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Together AI
Together AI provides managed inference, fine-tuning, training, and GPU infrastructure for developers and teams building production applications with open-source models.
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›What is Together AI?
Together AI is a cloud platform for working with open-weight AI models. It offers serverless and dedicated inference, batch workloads, fine-tuning, custom training, evaluations, GPU clusters, managed storage, and developer environments. Its model library includes more than 200 open-source models for chat, code, image, audio, video, embeddings, and related tasks.
›What are the pros and cons of Together AI?
Strengths
Provides access to more than 200 open-source models
Covers serverless inference, dedicated endpoints, fine-tuning, training, and GPU clusters in one platform
Supports specialized inference workloads including embeddings, reranking, moderation, transcription, and video
Offers dedicated inference and provisioned throughput for production workloads that need reserved capacity
Includes evaluations, managed storage, developer environments, demos, and cookbooks
Trade-offs
It does not provide frontier proprietary models, so it is not a universal replacement for providers centered on those models
Quality, latency, and reliability require evaluation for each model and endpoint type
Usage-based inference and hourly dedicated GPU billing can make costs vary substantially by model and workload
The platform offers many infrastructure and model-shaping options, which may be more than a team needs for simple inference
›What are Together AI’s key features?
Serverless inference APIs for chat, vision, image, audio, video, transcription, embeddings, reranking, and moderation
Provisioned throughput and dedicated inference for reserved capacity or custom hardware
Managed fine-tuning and custom training for adapting open-source models
GPU clusters and custom infrastructure for larger training and inference workloads
Model evaluations for measuring model quality
Managed storage for model weights and data
Developer environments, documentation, demos, and implementation cookbooks
›What are the best use cases for Together AI?
Testing and comparing open-source models before selecting one for an application
Serving moderate or production inference traffic through serverless or dedicated endpoints
Fine-tuning an open model on proprietary or domain-specific data
Running custom model training on rented GPU clusters
Processing batch inference workloads
Building voice agents and other applications with open models
›What is the pricing for Together AI?
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›Who is Together AI best for?
developersA strong fit for developers who want API access to a broad range of open-source models without managing GPU infrastructure.
small teamSmall teams can start with serverless inference and move to dedicated endpoints as production traffic grows.
enterpriseEnterprise teams with training, dedicated capacity, or custom infrastructure requirements can use its GPU clusters and provisioned inference options.
data scientistsData scientists can use fine-tuning, custom training, evaluations, and managed storage across the model development lifecycle.
Not for
- Teams that need a single frontier proprietary model rather than a broad open-source model library.
- Buyers who require a simple fixed subscription instead of usage-based inference and hourly GPU charges.
