Dify icon
developer-tools

Dify

API-first managed RAG with strong table answers and connectors, but you still need guardrails for scans, multilingual retrieval, and refusal cases.

Visit Dify
RAG APIConnector syncOCR gap
TL;DR — our verdictUpdated August 2026 · 14 test artifacts

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 (verified plans)
Professional $59.00Team $159.00Enterprise CustomSandbox Free
Strongest test artifacts
OUTPUT →

Our take

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.

Download video: Demo video
Screen-recorded live session showing the Dify home dashboard, a Create Custom Tool modal, and knowledge-base API documentation.

In-Depth Review

Our detailed analysis of Dify — features, performance, and real-world testing.

AD
AI Demos Team
Expert Reviewer
Verified Review

Feature-by-Feature Breakdown

Document Ingestion and Indexing
Uploads and indexing are real, but dense PDFs need chunking control to avoid over-fragmentation and storage blowups.
▾
Test Summary
Feature tested: Document Ingestion and Indexing
Result: Passed — Uploads and indexing are real, but dense PDFs need chunking control to avoid over-fragmentation and storage blowups.

Feature tested: Document Ingestion and Indexing

Result: Passed

Verdict: Uploads and indexing are real, but dense PDFs need chunking control to avoid over-fragmentation and storage blowups.

Expected behavior: 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.

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Table-heavy Sumitomo Q1 2025 PDF used for upload testing. — 01_tables_sumitomo-heavy-industries-Q1-2025.pdf

Observed output: Output artifact (Image): HTTP 200 upload call with real document and batch IDs; the upload/indexing path is live. — code_evidence_upload_api_BEST.png

Input artifact: Input artifact (PDF document): Table-heavy Sumitomo Q1 2025 PDF used for upload testing. — 01_tables_sumitomo-heavy-industries-Q1-2025.pdf

Output artifact: Output artifact (Image): HTTP 200 upload call with real document and batch IDs; the upload/indexing path is live. — code_evidence_upload_api_BEST.png

What changed: PDF document transformed into Image

Why it matters / Conclusion: The ingestion path is genuine and automatable, but the default chunking strategy needs tuning on dense documents.

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.

file
01_tables_sumitomo-heavy-industries-Q1-2025.pdf
Table-heavy Sumitomo Q1 2025 PDF used for upload testing.
↓
image
Output artifact for "Document Ingestion and Indexing" test: HTTP 200 upload call with real document and batch IDs; the upload/indexing path is live., code_evidence_upload_api_BEST.png
HTTP 200 upload call with real document and batch IDs; the upload/indexing path is live.
Bottom Line
The ingestion path is genuine and automatable, but the default chunking strategy needs tuning on dense documents.
Grounded Corpus Question Answering
Strong on ordinary grounded QA, including direct facts, table cells, and deep long-doc passages.
8/10
▾
Test Summary
Feature tested: Grounded Corpus Question Answering
Result: Partial (8/10) — Strong on ordinary grounded QA, including direct facts, table cells, and deep long-doc passages.

Feature tested: Grounded Corpus Question Answering

Result: Partial (8/10)

Verdict: Strong on ordinary grounded QA, including direct facts, table cells, and deep long-doc passages.

Expected behavior: 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.

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Direct factual question from the Sumitomo PDF about listing venue and stock code. — 01_tables_sumitomo-heavy-industries-Q1-2025.pdf

Observed output: Output artifact (Image): Correct direct factual answer from the knowledge base: Tokyo Stock Exchange, stock code 6302. — ui_D7_direct_factual_BEST.png

Input artifact: Input artifact (PDF document): Direct factual question from the Sumitomo PDF about listing venue and stock code. — 01_tables_sumitomo-heavy-industries-Q1-2025.pdf

Output artifact: Output artifact (Image): Correct direct factual answer from the knowledge base: Tokyo Stock Exchange, stock code 6302. — ui_D7_direct_factual_BEST.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Table/numeric question from the same Sumitomo PDF. — 01_tables_sumitomo-heavy-industries-Q1-2025.pdf

Observed output: Output artifact (Image): Correct table answer: Q1 2025 consolidated net sales of 241,536 million yen. — ui_T1_table_numeric_BEST.png

Input artifact: Input artifact (PDF document): Table/numeric question from the same Sumitomo PDF. — 01_tables_sumitomo-heavy-industries-Q1-2025.pdf

Output artifact: Output artifact (Image): Correct table answer: Q1 2025 consolidated net sales of 241,536 million yen. — ui_T1_table_numeric_BEST.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Deep-in-long-document question from the Fed report. — 03_longform_fed-monetary-policy-report-2026-07.pdf

Observed output: Output artifact (Image): Correct buried-answer retrieval from the long report, including the 2.2 percent 2026 real GDP projection. — ui_DL1_deep_in_long_doc_BEST.png

Input artifact: Input artifact (PDF document): Deep-in-long-document question from the Fed report. — 03_longform_fed-monetary-policy-report-2026-07.pdf

Output artifact: Output artifact (Image): Correct buried-answer retrieval from the long report, including the 2.2 percent 2026 real GDP projection. — ui_DL1_deep_in_long_doc_BEST.png

What changed: PDF document transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Hallucinated 3.5% to 3.75% with a citation attached. — api_evidence_02_O4_hallucination_with_citation_BEST.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Hallucinated 3.5% to 3.75% with a citation attached. — api_evidence_02_O4_hallucination_with_citation_BEST.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The cited chunk does not actually support the September claim. — api_evidence_03_O4_citation_fidelity_FAIL_BEST.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The cited chunk does not actually support the September claim. — api_evidence_03_O4_citation_fidelity_FAIL_BEST.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Fabricated a casualty figure from a different earthquake and used no citation. — code_evidence_O6_hallucination_BEST.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Fabricated a casualty figure from a different earthquake and used no citation. — code_evidence_O6_hallucination_BEST.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: It is useful for standard fact lookup and long-doc Q&A when the answer is actually in the corpus.

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.

file
01_tables_sumitomo-heavy-industries-Q1-2025.pdf
Direct factual question from the Sumitomo PDF about listing venue and stock code.
↓
image
Output artifact for "Grounded Corpus Question Answering" test: Correct direct factual answer from the knowledge base: Tokyo Stock Exchange, stock code 6302., ui_D7_direct_factual_BEST.png
Correct direct factual answer from the knowledge base: Tokyo Stock Exchange, stock code 6302.
file
01_tables_sumitomo-heavy-industries-Q1-2025.pdf
Table/numeric question from the same Sumitomo PDF.
↓
image
Output artifact for "Grounded Corpus Question Answering" test: Correct table answer: Q1 2025 consolidated net sales of 241,536 million yen., ui_T1_table_numeric_BEST.png
Correct table answer: Q1 2025 consolidated net sales of 241,536 million yen.
file
03_longform_fed-monetary-policy-report-2026-07.pdf
Deep-in-long-document question from the Fed report.
↓
image
Output artifact for "Grounded Corpus Question Answering" test: Correct buried-answer retrieval from the long report, including the 2.2 percent 2026 real GDP projection., ui_DL1_deep_in_long_doc_BEST.png
Correct buried-answer retrieval from the long report, including the 2.2 percent 2026 real GDP projection.
INPUT
September 2026 FOMC target-range question, unanswerable from the July 2026 corpus
↓
image
Output artifact for "Grounded Corpus Question Answering" test: Hallucinated 3.5% to 3.75% with a citation attached., api_evidence_02_O4_hallucination_with_citation_BEST.png
Hallucinated 3.5% to 3.75% with a citation attached.
INPUT
Same question, citation-fidelity inspection
↓
image
Output artifact for "Grounded Corpus Question Answering" test: The cited chunk does not actually support the September claim., api_evidence_03_O4_citation_fidelity_FAIL_BEST.png
The cited chunk does not actually support the September claim.
INPUT
Ende, Indonesia M7.7 casualties question
↓
image
Output artifact for "Grounded Corpus Question Answering" test: Fabricated a casualty figure from a different earthquake and used no citation., code_evidence_O6_hallucination_BEST.png
Fabricated a casualty figure from a different earthquake and used no citation.
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
Good on English cross-document joins and version selection, but the weakness shows up when the join depends on Hindi content.
▾
Test Summary
Feature tested: Cross-Document Synthesis
Result: Passed — Good on English cross-document joins and version selection, but the weakness shows up when the join depends on Hindi content.

Feature tested: Cross-Document Synthesis

Result: Passed

Verdict: Good on English cross-document joins and version selection, but the weakness shows up when the join depends on Hindi content.

Expected behavior: 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.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Multi-hop question answered correctly from the English corpus. — ui_M3_multi_hop_BEST.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Multi-hop question answered correctly from the English corpus. — ui_M3_multi_hop_BEST.png

What changed: Text prompt transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Superseded 2025 and current 2026 IRS Publication 15 pair — 05b_contradiction_irs-pub15_2026_CURRENT.pdf

Observed output: Output artifact (Image): Resolved to the current 2026 IRS figures, including the $8,400 Social Security wage-base increase. — ui_C1_contradiction_BEST.png

Input artifact: Input artifact (PDF document): Superseded 2025 and current 2026 IRS Publication 15 pair — 05b_contradiction_irs-pub15_2026_CURRENT.pdf

Output artifact: Output artifact (Image): Resolved to the current 2026 IRS figures, including the $8,400 Social Security wage-base increase. — ui_C1_contradiction_BEST.png

What changed: PDF document transformed into Image

Test case: PDF document → Image

Input type: PDF document

Input used: Input artifact (PDF document): Near-duplicate 2025 and 2026 IRS Publication 15-B editions — 06b_nearduplicate_irs-pub15b_2026.pdf

Observed output: Output artifact (Image): Selected the 2026 edition and the $340 monthly qualified-parking exclusion. — ui_N2_near_duplicate_BEST.png

Input artifact: Input artifact (PDF document): Near-duplicate 2025 and 2026 IRS Publication 15-B editions — 06b_nearduplicate_irs-pub15b_2026.pdf

Output artifact: Output artifact (Image): Selected the 2026 edition and the $340 monthly qualified-parking exclusion. — ui_N2_near_duplicate_BEST.png

What changed: PDF document transformed into Image

Why it matters / Conclusion: English cross-document reasoning is decent; the failure mode shows up when the needed join crosses into Hindi.

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
↓
image
Output artifact for "Cross-Document Synthesis" test: Multi-hop question answered correctly from the English corpus., ui_M3_multi_hop_BEST.png
Multi-hop question answered correctly from the English corpus.
file
05b_contradiction_irs-pub15_2026_CURRENT.pdf
Superseded 2025 and current 2026 IRS Publication 15 pair
↓
image
Output artifact for "Cross-Document Synthesis" test: Resolved to the current 2026 IRS figures, including the $8,400 Social Security wage-base increase., ui_C1_contradiction_BEST.png
Resolved to the current 2026 IRS figures, including the $8,400 Social Security wage-base increase.
file
06b_nearduplicate_irs-pub15b_2026.pdf
Near-duplicate 2025 and 2026 IRS Publication 15-B editions
↓
image
Output artifact for "Cross-Document Synthesis" test: Selected the 2026 edition and the $340 monthly qualified-parking exclusion., ui_N2_near_duplicate_BEST.png
Selected the 2026 edition and the $340 monthly qualified-parking exclusion.
Bottom Line
English cross-document reasoning is decent; the failure mode shows up when the needed join crosses into Hindi.
Multilingual Retrieval
The Hindi document ingests fine, but retrieval from Hindi content does not work in the tested set.
0/4
▾
Test Summary
Feature tested: Multilingual Retrieval
Result: Failed (0/4) — The Hindi document ingests fine, but retrieval from Hindi content does not work in the tested set.

Feature tested: Multilingual Retrieval

Result: Failed (0/4)

Verdict: The Hindi document ingests fine, but retrieval from Hindi content does not work in the tested set.

Expected behavior: 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.

Test case: PDF document → Text prompt

Input type: PDF document

Input used: Input artifact (PDF document): Hindi RBI MPC PDF used for the multilingual retrieval test. — 07_nonenglish_hindi_rbi_mpc.pdf

Observed output: Output artifact (Text prompt): OUTPUT

Input artifact: Input artifact (PDF document): Hindi RBI MPC PDF used for the multilingual retrieval test. — 07_nonenglish_hindi_rbi_mpc.pdf

Output artifact: Output artifact (Text prompt): OUTPUT

What changed: PDF document transformed into Text prompt

Test case: Text prompt → Text prompt

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Text prompt): OUTPUT

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Text prompt): OUTPUT

What changed: Text prompt transformed into Text prompt

Why it matters / Conclusion: Hindi indexing works; Hindi retrieval does not.

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.

file
07_nonenglish_hindi_rbi_mpc.pdf
Hindi RBI MPC PDF used for the multilingual retrieval test.
↓
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
A real connector ecosystem exists, but it is plugin-based and carries onboarding friction.
▾
Test Summary
Feature tested: Connector Sync and Retrieval
Result: Passed — A real connector ecosystem exists, but it is plugin-based and carries onboarding friction.

Feature tested: Connector Sync and Retrieval

Result: Passed

Verdict: A real connector ecosystem exists, but it is plugin-based and carries onboarding friction.

Expected behavior: 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.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The synced Notion page is available in Knowledge with retrieval enabled. — connector_notion_synced_available_BEST.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The synced Notion page is available in Knowledge with retrieval enabled. — connector_notion_synced_available_BEST.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The marketplace shows 25+ connector plugins, but nothing is pre-installed. — connector_marketplace_catalog_BEST.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The marketplace shows 25+ connector plugins, but nothing is pre-installed. — connector_marketplace_catalog_BEST.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Connector support is real, but every source still has plugin-install 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
↓
image
Output artifact for "Connector Sync and Retrieval" test: The synced Notion page is available in Knowledge with retrieval enabled., connector_notion_synced_available_BEST.png
The synced Notion page is available in Knowledge with retrieval enabled.
INPUT
Browse the Data Source marketplace for available connectors
↓
image
Output artifact for "Connector Sync and Retrieval" test: The marketplace shows 25+ connector plugins, but nothing is pre-installed., connector_marketplace_catalog_BEST.png
The marketplace shows 25+ connector plugins, but nothing is pre-installed.
Bottom Line
Connector support is real, but every source still has plugin-install onboarding friction.
Dataset Isolation
No cross-tenant leakage was observed in the isolation test.
▾
Test Summary
Feature tested: Dataset Isolation
Result: Passed — No cross-tenant leakage was observed in the isolation test.

Feature tested: Dataset Isolation

Result: Passed

Verdict: No cross-tenant leakage was observed in the isolation test.

Expected behavior: 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.

Test case: Text prompt → Text/code file

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Text/code file): The retrieval response contains only low-relevance Fed chunks, indicating no leakage from the excluded IRS documents. — tenancy_03_leakage_test_retrieve.txt

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Text/code file): The retrieval response contains only low-relevance Fed chunks, indicating no leakage from the excluded IRS documents. — tenancy_03_leakage_test_retrieve.txt

What changed: Text prompt transformed into Text/code file

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The workspace shows both the scored corpus and the isolation-test dataset. — tenancy_datasets_list_BEST.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The workspace shows both the scored corpus and the isolation-test dataset. — tenancy_datasets_list_BEST.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Isolation held cleanly with no cross-tenant leakage.

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
↓
file
tenancy_03_leakage_test_retrieve.txt
Loading file...
The retrieval response contains only low-relevance Fed chunks, indicating no leakage from the excluded IRS documents.
INPUT
Workspace dataset list showing the separate tenant2 corpus
↓
image
Output artifact for "Dataset Isolation" test: The workspace shows both the scored corpus and the isolation-test dataset., tenancy_datasets_list_BEST.png
The workspace shows both the scored corpus and the isolation-test dataset.
Bottom Line
Isolation held cleanly with no cross-tenant leakage.

There’s Always a Plan for You

Dify Cloud, Sandbox, Community, and Enterprise options

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.

✓ Use This 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 This 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
developer-toolsagent-platformstextOther
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.
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.
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.
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.
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.
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.

Banner Preview

How the embed badge will look on your site

Dify featured on AI Demos

Embed HTML

Copy this code to your website source

<a target="_blank" href="https://aidemos.com/tools/dify?utm_source=dify_embed" style="width: 250px; height: 80px; border-radius:4px;" width="250" height="80"> <img src="https://aidemos-website-images.s3.amazonaws.com/featured.png" alt="Dify | Featured on AI Demos" style="width: 250px; height: 80px; border-radius:4px;" width="250" height="80"> </a>

Quick Integration Guide

  • 1Copy the HTML code block above.
  • 2Paste it into your site's HTML or CMS editor.
  • 3Banner appears instantly on your page.
  • 4Links back to your tool profile here.
Similar Tools

Similar Tools

Discover more AI tools like Dify to enhance your workflow.

🤖
Vectara
Vectara gives you a grounded-answer API with strong refusals, but tables, contradictions, scans, and pricing are the weak spots.
AI Tool
🤖
RAGFlow Cloud
AI Tool
🤖
CustomGPT AI
AI Tool
🤖
Vectorize.io
Managed RAG API with clean ingestion, OCR, and public pricing, but with caveats on refusals and freshness.
AI Tool
🤖
Onyx
AI Tool

Comments (0)

Please Log in to join the discussion.

Built by FutureSmart AI — the team behind AI Demos

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.

Get a custom build

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.

Back to Top