PrivateGPT vs Text Generation WebUI

Last reviewed Aug 26, 2026 · 737 words

PrivateGPT answers questions about your files; Text Generation WebUI runs language models. The Gradio front-end they share is a coincidence of the 2023 Python ecosystem, not a sign they compete. A folder of PDFs and "what does clause 14 say" is PrivateGPT's job and not oobabooga's. A 24 GB GPU and "which quant of which model fits" is oobabooga's job and not PrivateGPT's.

What the two actually are

Text Generation WebUI (AGPL-3.0, Python, 47,576 stars) is a workbench. It loads models through several backends, llama.cpp for GGUF, Transformers for full-weight checkpoints, ExLlama for the EXL formats, and offers a chat tab with character cards, a notebook tab where the model continues raw text, and every sampler setting down to the parameter. There is a Training tab for LoRA fine-tuning, an extensions folder for speech, TTS and a basic document lookup, and an --api flag that exposes an OpenAI-compatible server on port 5000 beside the UI on 7860.

PrivateGPT (Apache-2.0, Python, 57,466 stars) is a service. Its centre is a FastAPI process on port 8001 with ingestion endpoints and a /v1/chat/completions route that retrieves chunks from your indexed documents before answering. The Gradio page on the same port demonstrates the API. Model loading is delegated: a "profile" picks llama.cpp in-process, Ollama, or a hosted provider. No training, no sampler playground, no characters.

Install effort is a wash, for different reasons

Both carry a Medium difficulty rating and an 8,192 MB RAM floor, and both earn the "GPU recommended" con in the catalogue. Oobabooga's start_linux.sh (or the Windows and macOS equivalents) builds a Conda environment of several gigabytes, the "bulky Python environment" the catalogue warns about, but it works first time more often than not. PrivateGPT wants Poetry with the right extras and, on the default path, a llama-cpp-python build that matches your CUDA toolkit; "setup can be fiddly" is the polite version. My shortcut is to skip in-process loading, run Ollama, and use PrivateGPT's ollama profile, which removes the compile step entirely.

Two OpenAI-compatible APIs that serve different callers

Both advertise an OpenAI-compatible API, and this is where people confuse them. Oobabooga's is a model endpoint: send a prompt, get tokens. PrivateGPT's is a retrieval endpoint: the same request shape, but the server searches your ingested documents and puts the relevant passages into context before generating. Ask oobabooga to "summarise the Q3 contract" and you get a hallucination; ask PrivateGPT and you get a summary with source chunks attached, provided you ran ingestion first. The distinction is the subject of RAG vs fine-tuning, and it decides this comparison more than any feature list.

Momentum and licence

Oobabooga ships releases every few weeks and tracks new loaders quickly. PrivateGPT's commit rate has slowed since 2024 as its maintainers moved to their commercial product; the code works, but pin dependencies and expect to fix breakage yourself. The licences differ in a way that matters if you build a product: Apache-2.0 lets you embed PrivateGPT in anything, while oobabooga's AGPL-3.0 obliges you to publish source if you serve a modified version over a network. For homelab use neither clause touches you.

Decision table

What I'd do

Install Text Generation WebUI first, because whatever you end up building needs a model you have actually tested, and its loader tabs are the fastest way to learn what your hardware can hold; VRAM math covers the arithmetic before you download 30 GB of weights. Add PrivateGPT only when a real document question appears, and run it against Ollama rather than its in-process loader. If you want document chat without writing code, and most people do, AnythingLLM is the more finished answer than either of these.