In this comprehensive guide, we compare Dataworkz and Glean 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 Glean, 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 Glean if: you value permissions-aware ai is genuinely differentiated - real-time enforcement across 100+ datasources addresses critical enterprise concern
About Dataworkz
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 Glean
Glean is enterprise work ai with permissions-aware rag across 100+ apps. Glean is a premium enterprise RAG platform with permissions-aware AI as its core differentiator. Founded by ex-Google Search engineers, Glean achieved $100M ARR in three years and a $7.2B valuation (2025). It connects 100+ enterprise apps with real-time access controls, supports 15+ LLMs, and offers comprehensive APIs with 4-language SDKs. Trade-offs: enterprise-only sales (~$50/user/month, ~$60K minimum), no consumer messaging channels, and premium positioning over plug-and-play simplicity. Founded in 2019, headquartered in Palo Alto, CA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
96/100
Starting Price
$50/mo
Key Differences at a Glance
In terms of user ratings, Glean in overall satisfaction. From a cost perspective, Dataworkz starts at a lower price point. The platforms also differ in their primary focus: RAG Platform versus Enterprise RAG. 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
Dataworkz
Glean
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.
100+ native connectors covering major enterprise categories
Cloud Storage: Google Drive, SharePoint, OneDrive, Dropbox, Box
Communication: Slack, Microsoft Teams, Gmail, Outlook, Zoom
Indexing API: 10 requests/second for bulk operations, ProcessAll limited to once per 3 hours
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.
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.
Glean Chat interface: Primary interface for interacting with Glean Assistant offering familiar chat-like experience enabling natural conversations with company knowledge base
Multi-turn conversations: Supports conversational AI with natural language and context awareness maintaining context across conversation turns
Streaming responses: Real-time response streaming for better user experience with automatic source citations for transparency
Chatbot context understanding: Understands thread and sequence of conversations tracking references like "their" and "they" across multiple exchanges
Enterprise knowledge integration: Works across all company apps and knowledge sources including Microsoft 365, Google Workspace, Salesforce, Jira, GitHub and nearly 100 more applications
Personalization and security: Delivers answers highly customized to each user based on deep understanding of company content, employees, and activity while adhering to real-time enterprise data permissions and governance rules
Citation and transparency: Provides full linking to source information across documents, conversations and applications for transparency and trust
Simple chatbot API: Powerful tool for integrating conversational AI into products creating custom conversational interfaces leveraging Glean's AI capabilities
Use case flexibility: Build chatbots answering customer questions using help documentation, FAQs, knowledge bases or create internal tools helping employees find company policies, procedures, documentation
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.
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.
Standard support: 24x5 (Mon-Fri) via portal, email, Slack Connect
Premium support: 24x7 (critical only) with additional fee
Dedicated CSMs: Enterprise accounts with hands-on onboarding
Documentation: Excellent at developers.glean.com
GitHub repositories: SDK examples and sample projects
Regular business reviews for enterprise customers
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.
Cannot create content directly: Glean focuses purely on search and retrieval - not suitable for organizations needing content creation within platform
Platform designed for large organizations: Feature set and pricing optimized for large enterprises - smaller teams may find it overkill and less cost-effective
AI production challenges: 68% of organizations report moving only 30% or fewer AI experiments into full production highlighting persistent scaling difficulties beyond proof-of-concept
Integration complexity: Requires strategic overhaul of processes to ensure seamless technology incorporation into existing workflows
Change management: Overcoming resistance to change demands strong leadership and commitment to fostering innovation and adaptability environment
Data reliability monitoring: Potential for inaccuracies in AI outputs necessitates rigorous monitoring frameworks to ensure data reliability and trustworthiness
Cybersecurity concerns: As AI deployment expands, cybersecurity threats become more pronounced requiring enhanced protective measures for sensitive information
Bias in AI models: Models can inadvertently learn and replicate biases in training data leading to unfair or discriminatory outcomes particularly in hiring, customer service, legal decisions
Training investment required: Enterprises must invest in training workforce to effectively use AI tools - upskilling employees, hiring AI talent, or partnering with consultants
Security risks and shadow IT: Many organizations hesitate due to uncertainties from security risks and shadow IT - ad hoc generative AI adoption comes with heavy risks and costs
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.
Natural language agent configuration: Describe goals in plain language
Visual builder: Drag-and-drop workflow creation
AI-assisted creation: Glean suggests starting points and auto-generates draft agents
Agent Library: Pre-built templates for common use cases
30+ prebuilt agents: Sales, engineering, IT, HR use cases
RBAC hierarchy: Setup Admin, Admin, Super Admin with granular permissions
4.8/5 ease of use rating on G2
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
vs CustomGPT: Enterprise-premium vs developer-friendly; permissions-aware AI vs flexible customization
vs Zendesk: Enterprise search + RAG vs customer service platform
Unique strength: Real-time permissions-aware AI across 100+ datasources (no competitor matches this)
Target audience: Large enterprises (1K-100K users) with complex permission hierarchies
Pricing barrier: ~$50/user/month with ~$60K minimum excludes SMBs
Enterprise focus: Security, governance, compliance over plug-and-play simplicity
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 Hub supports 15+ LLMs across multiple hosting providers with per-step model selection
OpenAI: GPT-3.5, GPT-4 via OpenAI or Azure OpenAI endpoints
Google Vertex AI: Gemini 1.5 Pro with multimodal capabilities
Amazon Bedrock: Claude 3 Sonnet for high-accuracy enterprise use cases
Temperature controls: Factual, balanced, or creative output settings per workflow
Model tiers: Basic, Standard, Premium (premium consumes FlexCredits on Enterprise Flex plan)
Two access options: Glean Universal Key (managed) or Customer Key (BYOK) for data sovereignty
Zero data retention: Customer data never used for model training with automatic model updates
Automatic routing: Optimizes using best-in-class models per query type for accuracy and cost
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
Hybrid search: Combines semantic (vector-based) and lexical (keyword) approaches for maximum accuracy
Knowledge Graph Framework: Proprietary anchors and signals across enterprise data with rich, scalable crawler
LLM Control Layer: Optimizes and controls LLM outputs with permission-safe document retrieval and ranking
Real-time permissions enforcement: Users only see authorized content with identity crawling and connector-level permission mirroring
Context-aware query rewriting: LLM determines optimal query set with enterprise-specific rewrites
Hallucination prevention: RAG grounding, permission-aware retrieval, citation/source attribution for every answer
74% human-agreement rate on AI Evaluator benchmarks with 25% precision increases in customer case studies
141% ROI over 3 years: $15.6M NPV for composite organizations, 110 hours saved per employee annually (Forrester)
Permissions-aware AI (unique): Real-time access control enforcement across all 100+ datasources - no competitor matches this capability
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
Enterprise knowledge retrieval: Unified search across 100+ datasources (Google Drive, SharePoint, Confluence, Salesforce, Zendesk, GitHub, Slack) for 10K-100K user organizations
Permissions-aware search: Complex permission hierarchies requiring real-time enforcement - healthcare, finance, legal industries with sensitive data access controls
AI agents and automation: 30+ prebuilt agents for sales, engineering, IT, HR use cases with workflow automation capabilities
Developer-friendly RAG: Official SDKs (Python, Java, Go, TypeScript), LangChain integration, MCP Server for Claude Desktop/Cursor/VS Code
Active Data Governance: Continuous scanning with 100+ predefined infotypes (PII, PCI, M&A) and customizable policies with auto-hide
Cloud-Prem deployment: Customer-hosted in AWS or GCP for regulated industries requiring full data residency control
NOT suitable for: SMBs with <100 users or <$60K budgets, simple document Q&A without permission requirements, consumer messaging channels (WhatsApp, Telegram)
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)
FlexCredits (Enterprise Flex): For premium LLM usage with consumption-based billing
Support tiers: Standard (24x5, included) or Premium (24x7 critical, additional fee)
Dedicated CSMs: Assigned to enterprise accounts with regular business reviews and hands-on onboarding
Pricing barrier: Excludes SMBs and startups - targets Fortune 500 and mid-market enterprises with 1K-100K users
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
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
High cost barrier: ~$50/user/month with ~$60K minimum ACV excludes SMBs, startups, and budget-conscious teams
No public pricing: Requires sales contact creating friction for evaluation and budget planning vs transparent competitors
Paid POCs: No free trial, POCs reportedly cost up to $70K for large enterprise pilots
NO FedRAMP certification: Not suitable for US federal government deployments
Limited consumer channels: No native WhatsApp, Telegram integrations - designed for internal enterprise use only
Complex implementation: Initial indexing takes "few days" depending on data volume, requires enterprise IT coordination
Cross-language queries in early access: English query finding Spanish documents still in testing phase
Best for: Large enterprises (1K-100K users) with complex permission hierarchies, $60K+ budgets, and need for permissions-aware AI across 100+ datasources
NOT suitable for: SMBs, startups, simple document Q&A without permission requirements, organizations prioritizing transparent pricing
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
Autonomous AI agents: Agents use AI to understand tasks and take action on behalf of users from answering questions and retrieving information to executing work autonomously
Natural language agent builder: Build agents by describing desired output in simple natural language - Glean understands goal and designs complex multi-step workflows
Agentic reasoning engine: LLM-agnostic engine enables agents to go beyond retrieval and generation - powers sophisticated automation and decision-making by understanding outcomes, building multi-step plans, and using action library
100+ native actions: Supports 100+ new native actions across Slack, Microsoft Teams, Salesforce, Jira, GitHub, Google Workspace and other applications
MCP host support: Gives agents dramatically larger surface area to operate across enterprise applications
Human-in-the-loop design: Agents can autonomously do work end-to-end with human review checkpoints - process customer support tickets, conduct research, prepare responses for employee review before execution
Vibe coding: Upgraded builder makes agent creation as simple as chatting - anyone (not just developers) can create and refine agents without understanding or interacting with code
Grounded in enterprise data: Autonomous agents grounded in most relevant authoritative information for confident work automation
Automatic agent triggering: Orchestrates agents automatically based on schedules or events and surfaces agent recommendations across enterprise
Visual and conversational workflow design: Turn ideas into structured workflows using simple natural language prompts or visual builder
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
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
Core R A G Features
N/A
Hybrid search: Combines semantic (vector-based) and lexical (keyword) approaches
Knowledge Graph Framework: Proprietary anchors and signals across enterprise data
Rich, Scalable Crawler: Permission rule synchronization at scale
LLM Control Layer: Optimizes and controls LLM outputs
After analyzing features, pricing, performance, and user feedback, both Dataworkz and Glean 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 Glean
You value permissions-aware ai is genuinely differentiated - real-time enforcement across 100+ datasources addresses critical enterprise concern
Model flexibility without vendor lock-in - 15+ LLMs with per-step selection and bring-your-own-key option
Best For: Permissions-aware AI is genuinely differentiated - real-time enforcement across 100+ datasources addresses critical enterprise concern
Migration & Switching Considerations
Switching between Dataworkz and Glean 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 Glean begins at $50/month. 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
Start with a free trial - Both platforms offer trial periods to test with your actual data
Define success metrics - Response accuracy, latency, user satisfaction, cost per query
Test with real use cases - Don't rely on generic demos; use your production data
Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
Check vendor stability - Review roadmap transparency, update frequency, and support quality
For most organizations, the decision between Dataworkz and Glean 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 13, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
The most accurate RAG-as-a-Service API. Deliver production-ready reliable RAG applications faster. Benchmarked #1 in accuracy and hallucinations for fully managed RAG-as-a-Service API.
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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