and a 99% uptime SLA.
Fine-tuning and GPU clusters are available on the same platform as inference services.
Cons
The supplied review research does not establish Together AI-specific constraints, missing capabilities, or operational costs, so product-specific user-reported limitations cannot be verified from these sources.
API compatibility is specified for provisioned throughput only; the supplied metadata does not specify a target API.
FAQs
Frequently asked questions
How is Together AI priced?
Together AI uses usage-based pricing across inference, fine-tuning, and GPU clusters; provisioned throughput uses token-based pricing, and enterprise pricing is available through custom quotes.
Which integrations does Together AI support?
Together AI supports the CodeSandbox SDK and LobeHub integrations.
What skills are required to use Together AI?
Together AI requires programming and API integration skills; advanced workloads require model deployment and fine-tuning knowledge.
Is Together AI open source?
Together AI has a Closed source type and provides services for running open-source generative AI models.
