--- title: "Dify" type: "AI Tool" url: "https://aidemos.com/tools/dify" description: "Hands-on Dify tests showed strong table answers, ingestion, and connector sync, but scanned docs, Hindi retrieval, and citation drift broke." category: "developer-tools" website: "https://dify.ai" published: "2026-08-29T08:43:47.884699+00:00" updated: "2026-08-29T10:25:09.223286+00:00" --- # Dify API-first managed RAG with strong table answers and connectors, but you still need guardrails for scans, multilingual retrieval, and refusal cases. ## TL;DR Verdict **Good core, but not safe to trust blindly** **Where it wins:** - you want an API-first managed RAG service with upload/connect workflows, citations, and configurable chunking - you need built-in connectors, Connector sync, and verified dataset isolation - you can tolerate slower responses and will manually check edge cases **Main limitation:** you need reliable scanned-PDF retrieval **Pricing:** Professional $59.00 · Team $159.00 · Enterprise Custom · Sandbox Free `RAG API` · `Connector sync` · `OCR gap` **Website:** [Visit Dify](https://dify.ai) > **Good core, but not safe to trust blindly** > > Dify looks like a real API-first RAG platform, not a marketing shell: ingestion, connector sync, and tenant isolation all held up in hands-on testing. But the evaluation also found three meaningful weaknesses — scanned-document retrieval, multilingual retrieval, and out-of-corpus hallucinations/citation drift — plus noticeably slow latency. It is promising if you can add guardrails and manual checks; it is risky if you need the system to be correct by default. ## Demo Recording [Video: Dify demo recording](https://cdn.futuresmart.ai/public/aidemos/c301ac46c9a5481ab823c9d1dbd03929.mov?v=1) *Video — Screen-recorded live session showing the Dify home dashboard, a Create Custom Tool modal, and knowledge-base API documentation.* ## Feature-by-Feature Breakdown ### Document Ingestion and Indexing **Verdict:** Uploads and indexing are real, but dense PDFs need chunking control to avoid over-fragmentation and storage blowups. Dify can take uploaded source material through its REST API and build a searchable knowledge base from PDFs and mixed-format documents. The tested corpus indexed without manual intervention, though dense PDFs sometimes needed chunking tuned. **Input:** > **File** **Output:** > **Image** **Bottom line:** The ingestion path is genuine and automatable, but the default chunking strategy needs tuning on dense documents. ### Grounded Corpus Question Answering — 8/10 **Verdict:** Strong on ordinary grounded QA, including direct facts, table cells, and deep long-doc passages. Dify can answer factual questions from the indexed corpus, including direct lookups, table/numeric extraction from PDFs, and buried questions in long reports. The same grounded QA path also correctly refused many questions the corpus could not answer and used citations when answering from source. **Input:** > **File** **Output:** > **Image** **Input:** > **File** **Output:** > **Image** **Input:** > **File** **Output:** > **Image** **Input:** ``` September 2026 FOMC target-range question, unanswerable from the July 2026 corpus ``` **Output:** > **Image** **Input:** ``` Same question, citation-fidelity inspection ``` **Output:** > **Image** **Input:** ``` Ende, Indonesia M7.7 casualties question ``` **Output:** > **Image** **Bottom line:** It is useful for standard fact lookup and long-doc Q&A when the answer is actually in the corpus. ### Cross-Document Synthesis **Verdict:** Good on English cross-document joins and version selection, but the weakness shows up when the join depends on Hindi content. Dify can combine or choose among related documents when answering, such as multi-hop prompts, superseded-vs-current policy pairs, and near-duplicate guide editions. The tested cases worked well in English-only scenarios. **Input:** ``` English multi-hop question combining two corpus documents ``` **Output:** > **Image** **Input:** > **File** **Output:** > **Image** **Input:** > **File** **Output:** > **Image** **Bottom line:** English cross-document reasoning is decent; the failure mode shows up when the needed join crosses into Hindi. ### Multilingual Retrieval — 0/4 **Verdict:** The Hindi document ingests fine, but retrieval from Hindi content does not work in the tested set. Dify indexed the Hindi RBI PDF, but questions that depended on it failed, including mixed Hindi-English prompts. The tested behavior shows language-specific retrieval support as a distinct capability, even though the Hindi side was not successfully surfaced. **Input:** > **File** **Output:** ``` Direct factual questions requiring the Hindi RBI PDF failed on all 3 trials. ``` **Input:** ``` Multi-hop question that required combining an English Fed source with the Hindi RBI source ``` **Output:** ``` Cross-lingual multi-hop prompts also failed on all 3 trials; retrieval only surfaced the English side. ``` **Bottom line:** Hindi indexing works; Hindi retrieval does not. ### Connector Sync and Retrieval **Verdict:** A real connector ecosystem exists, but it is plugin-based and carries onboarding friction. Dify supports marketplace data-source connectors and can sync live external sources such as Notion into Knowledge. The tested connector path indexed a real page and made it retrievable, though it required plugin-install onboarding. **Input:** ``` Connect a real Notion workspace via OAuth and sync one page ``` **Output:** > **Image** **Input:** ``` Browse the Data Source marketplace for available connectors ``` **Output:** > **Image** **Bottom line:** Connector support is real, but every source still has plugin-install onboarding friction. ### Dataset Isolation **Verdict:** No cross-tenant leakage was observed in the isolation test. Separate datasets stayed isolated in testing: a second dataset containing only part of the corpus did not leak answers from excluded documents. A question answerable only from missing files returned irrelevant chunks instead. **Input:** ``` Ask a Pub 15-only question against the second tenant dataset ``` **Output:** > **File** **Input:** ``` Workspace dataset list showing the separate tenant2 corpus ``` **Output:** > **Image** **Bottom line:** Isolation held cleanly with no cross-tenant leakage. ## There’s Always a Plan for You Dify Cloud, Sandbox, Community, and Enterprise options | Plan | Price | Notes | | --- | --- | --- | | Professional ★ | $59.00 | Dify Cloud; most popular | | Team | $159.00 | Dify Cloud | | Enterprise | Custom | Self-hosted & Enterprise; contact sales | | Sandbox | Free | Free tier | | Community | Free | Open-source self-hosted | *The pricing capture shows a Monthly/Annually toggle and a mix of paid and free tiers. The report also notes Sandbox's 200 one-time credits and 50 MB storage cap, plus fully self-serve paid tiers and a quote-gated Enterprise option.* ## Is It Right For You? **Use it if** - you want an API-first managed RAG service with upload/connect workflows, citations, and configurable chunking - you need built-in connectors, Connector sync, and verified dataset isolation - you can tolerate slower responses and will manually check edge cases **Skip it if** - you need reliable scanned-PDF retrieval - you need strong cross-lingual retrieval from documents - you need airtight refusal behavior on out-of-corpus questions or very low latency ## Classification - **Category:** developer-tools - **Subcategory:** agent-platforms - **Type:** text - **Built for:** Other ## Frequently Asked Questions **Q: How well does Dify handle table-heavy PDFs?** Very well in this evaluation. On the Sumitomo Heavy Industries table-heavy PDF, it answered 7/8 table and numeric questions correctly, including the Q1 2025 net sales figure and other direct financial lookups. The one consistent miss answered the wrong earthquake row. **Q: Does Dify refuse questions that are not in the documents?** Mostly, but not perfectly. It handled 8/10 out-of-corpus questions correctly and refused them, but 2/10 were reproducible hallucinations: one answer came with a citation that did not actually support the claim, and another was fabricated with no citation at all. **Q: Can Dify retrieve from scanned PDFs?** Not reliably in this test. The scanned USDA PDF was OCR'd into text, but retrieval still failed on every scanned-document question, so the problem was retrieval rather than extraction. **Q: Does Dify support connectors like Notion?** Yes. The hands-on test used a real Notion OAuth sync, and the synced page became available in Knowledge and retrievable. The marketplace also showed 25+ connector plugins, but they are not pre-installed. **Q: How did Dify do on contradictory or updated documents?** Mostly well. It usually chose the current 2026 IRS publication over the superseded 2025 version and selected the newer near-duplicate edition correctly. One broader version question was flaky and fell back to stale model memory on one trial. **Q: What does Dify pricing look like?** The captured pricing page showed Professional at $59.00, Team at $159.00, Enterprise as custom pricing, and both Sandbox and Community as free. The report also notes Sandbox usage caps and that paid tiers are self-serve. ## Similar Tools AI tools similar to Dify: - [Vectara](https://aidemos.com/tools/vectara) — Vectara gives you a grounded-answer API with strong refusals, but tables, contradictions, scans, and pricing are the weak spots. - [Vectorize.io](https://aidemos.com/tools/vectorize-io) — Managed RAG API with clean ingestion, OCR, and public pricing, but with caveats on refusals and freshness. ## Need a custom AI solution for this use case? If you are looking to build a custom RAG assistant, knowledge base chatbot, or internal search system for your business or internal workflow, email us at [contact@futuresmart.ai](mailto:contact@futuresmart.ai). ### Found something inaccurate or missing? We try to keep our AI research accurate and useful. If you found outdated information, an issue, or have a suggestion, email us at [collaborate@aidemos.com](mailto:collaborate@aidemos.com).