Chat with your enterprise data using LLM vs private-gpt
Side-by-side comparison of two AI agent tools
Short answer
- Chat with your enterprise data using LLM has had no commit in 21 months; private-gpt is actively maintained (62 commits in the last 90 days).
- private-gpt is growing faster: +56 GitHub stars in the last 30 days vs +-0 for Chat with your enterprise data using LLM.
- Pick Chat with your enterprise data using LLM for: open-source sample for chatting with uploaded enterprise data using Azure OpenAI and vector search. Pick private-gpt for: interact with your documents using the power of GPT, 100% privately, no data leaks.
From GitHub data refreshed daily.
Chat with your enterprise data using LLMopen-source
Open-source sample for chatting with uploaded enterprise data using Azure OpenAI and vector search
private-gptopen-source
Interact with your documents using the power of GPT, 100% privately, no data leaks
Metrics
| Chat with your enterprise data using LLM | private-gpt | |
|---|---|---|
| Stars | 865 | 57.6k |
| Star velocity /mo | -0.4736842105263158 | 55.89473684210526 |
| Commits (90d) | 0 | 62 |
| Releases (6m) | 0 | 4 |
| Overall score | 0.12103260760690508 | 0.5274831341481462 |
Pros
- +Supports multiple vector stores (Pinecone, Redis, Azure Cognitive Search) providing flexibility in deployment options
- +Includes comprehensive evaluation framework with Prompt Flow integration and metrics like groundedness and Ada similarity
- +Active development with regular updates and refactoring to improve core functionality and remove complexity
- +Complete data privacy with 100% local processing and no external data transmission
- +Production-ready with comprehensive API following OpenAI standards and streaming support
- +Flexible architecture offering both high-level RAG pipeline and low-level API for custom implementations
Cons
- -Designed as a sample application rather than production-ready solution, requiring additional development for enterprise deployment
- -Specifically tied to Azure OpenAI Service, limiting flexibility in LLM provider choice
- -Has undergone multiple refactoring cycles that removed features, suggesting potential instability in feature set
- -Requires significant local compute resources to run LLMs effectively
- -Setup complexity may be challenging for non-technical users
- -Limited to documents that can be processed and stored locally
Use Cases
- •Enterprise document Q&A systems where employees need to query internal knowledge bases using natural language
- •Internal chatbots for customer support teams to quickly access company policies and procedures
- •Research and development teams building custom RAG applications for proprietary data analysis
- •Enterprise document analysis for regulated industries requiring complete data privacy
- •Offline research and document querying in environments without internet connectivity
- •Building custom AI applications with contextual document understanding without cloud dependencies
FAQ
- Which is more popular, Chat with your enterprise data using LLM or private-gpt?
- private-gpt has more GitHub stars (57,558 vs 865).
- Which is more actively developed, Chat with your enterprise data using LLM or private-gpt?
- private-gpt had more commits in the last 90 days (62 vs 0).
- Should I use Chat with your enterprise data using LLM or private-gpt?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.