RAGFlow vs Dify
Both are alternatives to Vectara. Here's how they stack up — verified facts, no spin.
Also searched as Dify vs RAGFlow — same comparison, one verdict.
RAGFlow is open source (Apache-2.0) and Dify is not (Apache-2.0 with additional conditions) — so the real question is whether you want to own the rag & retrieval platforms stack or rent it.
RAGFlow
Deep document understanding — the one for messy PDFs and real-world files.
RAGFlow's differentiator is what happens before retrieval. Most RAG failures are not retrieval failures at all — they are parsing failures, where a table became word soup or a two-column layout interleaved into nonsense during ingestion. RAGFlow puts layout-aware document understanding at the front of the pipeline, handling tables, figures and multi-column layouts, and it shows you the chunks so you can see what the model will actually be given. Apache-2.0, ships with a web UI.
Dify
A self-hostable AI application platform — the closest product-shaped replacement.
Dify is a full LLM application platform you can run yourself: visual workflow builder, RAG pipeline, agent tooling, prompt management and observability behind a web UI. For a team replacing a managed product rather than building from parts, it is the least disruptive landing spot here. One important caveat, and we would rather state it than let you find it in a licence review: Dify ships under Apache-2.0 with additional conditions — notably restrictions around multi-tenant hosting and removing branding — so it is not open source in the unqualified sense the others here are.
Side by side
10 points of comparison, every one read from a verified field. Green marks the side that wins a row outright. A dash means we do not hold that fact — never that it is zero.
| RAGFlow | Dify | |
|---|---|---|
| Sovereignty ScoreOur transparent 0–100 composite for data ownership and exit cost. | 91 | 74 |
| Open source | Yes | No |
| Self-hostable | Yes | Yes |
| Local-first data | Yes | Yes |
| License | Apache-2.0 | Apache-2.0 with additional conditions |
| Pricing | Free to self-host, Apache-2.0. A managed cloud tier is offered separately. | Free to self-host under its modified licence; paid cloud tiers available. |
| RAM to run it wellThe figure that actually matters, not the vendor's minimum. | 16 GB | 8 GB. The default compose file starts 16 containers — api, api_websocket, worker, worker_beat, web, plugin_daemon, agent_backend, Weaviate, PostgreSQL, Redis, nginx, two SSRF proxies, two sandboxes and a one-shot init job — and Dify's own macOS instructions ask for a Docker VM with 8 GiB. More if a local model shares the box. |
| Realistic running costWhat the box costs each month if you run it yourself. | $70–120/mo — the heaviest thing on this list, and the parsing quality is why | $24–48/mo for a 4–8 GB VPS at DigitalOcean list prices (less on budget hosts), plus whatever LLM API or GPU your workflows call — Dify itself does not run the model |
| Setup timeHonest first-install estimate, not the marketing quickstart. | Half a day | 30–60 minutes with Docker Compose; an afternoon once a domain, HTTPS and SMTP are involved |
| Ongoing maintenanceThe part nobody budgets for. | Moderate to high. Five stateful services, and Elasticsearch has opinions about memory. | Frequent releases. After every upgrade, compare each .env.example against your .env for new variables and reapply your customisations to the new docker-compose.yaml rather than keeping the old one — releases add services. Back up the PostgreSQL and vector-store volumes first. |
RAGFlow edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Weighing both against staying on Vectara? Is Vectara free? What it actually costs →
RAGFlow
Strengths
- +Best-in-group parsing of tables, figures and complex layouts
- +Visible chunks — you can see and correct what was extracted
- +Full application with a UI, not just a library
- +Apache-2.0
Trade-offs
- −Heavier to deploy than a library (Docker Compose stack)
- −Opinionated pipeline — less swappable than LlamaIndex
- −Document parsing is compute-hungry on large corpora
Dify
Strengths
- +Complete product with a UI — smallest change from a managed platform
- +Visual workflow builder covers RAG, agents and prompt management
- +Self-hostable with Docker Compose
- +Active development and a large user base
Trade-offs
- −Licence carries additional conditions — not unqualified open source
- −Multi-tenant SaaS use is restricted; read the terms before building on it
- −Platform shape means less control than a framework
Which one fits you
The trade-offs above, turned into a decision. Find the line that describes your team.
Choose RAGFlow
if you want the source and the option to fork it, and best-in-group parsing of tables, figures and complex layouts.
Choose Dify
if complete product with a UI — smallest change from a managed platform.
Neither, yet
if both carry a real cost you should weigh first — heavier to deploy than a library (Docker Compose stack), and licence carries additional conditions — not unqualified open source. If either of those is a dealbreaker for your team, the shortlist is wrong rather than the choice.
What it takes to run these yourself
Real requirements and honest running costs, not the vendor quickstart.
Self-hosting RAGFlow
16 GB RAM · Half a day
$70–120/mo — the heaviest thing on this list, and the parsing quality is why
Self-hosting Dify
8 GB. The default compose file starts 16 containers — api, api_websocket, worker, worker_beat, web, plugin_daemon, agent_backend, Weaviate, PostgreSQL, Redis, nginx, two SSRF proxies, two sandboxes and a one-shot init job — and Dify's own macOS instructions ask for a Docker VM with 8 GiB. More if a local model shares the box. RAM · 30–60 minutes with Docker Compose; an afternoon once a domain, HTTPS and SMTP are involved
$24–48/mo for a 4–8 GB VPS at DigitalOcean list prices (less on budget hosts), plus whatever LLM API or GPU your workflows call — Dify itself does not run the model
RAGFlow vs Dify — common questions
Is RAGFlow a better fit than Dify for rag & retrieval platforms?
It depends on what you are optimising for, and the honest split is this: RAGFlow scores 91 to Dify's 74 on data ownership and exit cost, so it is the safer choice if you care about being able to leave. Dify earns its place on a different axis — complete product with a UI — smallest change from a managed platform. Neither is a wrong answer for every team; the table above is the actual comparison.
What happens if we want to switch later?
RAGFlow keeps its data local or in open formats, so leaving is an export rather than a negotiation. Dify is still self-hostable, so the files stay on your server either way — but it is not local-first by design, so check what its export produces before you rely on it.
Can I self-host RAGFlow or Dify?
Both can be self-hosted. The difference is what it costs you in time rather than whether it is possible — see the setup and maintenance rows above.
Are RAGFlow and Dify both alternatives to Vectara?
Yes — both appear in our Vectara comparison, which is why they are worth putting side by side. People usually arrive here already having decided to move off Vectara and now choosing between the two replacements, which is a narrower and much easier question.
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Facts verified 2026-09-26. Licenses and pricing change — spotted something out of date? That's a correction we want.