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usage based

Together AI

Together AI provides serverless LLM inference for open models and custom fine-tuned variants, enabling builders to ship AI features without managing infrastructure.

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Overview

Together AI is an inference platform designed for builders shipping AI-powered features at scale. The service hosts open-source language models (Llama, Mistral, Qwen, and others) alongside proprietary options, offering API-first access without requiring operators to provision or manage GPU infrastructure. The platform targets teams that need predictable inference costs and model flexibility. Rather than committing to a single closed model, builders can experiment across open models, switch between providers, or fine-tune variants on Together's infrastructure. This is particularly valuable for teams building domain-specific applications where model selection directly impacts product quality and cost. Key operational decisions: Together AI charges per token (input and output), with pricing varying by model size and quantization. Verify current pricing on vendor site. The service includes batch inference for non-real-time workloads, which can reduce per-token costs significantly. Builders can also upload custom fine-tuned models and serve them through the same API, avoiding vendor lock-in on model weights. Integration is straightforward for teams already using LLM APIs—the SDKs and REST endpoints follow familiar patterns. The platform supports streaming responses, which is essential for user-facing chat and generation features. Operators should verify current model availability and SLA terms on the vendor site, as model catalogs and performance guarantees evolve. Common use cases include: chat interfaces powered by open models, content generation with fine-tuned variants, retrieval-augmented generation (RAG) pipelines, and cost-optimized inference for high-volume applications. Teams often choose Together AI when they need to balance cost, model control, and infrastructure simplicity—particularly if they're evaluating multiple models or building multi-tenant systems where model selection is a product decision.

Key features

  • Serverless API access to 100+ open-source and proprietary models (verify current catalog on vendor site)
  • Per-token pricing with batch inference discounts for non-real-time workloads
  • Custom model fine-tuning and deployment on shared infrastructure
  • Streaming responses for real-time chat and generation features
  • REST and Python SDK interfaces with familiar LLM API patterns
  • Model switching without application code changes (verify feature scope on vendor site)

Use cases

  • Ship chat interfaces powered by open-source language models without managing GPU infrastructure
  • Fine-tune and serve custom models for domain-specific tasks (legal, medical, technical domains)
  • Build cost-optimized inference pipelines for high-volume text generation and summarization
  • Experiment across multiple models (Llama, Mistral, Qwen) to optimize for quality and latency
  • Implement batch inference for non-real-time workloads like document processing and data enrichment
  • Create multi-tenant systems where end users or customers can select their preferred model

Advantages

  • No infrastructure management required—focus on product, not GPU provisioning
  • Model flexibility: experiment across open models and fine-tuned variants without vendor lock-in
  • Transparent, usage-based pricing with batch discounts for cost optimization
  • Familiar API patterns reduce integration time for teams experienced with LLM APIs
  • Fine-tuning and custom model hosting on shared infrastructure lowers barrier to model customization

Limitations

  • Inference latency and availability depend on shared infrastructure (verify SLA terms on vendor site)
  • Per-token pricing can become expensive at very high volumes compared to self-hosted solutions
  • Model catalog and availability subject to change; verify current offerings before architecture decisions
  • Limited control over underlying hardware and optimization compared to dedicated GPU rental
  • Custom fine-tuning requires familiarity with model training; not a managed service for non-technical teams

Alternatives

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At a glance

Starting See vendor site — sample data

  • Free plan available
  • Free trial available
  • API available
  • Closed source

Integrations

LangChain, LlamaIndex, Hugging Face, OpenAI-compatible endpoints

solo
small
mid market
enterprise

Ekofi Lyrae

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