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LangGraph vs Together AI

LangGraphTogether AI

Bottom line: LangGraph for engineering teams building production agents; Together AI for cost-conscious teams on open models.

LangGraphLangGraph

Graph-based orchestration for stateful, controllable LLM agents.

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Together AITogether AI

Inference, fine-tuning, and GPU clusters for open models.

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Votes00
PricingFreemiumFreemium
CategoryCodingCoding
Tags
agentsorchestrationllm-frameworkopen-sourceworkflow
inferencefine-tuninggpu-cloudopen-sourcellm-api
Best for
  • Engineering teams building production agents
  • Developers needing controllable, resumable workflows
  • Teams already invested in LangChain
  • Cost-conscious teams on open models
  • ML teams that fine-tune
  • Startups scaling inference volume
Pros
  • Explicit, debuggable control over agent state and flow
  • MIT-licensed core, free to self-host
  • Model-agnostic, not tied to one LLM vendor
  • Strong support for cycles, checkpointing, and human-in-the-loop
  • Backed by the widely used LangChain ecosystem
  • Large catalog of open and open-weight models
  • Competitive per-token pricing
  • Fine-tuning with weight ownership
  • Dedicated GPU clusters for scale
  • OpenAI-compatible API
Cons
  • Steeper learning curve than high-level agent builders
  • Requires comfort with graph and state concepts
  • Paid platform and observability are separate products
  • Documentation and APIs have evolved quickly
  • Can be overkill for simple single-shot LLM tasks
  • Broad pricing surface across several product lines
  • You own quality and safety evaluation of open models
  • Dedicated clusters require commitment and planning
  • Less turnkey than closed frontier APIs
  • Model catalog and prices change over time

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