In this comprehensive guide, we compare Dataworkz and Deepset across various parameters including features, pricing, performance, and customer support to help you make the best decision for your business needs.
Overview
Welcome to the comparison between Dataworkz and Deepset!
Here are some unique insights on Dataworkz:
Dataworkz helps enterprises build agent-style RAG workflows: pull from docs, query live databases, even call APIs in one reasoning chain. A no-code builder simplifies parts of the process, but its depth still assumes some technical chops.
And here's more information on Deepset:
Deepset lets you stitch together RAG pipelines piece by piece: link data sources, choose models, tweak retrieval steps. Developers love the freedom, but casual users may find the learning curve steep.
Enjoy reading and exploring the differences between
Dataworkz and Deepset.
Detailed Feature Comparison
Features
Dataworkz
Deepset
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Brings in a mix of knowledge sources through a point-and-click RAG pipeline builder
[MongoDBÂ Reference].
Lets you wire up SharePoint, Confluence, databases, or document repositories with just a few settings.
Gives fine-grained control over chunk sizes and embedding strategies.
Happy to blend multiple sources—pull docs and hit a live database in the same pipeline.
Gives developers a flexible framework to wire up connectors and process nearly any file type or data source with libraries like Unstructured.
Lets you push content into vector stores such as OpenSearch, Pinecone, Weaviate, or Snowflake—pick the backend that fits best. Learn more
Setup is hands-on, but the payoff is deep, domain-specific customization of your ingestion pipelines.
Lets you ingest more than 1,400 file formats—PDF, DOCX, TXT, Markdown, HTML, and many more—via simple drag-and-drop or API.
Crawls entire sites through sitemaps and URLs, automatically indexing public help-desk articles, FAQs, and docs.
Turns multimedia into text on the fly: YouTube videos, podcasts, and other media are auto-transcribed with built-in OCR and speech-to-text.
View Transcription Guide
Connects to Google Drive, SharePoint, Notion, Confluence, HubSpot, and more through API connectors or Zapier.
See Zapier Connectors
Supports both manual uploads and auto-sync retraining, so your knowledge base always stays up to date.
No prefab chat widget—bring or build your own front-end.
Because it’s pure API, you can drop the AI into any environment that can make HTTP calls.
API-first approach—drop the RAG system into your own app through REST endpoints or the Haystack SDK.
Shareable pipeline prototypes are great for demos, but production channels (Slack bots, web chat, etc.) need a bit of custom code. See prototype feature
Embeds easily—a lightweight script or iframe drops the chat widget into any website or mobile app.
Offers ready-made hooks for Slack, Microsoft Teams, WhatsApp, Telegram, and Facebook Messenger.
Explore API Integrations
Connects with 5,000+ apps via Zapier and webhooks to automate your workflows.
Supports secure deployments with domain allowlisting and a ChatGPT Plugin for private use cases.
Core Chatbot Features
Runs on an agentic architecture for multi-step reasoning and tool use
[Agentic RAG].
Agents decide when to query a knowledge base versus a live DB depending on the question.
Copes with complex flows—fetch structured data, retrieve docs, then blend the answer.
Builds RAG agents as modular pipelines—retriever + reader, plus optional rerankers or multi-step logic.
Multi-turn chat? Source attributions? Fine-grained retrieval tweaks? All possible with the right config. Pipeline overview
Advanced users can layer in tool use and external API calls for richer agent behavior.
Powers retrieval-augmented Q&A with GPT-4 and GPT-3.5 Turbo, keeping answers anchored to your own content.
Reduces hallucinations by grounding replies in your data and adding source citations for transparency.
Benchmark Details
Handles multi-turn, context-aware chats with persistent history and solid conversation management.
Speaks 90+ languages, making global rollouts straightforward.
Includes extras like lead capture (email collection) and smooth handoff to a human when needed.
Customization & Branding
No built-in UI means you own the front-end look and feel 100 %.
Tweak behavior deeply with prompt templates and scenario configs.
Create multiple personas or rule sets for different agent needs—no single-persona limit.
No drag-and-drop theming here—you’ll craft your own front end if you need branded UI.
That also means full freedom to shape the visuals and conversational tone any way you like. Custom components
Fully white-labels the widget—colors, logos, icons, CSS, everything can match your brand.
White-label Options
Provides a no-code dashboard to set welcome messages, bot names, and visual themes.
Lets you shape the AI’s persona and tone using pre-prompts and system instructions.
Uses domain allowlisting to ensure the chatbot appears only on approved sites.
L L M Model Options
Model-agnostic: plug in GPT-4, Claude, open-source models—whatever fits.
You also pick the embedding model, vector DB, and orchestration logic.
More power, a bit more setup—full control over the pipeline.
Model-agnostic: plug in GPT-4, Llama 2, Claude, Cohere, and more—whatever works for you.
Switch models or embeddings through the “Connections” UI with just a few clicks. View supported models
Taps into top models—OpenAI’s GPT-4, GPT-3.5 Turbo, and even Anthropic’s Claude for enterprise needs.
Automatically balances cost and performance by picking the right model for each request.
Model Selection Details
Uses proprietary prompt engineering and retrieval tweaks to return high-quality, citation-backed answers.
Handles all model management behind the scenes—no extra API keys or fine-tuning steps for you.
Developer Experience ( A P I & S D Ks)
No-code builder lets you design pipelines; once ready, hit a single API endpoint to deploy.
No official SDK, but REST/GraphQL integration is straightforward.
Sandbox mode encourages rapid testing and tweaking before production.
Comprehensive REST API plus the open-source Haystack SDK for building, running, and querying pipelines.
Deepset Studio’s visual editor lets you drag-and-drop components, then export YAML for version control. Studio overview
Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat.
APIÂ Documentation
Offers open-source SDKs—like the Python customgpt-client—plus Postman collections to speed integration.
Open-Source SDK
Backs you up with cookbooks, code samples, and step-by-step guides for every skill level.
We hope you found this comparison of Dataworkz vs
Deepset helpful.
Dataworkz is ideal when your AI assistant needs multi-step tasks across several systems. For straightforward Q&A, its sophistication might feel like overkill.
If your team enjoys building from components and wants total control, Deepset is a strong choice. Otherwise, a simpler, managed platform might save time.
Stay tuned for more updates!
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