What are the main differences between Dataworkz and Deepset?

Dataworkz and Deepset differ in features, pricing, and target audience. Dataworkz excels in specific use cases while Deepset offers different strengths. Our detailed comparison covers all key differences to help you choose.

Which is better: Dataworkz or Deepset?

The better choice between Dataworkz and Deepset depends on your specific needs. Consider factors like budget, required features, team size, and technical requirements. Our comparison tool helps you evaluate both options side-by-side.

How much does Dataworkz cost compared to Deepset?

Pricing varies between Dataworkz and Deepset. Both offer different pricing tiers and models. Check our detailed pricing comparison to see which fits your budget and requirements better.

Can I switch from Dataworkz to Deepset easily?

Migration between Dataworkz and Deepset depends on your current setup and data. Many tools offer migration assistance or import features. Review our migration guide section for specific steps and considerations.

What integrations do Dataworkz and Deepset support?

Both Dataworkz and Deepset offer various integrations with popular tools and platforms. Our comparison includes a detailed integration matrix to help you ensure compatibility with your existing tech stack.

Dataworkz vs Deepset

Make an informed decision with our comprehensive comparison. Discover which RAG solution perfectly fits your needs.

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Priyansh KhodiyarDevRel at CustomGPT.ai

Fact checked and reviewed by Bill Cava

Published: 01.04.2025Updated: 25.04.2025

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 [MongoDB 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.
Integrations & Channels	API-first: surface agents via REST or GraphQL [MongoDB: API Approach]. 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, Zendesk, Confluence, YouTube, Sharepoint, 100+ more. 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. Hosted CustomGPT.ai offers hosted MCP Server with support for Claude Web, Claude Desktop, Cursor, ChatGPT, Windsurf, Trae, etc. Read more here. Supports OpenAI API Endpoint compatibility. Read more here.
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.	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. API 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.
Integration & Workflow	Typical flow: ingest, set chunking/indexing, test, tweak, repeat [MongoDB: Iterative Setup]. Supports live DB/API hooks so answers stay fresh. Fits nicely into CI/CD—teams can version pipelines and roll out updates automatically.	Embed deeply into enterprise stacks—custom connectors, bespoke endpoints, the works. Schedule ETL jobs and route data conditionally right from the pipeline config. Deployment API	Gets you live fast with a low-code dashboard: create a project, add sources, and auto-index content in minutes. Fits existing systems via API calls, webhooks, and Zapier—handy for automating CRM updates, email triggers, and more. Auto-sync Feature Slides into CI/CD pipelines so your knowledge base updates continuously without manual effort.
Performance & Accuracy	Lets you mix semantic + lexical retrieval or use graph search for sharper context. Threshold tuning helps balance precision vs. recall for your domain. Built to scale—pairs with robust vector DBs and data stores for enterprise loads.	Tune for max accuracy with multi-step retrieval, hybrid search, and custom rerankers. Mix and match components to hit your latency targets—even at large scale. Benchmark insights	Delivers sub-second replies with an optimized pipeline—efficient vector search, smart chunking, and caching. Independent tests rate median answer accuracy at 5/5—outpacing many alternatives. Benchmark Results Always cites sources so users can verify facts on the spot. Maintains speed and accuracy even for massive knowledge bases with tens of millions of words.
Customization & Flexibility ( Behavior & Knowledge)	Supports multi-step reasoning, scenario logic, and tool calls within one agent. Blends structured APIs/DBs with unstructured docs seamlessly. Full control over chunking, metadata, and retrieval algorithms.	Build anything: multi-hop retrieval, custom logic, bespoke prompts—your pipeline, your rules. Create multiple datastores, add role-based filters, or pipe in external APIs as extra tools. Component templates	Lets you add, remove, or tweak content on the fly—automatic re-indexing keeps everything current. Shapes agent behavior through system prompts and sample Q&A, ensuring a consistent voice and focus. Learn How to Update Sources Supports multiple agents per account, so different teams can have their own bots. Balances hands-on control with smart defaults—no deep ML expertise required to get tailored behavior.
Pricing & Scalability	No public tiers—typically custom or usage-based enterprise contracts. Scales to huge data and high concurrency by leveraging your own infra. Ideal for large orgs that need flexible architecture and pricing.	Start free in Deepset Studio, then move to usage-based Enterprise plans as you scale. Deploy in cloud, hybrid, or on-prem setups to handle huge corpora and heavy traffic. Pricing overview	Runs on straightforward subscriptions: Standard (~$99/mo), Premium (~$449/mo), and customizable Enterprise plans. Gives generous limits—Standard covers up to 60 million words per bot, Premium up to 300 million—all at flat monthly rates. View Pricing Handles scaling for you: the managed cloud infra auto-scales with demand, keeping things fast and available.
Security & Privacy	Enterprise-grade security—encryption, compliance, access controls [MongoDB: Enterprise Security]. Data can stay entirely in your environment—bring your own DB, embeddings, etc. Supports single-tenant/VPC hosting for strict isolation if needed.	SOC 2 Type II, ISO 27001, GDPR, HIPAA—you’re covered for enterprise compliance. Choose cloud, VPC, or on-prem to keep data exactly where you need it. Security compliance	Protects data in transit with SSL/TLS and at rest with 256-bit AES encryption. Holds SOC 2 Type II certification and complies with GDPR, so your data stays isolated and private. Security Certifications Offers fine-grained access controls—RBAC, two-factor auth, and SSO integration—so only the right people get in.
Observability & Monitoring	Detailed monitoring for each pipeline stage—chunking, embeddings, queries [MongoDB: Lifecycle Tools]. Step-by-step debugging shows which tools the agent used and why. Hooks into external logging systems and supports A/B tests to fine-tune results.	Deepset Studio dashboard shows latency, error rates, resource use—everything you’d expect. Detailed logs integrate with Prometheus, Splunk, and more for deep observability. Monitoring features	Comes with a real-time analytics dashboard tracking query volumes, token usage, and indexing status. Lets you export logs and metrics via API to plug into third-party monitoring or BI tools. Analytics API Provides detailed insights for troubleshooting and ongoing optimization.
Support & Ecosystem	Geared toward large enterprises with tailored onboarding and solution engineering. Partners with MongoDB and other enterprise tech—tight integrations available [Case Study]. Focuses on direct engineer-to-engineer support over broad public forums.	Lean on the Haystack open-source community (Discord, GitHub) or paid enterprise support. Community insights Wide ecosystem of vector DBs, model providers, and ML tools means plenty of plug-ins and extensions.	Supplies rich docs, tutorials, cookbooks, and FAQs to get you started fast. Developer Docs Offers quick email and in-app chat support—Premium and Enterprise plans add dedicated managers and faster SLAs. Enterprise Solutions Benefits from an active user community plus integrations through Zapier and GitHub resources.
Additional Considerations	Supports graph-optimized retrieval for interlinked docs [MongoDB Reference]. Can act as a central AI orchestration layer—call APIs or trigger actions as part of an answer. Best for teams with LLMOps expertise who want deep customization, not a prefab chatbot. Aims for tailor-made AI agents rather than an out-of-box chat tool.	Perfect for teams that need heavily customized, domain-specific RAG solutions. Full control and future portability—but expect a steeper learning curve and more dev effort. More details	Slashes engineering overhead with an all-in-one RAG platform—no in-house ML team required. Gets you to value quickly: launch a functional AI assistant in minutes. Stays current with ongoing GPT and retrieval improvements, so you’re always on the latest tech. Balances top-tier accuracy with ease of use, perfect for customer-facing or internal knowledge projects.
No- Code Interface & Usability	No-code / low-code builder helps set up pipelines, chunking, and data sources. Exposes technical concepts—knowing embeddings and prompts helps. No end-user UI included; you build the front-end while Dataworkz handles the back-end logic.	Deepset Studio offers low-code drag-and-drop, yet it’s still aimed at developers and ML engineers. Non-tech users may need help, and production UIs will be custom-built.	Offers a wizard-style web dashboard so non-devs can upload content, brand the widget, and monitor performance. Supports drag-and-drop uploads, visual theme editing, and in-browser chatbot testing. User Experience Review Uses role-based access so business users and devs can collaborate smoothly.

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Final Thoughts

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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Priyansh Khodiyar

DevRel at CustomGPT.ai. Passionate about AI and its applications. Here to help you navigate the world of AI tools and make informed decisions for your business.