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Refact.ai vs Meta Llama
This comparison was retrieved by database IDs selected from the catalog.
Refact.ai
Refact.ai
Meta Llama
Meta AI
Free and open source self-hosted version. Refact.ai cloud pro plan $20/month. Enterprise on-premise custom pricing.
Free to download and use commercially (with usage limits at scale). Meta provides API access via partners (AWS, Azure, NVIDIA). Open source under the Llama Community License.
Self-hosted: data never leaves customer's infrastructure. Cloud version: review Refact.ai privacy policy. Enterprise includes data handling agreements.
Open source model under Meta Llama Community License. Derivative models subject to same licence terms. Commercial use permitted with usage threshold restrictions.
Meta's data handling: no data sent to Meta when running locally. Cloud API providers subject to their own data handling policies.
Self-hosted deployment keeps all code within customer's own infrastructure. Cloud service processes code on Refact.ai's servers — review privacy policy for cloud version use.
When running Llama locally, no data is sent to Meta or any external party. Meta's cloud API providers have their own data handling policies. Review the Llama Community License for commercial use restrictions above 700M MAU.
- Self-hosted deployment keeps code within customer infrastructure — strongest data privacy among AI coding tools
- Open source model allows inspection and modification of the server code
- Multiple model support gives flexibility to choose based on capabilities and GPU hardware
- Agentic multi-step coding in a self-hosted package is unique in this space
- Completely free to use without API fees — massive cost advantage for high-volume applications
- Full data privacy when run locally — no data leaves company infrastructure
- Largest ecosystem of fine-tuned derivatives, tools, and community support of any open source model
- Llama 3.1 405B performance competitive with frontier proprietary models on many benchmarks
- Self-hosting requires GPU hardware investment and IT operational overhead
- Model quality is competitive but below frontier models like Claude and GPT-4o on complex coding tasks
- Active development but smaller community than GitHub Copilot or Cursor ecosystems
- Running 405B model requires significant GPU hardware investment for on-premise deployment
- Smaller Llama variants (8B) less capable than frontier proprietary models for complex tasks
- Meta's commercial use licence has restrictions at very large scale (700M+ MAU) — review for large applications
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