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Axolotl vs Together AI

AxolotlTogether AI

Bottom line: Axolotl for mL engineers fine-tuning open models; Together AI for cost-conscious teams on open models.

AxolotlAxolotl

Open-source framework that makes LLM fine-tuning reproducible from a single YAML config

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Together AITogether AI

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

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Votes00
PricingFreeFreemium
CategoryMlopsAi Infrastructure
Tags
fine-tuningllm-trainingloraopen-sourcedistributed-training
inferencefine-tuninggpu-cloudopen-sourcellm-api
Best for
  • ML engineers fine-tuning open models
  • Research teams needing reproducibility
  • Practitioners running multi-GPU training
  • Cost-conscious teams on open models
  • ML teams that fine-tune
  • Startups scaling inference volume
Pros
  • Free and open source under MIT/Apache
  • Single YAML config makes runs reproducible
  • Supports LoRA, QLoRA, and full fine-tuning
  • Multi-GPU training with FSDP and DeepSpeed
  • Very active development and new model support
  • 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
  • Requires ML and infrastructure expertise
  • No managed UI or hosted service in the core project
  • You supply and pay for your own GPUs
  • Debugging distributed runs can be complex
  • Not aimed at non-technical users
  • 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

Comparison generated from each tool's listing. Add or remove tools above to change it.