Dataworkz vs Deepset

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

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Priyansh Khodiyar's avatar
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

When choosing between Dataworkz and Deepset, understanding their unique strengths and architectural differences is crucial for making an informed decision. Both platforms serve the RAG (Retrieval-Augmented Generation) space but cater to different use cases and organizational needs.

Quick Decision Guide

  • Choose Dataworkz if: you value free tier available for testing
  • Choose Deepset if: you value mature open-source framework (since 2020)

About Dataworkz

Dataworkz Landing Page Screenshot

Dataworkz is rag-as-a-service platform for rapid genai development. Dataworkz is a managed RAG platform that enables businesses to build, deploy, and scale GenAI applications using proprietary data with pre-built tools for data discovery, transformation, and monitoring. Founded in 2020, headquartered in Milpitas, CA, the platform has established itself as a reliable solution in the RAG space.

Overall Rating
79/100
Starting Price
Custom

About Deepset

Deepset Landing Page Screenshot

Deepset is open-source framework and enterprise platform for llm orchestration. Deepset is the creator of Haystack, the leading open-source framework for building production-ready LLM applications, and offers an enterprise AI platform for developing and deploying custom AI agents and applications. Founded in 2018, headquartered in Berlin, Germany, the platform has established itself as a reliable solution in the RAG space.

Overall Rating
83/100
Starting Price
Custom

Key Differences at a Glance

In terms of user ratings, both platforms score similarly in overall satisfaction. From a cost perspective, pricing is comparable. The platforms also differ in their primary focus: RAG Platform versus AI Development Platform. These differences make each platform better suited for specific use cases and organizational requirements.

⚠️ What This Comparison Covers

We'll analyze features, pricing, performance benchmarks, security compliance, integration capabilities, and real-world use cases to help you determine which platform best fits your organization's needs. All data is independently verified from official documentation and third-party review platforms.

Detailed Feature Comparison

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Dataworkz
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Deepset
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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-5.1 series, GPT-4 series, and even Anthropic’s Claude for enterprise needs (4.5 opus and sonnet, etc ).
  • 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.
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.
Competitive Positioning
  • Market position: Enterprise agentic RAG platform with point-and-click pipeline builder for organizations needing custom AI orchestration without heavy coding
  • Target customers: Large enterprises with LLMOps expertise, data engineering teams building complex AI agents, and organizations requiring agentic architecture with multi-step reasoning and tool use capabilities
  • Key competitors: Deepset Cloud, LangChain/LangSmith, Haystack, Vectara.ai, and custom-built RAG solutions using MongoDB Atlas Vector Search
  • Competitive advantages: Model-agnostic with full control over LLM/embedding choices, agentic architecture for multi-step reasoning and dynamic tool selection, graph-optimized retrieval for interlinked documents, no-code pipeline builder with sandbox testing, MongoDB partnership for enterprise integrations, and bring-your-own-infrastructure flexibility (DB, embeddings, VPC)
  • Pricing advantage: Custom enterprise contracts with usage-based pricing; no public tiers but typically competitive for organizations with existing infrastructure that want orchestration layer without SaaS lock-in; best value for high-volume, complex use cases
  • Use case fit: Best for enterprises building sophisticated AI agents requiring multi-step reasoning, organizations needing to blend structured APIs/databases with unstructured documents seamlessly, and teams with ML expertise wanting deep customization of chunking, retrieval algorithms, and orchestration logic without building from scratch
  • Market position: Developer-first RAG framework (Haystack) with enterprise cloud offering (Deepset Cloud) for heavily customized, domain-specific RAG solutions
  • Target customers: ML engineers and development teams needing deep RAG customization, enterprises requiring domain-specific solutions with modular pipeline architecture, and organizations wanting future portability with open-source foundation
  • Key competitors: LangChain/LangSmith, Contextual.ai, Dataworkz, Vectara.ai, and custom implementations using Pinecone/Weaviate
  • Competitive advantages: Open-source Haystack framework for full portability, model-agnostic with easy model switching via Connections UI, Deepset Studio visual pipeline editor with YAML export for version control, modular components (retriever, reader, reranker) for maximum flexibility, wide ecosystem of vector DB integrations (OpenSearch, Pinecone, Weaviate, Snowflake), and SOC 2/ISO 27001/GDPR/HIPAA compliance with cloud/VPC/on-prem deployment
  • Pricing advantage: Free Deepset Studio for development, then usage-based Enterprise plans; competitive for teams wanting deep customization without vendor lock-in; best value comes from open-source foundation enabling future migration if needed
  • Use case fit: Perfect for teams needing heavily customized, domain-specific RAG with multi-hop retrieval and custom rerankers, organizations requiring modular pipeline architecture for complex workflows, and ML engineers wanting developer-friendly APIs with future portability through open-source Haystack foundation
  • Market position: Leading all-in-one RAG platform balancing enterprise-grade accuracy with developer-friendly APIs and no-code usability for rapid deployment
  • Target customers: Mid-market to enterprise organizations needing production-ready AI assistants, development teams wanting robust APIs without building RAG infrastructure, and businesses requiring 1,400+ file format support with auto-transcription (YouTube, podcasts)
  • Key competitors: OpenAI Assistants API, Botsonic, Chatbase.co, Azure AI, and custom RAG implementations using LangChain
  • Competitive advantages: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, SOC 2 Type II + GDPR compliance, full white-labeling included, OpenAI API endpoint compatibility, hosted MCP Server support (Claude, Cursor, ChatGPT), generous data limits (60M words Standard, 300M Premium), and flat monthly pricing without per-query charges
  • Pricing advantage: Transparent flat-rate pricing at $99/month (Standard) and $449/month (Premium) with generous included limits; no hidden costs for API access, branding removal, or basic features; best value for teams needing both no-code dashboard and developer APIs in one platform
  • Use case fit: Ideal for businesses needing both rapid no-code deployment and robust API capabilities, organizations handling diverse content types (1,400+ formats, multimedia transcription), teams requiring white-label chatbots with source citations for customer-facing or internal knowledge projects, and companies wanting all-in-one RAG without managing ML infrastructure
A I Models
  • Model-agnostic architecture: Supports GPT-4, Claude, Llama, and other open-source models - full flexibility in LLM selection
  • Public LLM APIs: Integration with AWS Bedrock and OpenAI APIs for managed model access
  • Private hosting: Option to host open-source foundation models in your own VPC for data sovereignty and cost control
  • Composable AI stack: Choose your own embedding model, vector database, chunking strategy, and LLM independently
  • No vendor lock-in: Flexibility to switch models based on performance, cost, or compliance requirements without platform migration
  • Model-agnostic architecture: Supports GPT-4, GPT-3.5, Claude (Anthropic), Llama 2, Cohere, and 80+ model providers through unified interface
  • Easy model switching: Change models via Connections UI with just a few clicks without code changes
  • Embedding models: OpenAI, Cohere, Sentence Transformers, and custom embedding models supported
  • Multiple LLMs per pipeline: Use different models for different pipeline components (retrieval vs generation)
  • Custom model fine-tuning: Fine-tune on proprietary data for domain-specific terminology and accuracy
  • Baseline models available: Pre-configured with common models for quick prototyping
  • Primary models: GPT-5.1 and 4 series from OpenAI, and Anthropic's Claude 4.5 (opus and sonnet) for enterprise needs
  • Automatic model selection: Balances cost and performance by automatically selecting the appropriate model for each request Model Selection Details
  • Proprietary optimizations: Custom prompt engineering and retrieval enhancements for high-quality, citation-backed answers
  • Managed infrastructure: All model management handled behind the scenes - no API keys or fine-tuning required from users
  • Anti-hallucination technology: Advanced mechanisms ensure chatbot only answers based on provided content, improving trust and factual accuracy
R A G Capabilities
  • Advanced RAG pipeline: Point-and-click builder for configuring and optimizing each aspect of RAG with fine-grained control RAG-as-a-Service
  • Agentic architecture: LLM-powered agents that reason through multi-step tasks, call external tools/APIs, and adapt based on context Agentic RAG
  • Hybrid retrieval: Mix semantic and lexical retrieval, or use graph search for sharper context and improved accuracy
  • Hallucination mitigation: RAG references source data to reduce hallucinations and improve factual accuracy
  • Graph-optimized retrieval: Specialized for interlinked documents with relationship-aware context Graph Capabilities
  • Threshold tuning: Balance precision vs. recall for domain-specific requirements
  • Dynamic tool selection: Agents decide when to query knowledge bases vs. live databases vs. external APIs based on question context
  • Advanced RAG architecture: Multi-step retrieval, hybrid search (semantic + keyword), and custom rerankers for maximum accuracy
  • Modular pipeline design: Flexible retriever + reader + optional reranker components for customized workflows
  • Multi-hop retrieval: Chain multiple retrieval steps for complex queries requiring deep context
  • Vector database flexibility: OpenSearch, Pinecone, Weaviate, Snowflake, Qdrant, and more - choose your preferred backend
  • Benchmark-proven performance: Published performance metrics on MTEB and domain-specific benchmarks
  • Source attribution: Full citation tracking with document references and confidence scores
  • Haystack framework: Open-source foundation enables complete RAG customization and future portability
  • Core architecture: GPT-4 combined with Retrieval-Augmented Generation (RAG) technology, outperforming OpenAI in RAG benchmarks RAG Performance
  • Anti-hallucination technology: Advanced mechanisms reduce hallucinations and ensure responses are grounded in provided content Benchmark Details
  • Automatic citations: Each response includes clickable citations pointing to original source documents for transparency and verification
  • Optimized pipeline: Efficient vector search, smart chunking, and caching for sub-second reply times
  • Scalability: Maintains speed and accuracy for massive knowledge bases with tens of millions of words
  • Context-aware conversations: Multi-turn conversations with persistent history and comprehensive conversation management
  • Source verification: Always cites sources so users can verify facts on the spot
Use Cases
  • Retail and e-commerce: Product recommendations, inventory queries, customer service with agentic RAG blending structured data (inventory) and unstructured content (product guides) Retail Case Study
  • Banking and financial services: Regulatory compliance queries, customer onboarding, risk assessment with enterprise-grade security and auditability
  • Healthcare: Clinical decision support, patient information systems, medical knowledge bases with HIPAA-compliant deployment options
  • Enterprise knowledge management: Internal documentation, policy queries, onboarding assistance with multi-source data integration (SharePoint, Confluence, databases)
  • Customer support: Multi-step troubleshooting, ticket routing, automated responses with tool calling and API integration
  • Research and analytics: Document analysis, research assistance, data exploration with graph-optimized retrieval for interlinked content
  • Manufacturing: Equipment manuals, maintenance procedures, supply chain queries with structured and unstructured data blending
  • Legal and compliance: Contract analysis, regulatory research, compliance checking with audit trails and traceability
  • Domain-specific Q&A: Enterprise knowledge bases with specialized terminology requiring fine-tuned models
  • Research and analysis: Multi-hop retrieval for complex research questions across large document corpora
  • Technical documentation: Developer-focused RAG for code documentation, API references, and technical guides
  • Compliance and legal: HIPAA/GDPR-compliant RAG systems for regulated industries requiring on-prem deployment
  • Custom AI agents: Build specialized agents with external API calls, tool use, and multi-step reasoning
  • Enterprise search: Large-scale search across millions of documents with hybrid retrieval and reranking
  • Future-proof AI: Migrate between LLM providers, vector databases, and hosting options without vendor lock-in
  • Customer support automation: AI assistants handling common queries, reducing support ticket volume, providing 24/7 instant responses with source citations
  • Internal knowledge management: Employee self-service for HR policies, technical documentation, onboarding materials, company procedures across 1,400+ file formats
  • Sales enablement: Product information chatbots, lead qualification, customer education with white-labeled widgets on websites and apps
  • Documentation assistance: Technical docs, help centers, FAQs with automatic website crawling and sitemap indexing
  • Educational platforms: Course materials, research assistance, student support with multimedia content (YouTube transcriptions, podcasts)
  • Healthcare information: Patient education, medical knowledge bases (SOC 2 Type II compliant for sensitive data)
  • Financial services: Product guides, compliance documentation, customer education with GDPR compliance
  • E-commerce: Product recommendations, order assistance, customer inquiries with API integration to 5,000+ apps via Zapier
  • SaaS onboarding: User guides, feature explanations, troubleshooting with multi-agent support for different teams
Security & Compliance
  • Enterprise-grade security: Encryption, compliance, and access controls built for large organizations Security Features
  • Audit and traceability: Every interaction, tool invocation, and data access can be audited and traced for compliance and transparency
  • Data sovereignty: Bring-your-own-infrastructure deployment options - keep data entirely in your environment (databases, embeddings, VPC)
  • Single-tenant hosting: VPC deployment for strict isolation and compliance with regulatory requirements
  • Access controls: Role-based access control and fine-grained permissions for multi-team environments
  • Compliance readiness: Architecture supports GDPR, HIPAA, SOC 2, and other regulatory frameworks through flexible deployment models
  • SOC 2 Type II certification: Annual audits ensuring enterprise security standards
  • ISO 27001 certification: International information security management compliance
  • GDPR compliance: European data protection regulation adherence with data sovereignty options
  • HIPAA compliance: Healthcare data protection standards for sensitive medical information
  • Flexible deployment: Cloud, hybrid, VPC, or on-premises deployment for complete data control
  • Data residency options: Choose where data is stored and processed (US, EU, on-prem)
  • No model training on customer data: Customer data never used to train third-party models
  • Audit trails: Comprehensive logging of all queries, retrievals, and system access
  • Encryption: SSL/TLS for data in transit, 256-bit AES encryption for data at rest
  • SOC 2 Type II certification: Industry-leading security standards with regular third-party audits Security Certifications
  • GDPR compliance: Full compliance with European data protection regulations, ensuring data privacy and user rights
  • Access controls: Role-based access control (RBAC), two-factor authentication (2FA), SSO integration for enterprise security
  • Data isolation: Customer data stays isolated and private - platform never trains on user data
  • Domain allowlisting: Ensures chatbot appears only on approved sites for security and brand protection
  • Secure deployments: ChatGPT Plugin support for private use cases with controlled access
Pricing & Plans
  • Enterprise contracts: Custom pricing tailored to organization size, usage volume, and deployment requirements - no public tiers
  • Credit-based pricing: Credits debited when functions are performed on data (transformations, logic), with 2M rows moved per credit for data movement
  • Usage-based model: Pay for what you use - ideal for variable workloads and avoiding over-provisioning
  • AWS Marketplace: Available for procurement through AWS Marketplace for streamlined enterprise purchasing AWS Marketplace
  • Bring-your-own-infrastructure: Leverage existing cloud infrastructure (databases, vector stores) to reduce platform costs
  • Scalability: Pricing scales with usage - cost-effective for high-volume, complex use cases where control matters
  • Deepset Studio (Free): Development environment with unlimited files and core features for prototyping
  • Enterprise pricing: Custom usage-based pricing based on queries, documents indexed, and compute resources
  • Deployment options pricing: Cloud (managed SaaS), hybrid, or on-premises with separate pricing tiers
  • No per-seat charges: Usage-based model scales with actual platform usage, not team size
  • Professional services: Optional consulting, integration support, and custom pipeline development available
  • Scaling flexibility: Enterprise plans handle huge corpora (millions of documents) and heavy traffic loads
  • Open-source advantage: Haystack framework free forever - only pay for managed cloud services if needed
  • Standard Plan: $99/month or $89/month annual - 10 custom chatbots, 5,000 items per chatbot, 60 million words per bot, basic helpdesk support, standard security View Pricing
  • Premium Plan: $499/month or $449/month annual - 100 custom chatbots, 20,000 items per chatbot, 300 million words per bot, advanced support, enhanced security, additional customization
  • Enterprise Plan: Custom pricing - Comprehensive AI solutions, highest security and compliance, dedicated account managers, custom SSO, token authentication, priority support with faster SLAs Enterprise Solutions
  • 7-Day Free Trial: Full access to Standard features without charges - available to all users
  • Annual billing discount: Save 10% by paying upfront annually ($89/mo Standard, $449/mo Premium)
  • Flat monthly rates: No per-query charges, no hidden costs for API access or white-labeling (included in all plans)
  • Managed infrastructure: Auto-scaling cloud infrastructure included - no additional hosting or scaling fees
Support & Documentation
  • Enterprise onboarding: Tailored onboarding and solution engineering for large organizations with complex requirements
  • Direct engineering support: Engineer-to-engineer support focused on technical implementation and optimization
  • Product documentation: Comprehensive docs covering platform setup, pipeline configuration, and agentic workflows Product Docs
  • MongoDB partnership: Tight integrations and joint support with MongoDB for Atlas Vector Search and enterprise deployments Partnership Details
  • Solution engineering: Dedicated resources for architecture design, pipeline optimization, and production deployment
  • Limited public resources: Focus on direct customer support over public forums and community-driven knowledge bases
  • Haystack community: Active Discord server and GitHub community (14,000+ stars) with responsive maintainers
  • Enterprise support tiers: Email, Slack Connect channels, and dedicated support engineers for paid customers
  • Comprehensive documentation: docs.cloud.deepset.ai with tutorials, API references, and integration guides
  • Video tutorials: YouTube channel with pipeline building guides and best practices
  • GitHub examples: Open-source example projects and starter templates for common use cases
  • Integration ecosystem: Wide community of vector DB providers, model vendors, and tool developers
  • Professional services: Custom development, architecture consulting, and hands-on implementation support available
  • Documentation hub: Rich docs, tutorials, cookbooks, FAQs, API references for rapid onboarding Developer Docs
  • Email and in-app support: Quick support via email and in-app chat for all users
  • Premium support: Premium and Enterprise plans include dedicated account managers and faster SLAs
  • Code samples: Cookbooks, step-by-step guides, and examples for every skill level API Documentation
  • Open-source resources: Python SDK (customgpt-client), Postman collections, GitHub integrations Open-Source SDK
  • Active community: User community plus 5,000+ app integrations through Zapier ecosystem
  • Regular updates: Platform stays current with ongoing GPT and retrieval improvements automatically
Limitations & Considerations
  • No built-in UI: Platform is API-first with no prefab chat widget - you must build or bring your own front-end interface
  • Technical expertise required: Best for teams with LLMOps expertise who understand embeddings, prompts, and RAG architecture - not ideal for non-technical users
  • Custom pricing only: No transparent public pricing tiers - requires sales engagement for pricing quotes and contracts
  • Enterprise focus: Designed for large organizations - may be overkill for small teams or simple chatbot use cases
  • Setup complexity: Point-and-click builder simplifies pipeline creation but still requires understanding of RAG concepts and architecture
  • Limited pre-built templates: Platform provides flexibility but fewer out-of-box solutions compared to turnkey chatbot platforms
  • No official SDK: REST/GraphQL integration is straightforward but lacks dedicated client libraries for popular languages
  • Infrastructure requirements: Bring-your-own-infrastructure model requires existing cloud infrastructure and data engineering capabilities
  • Steeper learning curve: Developer-first platform requires ML/engineering skills - not ideal for non-technical users
  • Custom UI required: No drag-and-drop chat widget - must build production interfaces from scratch
  • Hands-on setup: More initial configuration effort compared to plug-and-play SaaS platforms
  • Deepset Studio limitations: Visual editor still aimed at technical users - requires understanding of RAG concepts
  • Production readiness: Moving from Studio prototype to production deployment requires additional DevOps work
  • Enterprise costs: Usage-based pricing can become expensive at high query volumes without careful optimization
  • Best for technical teams: Maximum value requires ML engineers and developers - not suited for business users seeking no-code solutions
  • Integration effort: Native integrations like Slack bots require custom code vs turnkey options from competitors
  • Managed service approach: Less control over underlying RAG pipeline configuration compared to build-your-own solutions like LangChain
  • Vendor lock-in: Proprietary platform - migration to alternative RAG solutions requires rebuilding knowledge bases
  • Model selection: Limited to OpenAI (GPT-5.1 and 4 series) and Anthropic (Claude, opus and sonnet 4.5) - no support for other LLM providers (Cohere, AI21, open-source models)
  • Pricing at scale: Flat-rate pricing may become expensive for very high-volume use cases (millions of queries/month) compared to pay-per-use models
  • Customization limits: While highly configurable, some advanced RAG techniques (custom reranking, hybrid search strategies) may not be exposed
  • Language support: Supports 90+ languages but performance may vary for less common languages or specialized domains
  • Real-time data: Knowledge bases require re-indexing for updates - not ideal for real-time data requirements (stock prices, live inventory)
  • Enterprise features: Some advanced features (custom SSO, token authentication) only available on Enterprise plan with custom pricing
Core Agent Features
  • Agentic RAG Architecture: LLM-powered agents that reason through multi-step tasks, call external tools/APIs, and adapt based on context - built for autonomous operation Agentic Capabilities
  • Agent Memory System: Derived from three key artifacts - conversational history, user preferences, and business context from external sources via RAG pipelines and enterprise knowledge graphs
  • Complex Task Execution: Reasoning capabilities decompose complex tasks into multiple interdependent sub-tasks represented as directed acyclic graphs (DAGs) for parallel execution where possible Multi-Step Reasoning
  • LLM Compiler Integration: Identifies optimal sequence for executing sub-tasks with parallel execution when dependencies allow - implements advanced task orchestration patterns
  • Dynamic Tool Selection: Agents decide when to query knowledge bases versus live databases versus external APIs based on question context and system state
  • External API Integration: Invoke external APIs to create CRM leads, create support tickets, lookup order details, or trigger actions as part of generating answers Agent Builder
  • Continuous Learning & Adaptation: Agent frameworks support continuous learning and context switching across workflows - agents not only retrieve and generate but also plan multi-step tasks and adapt over time
  • Agent Builder Interface: Easy-to-use interface to assemble Agentic RAG Applications with minimal technical knowledge - takes business requirements and generates agent definitions
  • AI Agents with Haystack: Build LLM-powered autonomous agents that can reason, reflect, and act using tools, data, and critical introspection into their own decision-making processes Building Agents
  • Spectrum Approach: Combines structured workflows with autonomous capabilities - AI systems exist on a spectrum between linearity and autonomy based on decision-making capability needs Agentic Spectrum
  • Planning Mechanisms: Agents break tasks into steps using chain-of-thought or tree-of-thought planning, enabling complex multi-step reasoning and execution
  • Dynamic Routing: LLMs serve as "brains" of decision systems, using reasoning capabilities to evaluate and choose among multiple tools, courses of action, databases, and resources based on context and goals
  • Reflection & Self-Correction: Agents analyze intermediate results through reflection mechanisms, improving accuracy and adapting strategies based on outcomes
  • Tool Integration: Modular pipeline design allows agents to use retriever, reader, reranker components, external API calls, and custom tools for richer autonomous behavior
  • Agentic RAG Enhancement: Build agentic RAG pipelines in Deepset Studio that combine graphs, agentic properties, multimodal capabilities, and innovations to significantly reduce inaccurate or misleading information Agentic RAG Guide
  • Custom Workflows: Create anything from multi-hop retrieval to custom logic to bespoke prompts - modular components enable building specialized agents for domain-specific autonomous workflows
  • Custom AI Agents: Build autonomous agents powered by GPT-4 and Claude that can perform tasks independently and make real-time decisions based on business knowledge
  • Decision-Support Capabilities: AI agents analyze proprietary data to provide insights, recommendations, and actionable responses specific to your business domain
  • Multi-Agent Systems: Deploy multiple specialized AI agents that can collaborate and optimize workflows in areas like customer support, sales, and internal knowledge management
  • Memory & Context Management: Agents maintain conversation history and persistent context for coherent multi-turn interactions View Agent Documentation
  • Tool Integration: Agents can trigger actions, integrate with external APIs via webhooks, and connect to 5,000+ apps through Zapier for automated workflows
  • Hyper-Accurate Responses: Leverages advanced RAG technology and retrieval mechanisms to deliver context-aware, citation-backed responses grounded in your knowledge base
  • Continuous Learning: Agents improve over time through automatic re-indexing of knowledge sources and integration of new data without manual retraining
R A G-as-a- Service Assessment
  • Platform Type: TRUE RAG-AS-A-SERVICE PLATFORM - enterprise agentic RAG orchestration layer designed for custom AI agent development with point-and-click pipeline builder
  • Core Architecture: Model-agnostic RAG infrastructure with full control over LLM selection, embedding models, vector databases, and chunking strategies - composable AI stack approach
  • Agentic Focus: Built around LLM-powered autonomous agents that reason through multi-step tasks, call external tools/APIs, and adapt based on user interactions - not simple Q&A chatbots Agentic RAG
  • Developer Experience: Point-and-click pipeline builder with sandbox testing, REST/GraphQL API integration, and agent builder for minimal-code assembly - targets LLMOps-savvy teams
  • No-Code Capabilities: Agent Builder interface and pipeline configuration UI reduce coding requirements, but platform still assumes technical knowledge of RAG concepts and architectures
  • Target Market: Large enterprises with data engineering teams building sophisticated AI agents, organizations requiring agentic architecture with multi-step reasoning, and teams wanting deep customization without building RAG from scratch
  • RAG Technology Differentiation: Graph-optimized retrieval for interlinked documents, hybrid retrieval (semantic + lexical), threshold tuning for precision/recall balance, and agentic task decomposition via DAG execution Graph Capabilities
  • Deployment Flexibility: Bring-your-own-infrastructure model with MongoDB partnership - deploy on your cloud/VPC with full data sovereignty and infrastructure control
  • Enterprise Readiness: Enterprise-grade security and scalability, audit trails for every interaction, data sovereignty options, and custom enterprise contracts with usage-based pricing Enterprise Security
  • Use Case Fit: Best for enterprises building sophisticated AI agents requiring multi-step reasoning, organizations needing to blend structured APIs/databases with unstructured documents seamlessly, and teams with ML expertise wanting deep RAG customization
  • NOT Suitable For: Non-technical teams seeking turnkey chatbots, organizations without existing infrastructure, small businesses needing simple Q&A bots, or teams wanting pre-built UI widgets
  • Competitive Positioning: Competes with Deepset Cloud, LangChain/LangSmith, and custom RAG builds - differentiates through agentic architecture, no-code pipeline builder, and MongoDB partnership for enterprise scalability
  • Platform Type: HYBRID RAG FRAMEWORK + CLOUD SERVICE - open-source Haystack foundation with enterprise Deepset Cloud offering for heavily customized, domain-specific RAG solutions
  • Core Architecture: Modular pipeline architecture with retriever + reader + optional reranker components, full control over embedding models, vector databases (OpenSearch, Pinecone, Weaviate, Snowflake), and chunking strategies
  • Agentic Capabilities: Build autonomous AI agents with planning, routing, reflection mechanisms using Haystack framework - supports agentic RAG pipelines with graphs and multimodal capabilities Agent Development
  • Developer Experience: Comprehensive REST API, open-source Haystack SDK, Deepset Studio visual pipeline editor with YAML export for version control - targets ML engineers and development teams Studio Overview
  • No-Code Capabilities: Deepset Studio offers drag-and-drop visual editor for pipeline building, but still aimed at developers and ML engineers - not accessible to non-technical users
  • Target Market: ML engineers and development teams needing deep RAG customization, enterprises requiring domain-specific solutions with modular pipeline architecture, organizations wanting future portability with open-source foundation
  • RAG Technology Leadership: Advanced RAG with multi-step retrieval, hybrid search (semantic + keyword), custom rerankers for maximum accuracy, model-agnostic support (GPT-4, Llama 2, Claude, Cohere, 80+ providers), and benchmark-proven performance on MTEB Benchmark Insights
  • Deployment Flexibility: Free Deepset Studio for development, usage-based Enterprise plans, cloud/VPC/on-prem deployment options, and SOC 2/ISO 27001/GDPR/HIPAA compliance with flexible data residency
  • Enterprise Readiness: SOC 2 Type II, ISO 27001, GDPR, HIPAA compliance, cloud/hybrid/on-prem deployment, no model training on customer data, and comprehensive audit trails
  • Use Case Fit: Perfect for teams needing heavily customized domain-specific RAG with multi-hop retrieval and custom rerankers, organizations requiring modular pipeline architecture for complex workflows, ML engineers wanting developer-friendly APIs with future portability
  • Open-Source Advantage: Haystack framework (14,000+ GitHub stars) free forever with full portability - only pay for managed Deepset Cloud services if needed, avoiding vendor lock-in
  • NOT Suitable For: Non-technical teams seeking turnkey chatbots, business users wanting no-code deployment, organizations needing pre-built chat widgets or Slack/WhatsApp integrations
  • Competitive Positioning: Competes with LangChain/LangSmith, Contextual.ai, Dataworkz - differentiates through open-source Haystack foundation, model-agnostic flexibility, visual pipeline editor, and wide vector DB ecosystem
  • Platform Type: TRUE RAG-AS-A-SERVICE PLATFORM - all-in-one managed solution combining developer APIs with no-code deployment capabilities
  • Core Architecture: Serverless RAG infrastructure with automatic embedding generation, vector search optimization, and LLM orchestration fully managed behind API endpoints
  • API-First Design: Comprehensive REST API with well-documented endpoints for creating agents, managing projects, ingesting data (1,400+ formats), and querying chat API Documentation
  • Developer Experience: Open-source Python SDK (customgpt-client), Postman collections, OpenAI API endpoint compatibility, and extensive cookbooks for rapid integration
  • No-Code Alternative: Wizard-style web dashboard enables non-developers to upload content, brand widgets, and deploy chatbots without touching code
  • Hybrid Target Market: Serves both developer teams wanting robust APIs AND business users seeking no-code RAG deployment - unique positioning vs pure API platforms (Cohere) or pure no-code tools (Jotform)
  • RAG Technology Leadership: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, proprietary anti-hallucination mechanisms, and citation-backed responses Benchmark Details
  • Deployment Flexibility: Cloud-hosted SaaS with auto-scaling, API integrations, embedded chat widgets, ChatGPT Plugin support, and hosted MCP Server for Claude/Cursor/ChatGPT
  • Enterprise Readiness: SOC 2 Type II + GDPR compliance, full white-labeling, domain allowlisting, RBAC with 2FA/SSO, and flat-rate pricing without per-query charges
  • Use Case Fit: Ideal for organizations needing both rapid no-code deployment AND robust API capabilities, teams handling diverse content types (1,400+ formats, multimedia transcription), and businesses requiring production-ready RAG without building ML infrastructure from scratch
  • Competitive Positioning: Bridges the gap between developer-first platforms (Cohere, Deepset) requiring heavy coding and no-code chatbot builders (Jotform, Kommunicate) lacking API depth - offers best of both worlds

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Final Thoughts

Final Verdict: Dataworkz vs Deepset

After analyzing features, pricing, performance, and user feedback, both Dataworkz and Deepset are capable platforms that serve different market segments and use cases effectively.

When to Choose Dataworkz

  • You value free tier available for testing
  • No-code approach simplifies development
  • Flexible LLM and vector database choices

Best For: Free tier available for testing

When to Choose Deepset

  • You value mature open-source framework (since 2020)
  • Production-ready from day one
  • Highly modular and customizable

Best For: Mature open-source framework (since 2020)

Migration & Switching Considerations

Switching between Dataworkz and Deepset requires careful planning. Consider data export capabilities, API compatibility, and integration complexity. Both platforms offer migration support, but expect 2-4 weeks for complete transition including testing and team training.

Pricing Comparison Summary

Dataworkz starts at custom pricing, while Deepset begins at custom pricing. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.

Our Recommendation Process

  1. Start with a free trial - Both platforms offer trial periods to test with your actual data
  2. Define success metrics - Response accuracy, latency, user satisfaction, cost per query
  3. Test with real use cases - Don't rely on generic demos; use your production data
  4. Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
  5. Check vendor stability - Review roadmap transparency, update frequency, and support quality

For most organizations, the decision between Dataworkz and Deepset comes down to specific requirements rather than overall superiority. Evaluate both platforms with your actual data during trial periods, focusing on accuracy, latency, ease of integration, and total cost of ownership.

📚 Next Steps

Ready to make your decision? We recommend starting with a hands-on evaluation of both platforms using your specific use case and data.

  • • Review: Check the detailed feature comparison table above
  • • Test: Sign up for free trials and test with real queries
  • • Calculate: Estimate your monthly costs based on expected usage
  • • Decide: Choose the platform that best aligns with your requirements

Last updated: December 14, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.

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Priyansh Khodiyar's avatar

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.

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