Layer 4 · self-hosting reality check
What it actually takes to self-host RAGFlow
The docs say 4 GB. In practice you want 16 GB. Here is the honest version — real requirements, real monthly cost, what you will be maintaining, and the one thing that catches people out.
Usually reached from Vectara alternatives, where RAGFlow is one of the picks.
| RAM — documented minimum | 4 GB |
|---|---|
| RAM — what it really needs | 16 GB |
| CPU | 8 vCPU; a GPU materially speeds document parsing |
| Disk | 40 GB plus your corpus, and parsed artifacts are larger than the sources |
| Monthly cost | $70–120/mo — the heaviest thing on this list, and the parsing quality is why |
| Setup time | Half a day |
| How you install it | docker compose — it bundles Elasticsearch, MySQL, MinIO and Redis alongside the app |
| Ongoing maintenance | Moderate to high. Five stateful services, and Elasticsearch has opinions about memory. |
| Where it stops scaling | Large corpora with difficult documents — that is its whole purpose. Parsing throughput binds first. |
The thing that catches people out
Document parsing is the reason to choose RAGFlow and it is genuinely compute-hungry — a large PDF corpus can take days on CPU, and the process looks hung when it is working. Provision a GPU if the corpus is real, and never start a large ingest on a machine you need for something else that week.
When not to self-host RAGFlow
Your documents are clean text or markdown. RAGFlow's deep parsing is wasted on them and you are paying for it in RAM.
Every guide here carries this section. A site that only ever tells you to self-host is selling something — the useful answer is sometimes no.
Other Layer 4 self-hosting guides
- Self-hosting Ollama8 GB VRAM for a 7B model at usable speed; 24 GB for 30B-class
- Self-hosting vLLM24 GB VRAM minimum for useful production serving
- Self-hosting QdrantVectors × dimensions × 4 bytes, in RAM, plus overhead — 1M × 768d is roughly 3 GB
- Self-hosting pgvector8 GB — the HNSW index wants to be resident
- Self-hosting LlamaIndex4 GB for the app; your vector store is the real cost
- Self-hosting faster-whisper5 GB VRAM for large-v3 in float16; 2 GB with int8
Common questions
- How much RAM does RAGFlow actually need?
- 16 GB in practice. The documented minimum is 4 GB, which is the figure at which the process starts rather than the figure at which it works under real use. 8 vCPU; a GPU materially speeds document parsing alongside it.
- What does self-hosting RAGFlow cost per month?
- $70–120/mo — the heaviest thing on this list, and the parsing quality is why This is commodity VPS pricing and excludes your time, which is the larger cost for most people — budget for moderate to high. Five stateful services, and Elasticsearch has opinions about memory.
- How long does it take to set up RAGFlow?
- Half a day, via docker compose — it bundles Elasticsearch, MySQL, MinIO and Redis alongside the app.
- When should I NOT self-host RAGFlow?
- Your documents are clean text or markdown. RAGFlow's deep parsing is wasted on them and you are paying for it in RAM.
- What is the most common mistake when self-hosting RAGFlow?
- Document parsing is the reason to choose RAGFlow and it is genuinely compute-hungry — a large PDF corpus can take days on CPU, and the process looks hung when it is working. Provision a GPU if the corpus is real, and never start a large ingest on a machine you need for something else that week.