What are the main differences between Dataworkz and Glean?

Dataworkz excels in Free tier available for testing and No-code approach simplifies development, scoring 79/100 in user ratings with 82% accuracy. Glean leads in Permissions-aware AI is genuinely differentiated - real-time enforcement across 100+ datasources addresses critical enterprise concern and Strong developer experience - comprehensive APIs, 4-language SDKs (Python, Java, Go, TypeScript), LangChain integration with 96/100 overall rating and 90% accuracy. Key technical difference: Dataworkz focuses on No-code RAG builder while Glean emphasizes Permissions-aware AI with real-time access control enforcement. Pricing differs by $50/month.

Which is better for enterprises: Dataworkz or Glean?

Glean offers SOC 2 compliance for enterprise security. HIPAA compliance is available in Glean. For scalability, both offer cloud-based deployment options. Glean leads with 96/100 enterprise readiness score (98/100 enterprise features).

How much does Dataworkz cost vs Glean?

Dataworkz starts at custom pricing/monthly (freemium model), while Glean begins at $50/monthly (enterprise model). Dataworkz offers a free tier. Pricing is relatively comparable. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from Dataworkz to Glean?

Migration from Dataworkz to Glean typically requires API-based data export/import, which Glean provides. Glean provides comprehensive API access excellent documentation for the migration process. Estimated migration time: 1-2 days for cloud setup. Contact Glean support for migration assistance and best practices.

What integrations do Dataworkz and Glean support?

Dataworkz integrates with AWS, Azure, Google Cloud, OpenAI, Anthropic (10 integrations total). Glean supports Cloud Storage: Google Drive, SharePoint, OneDrive, Dropbox, Box, Communication: Slack (full bot support), Microsoft Teams, Gmail, Outlook, Zoom, Collaboration: Confluence, Notion, Jira, GitHub, GitLab, Miro, CRM/Support: Salesforce, ServiceNow, Zendesk, Identity: Okta, Microsoft Entra ID, Google Workspace, OneLogin (8 integrations available). Both offer REST APIs for custom integrations. Dataworkz offers 25% more integrations, with webhook support for real-time data sync.

What are the main differences between Dataworkz and Glean?

Dataworkz offers strong capabilities, scoring 0/100 in user ratings. Glean provides unique strengths with 0/100 overall rating. Key technical difference: Dataworkz focuses on advanced features while Glean emphasizes specialized capabilities.

Which is better for enterprises: Dataworkz or Glean?

Both platforms provide enterprise-grade security. HIPAA compliance varies - contact vendors for details. For scalability, both offer cloud-based deployment options. Glean leads with strong/100 enterprise readiness score.

How much does Dataworkz cost vs Glean?

Dataworkz starts at custom pricing/month (subscription model), while Glean begins at contact sales/month (subscription model). Both require paid plans. Pricing is relatively comparable. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from Dataworkz to Glean?

Migration from Dataworkz to Glean typically requires manual data transfer since API access may be limited. Glean offers standard documentation for the migration process. Estimated migration time: 1-2 days for cloud setup. Contact Glean support for migration assistance and best practices.

What integrations do Dataworkz and Glean support?

Dataworkz integrates with (multiple platforms). Glean supports (various tools). API availability varies by plan. Integration coverage is comparable.

Dataworkz vs Glean

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

More about Glean More about Dataworkz Get Started Today

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 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
✅ Point-and-click RAG builder – Mix SharePoint, Confluence, databases via visual pipeline [MongoDB Reference] ✅ Fine-grained control – Configure chunk sizes, embedding strategies, multiple sources simultaneously ✅ Multi-source blending – Combine documents and live database queries in same pipeline	✅ 100+ native connectors – Cloud storage, CRM, collaboration platforms plus Indexing API File formats – PDFs, Word, HTML, spreadsheets, structured data Real-time sync – Content within minutes, permission changes immediate ⚠️ Initial indexing – Few days depending on volume Scale – 10K-100K users, hundreds of millions of documents	1,400+ file formats – PDF, DOCX, Excel, PowerPoint, Markdown, HTML + auto-extraction from ZIP/RAR/7Z archives Website crawling – Sitemap indexing with configurable depth for help docs, FAQs, and public content Multimedia transcription – AI Vision, OCR, YouTube/Vimeo/podcast speech-to-text built-in Cloud integrations – Google Drive, SharePoint, OneDrive, Dropbox, Notion with auto-sync Knowledge platforms – Zendesk, Freshdesk, HubSpot, Confluence, Shopify connectors Massive scale – 60M words (Standard) / 300M words (Premium) per bot with no performance degradation
Integrations & Channels
✅ API-first architecture – Surface agents via REST or GraphQL endpoints [MongoDB: API Approach] ⚠️ No prefab UI – Bring or build your own front-end chat widget ✅ Universal integration – Drop into any environment that makes HTTP calls	Slack & Teams – Official apps, Gleanbot auto-responses, Real-Time Search API Browser extensions – Chrome (300K+ users), Firefox, Safari, Edge ⚠️ No WhatsApp/Telegram – Designed for internal enterprise use only SSO protocols – OIDC, SAML 2.0 with Okta, Microsoft Entra	Website embedding – Lightweight JS widget or iframe with customizable positioning CMS plugins – WordPress, WIX, Webflow, Framer, SquareSpace native support 5,000+ app ecosystem – Zapier connects CRMs, marketing, e-commerce tools MCP Server – Integrate with Claude Desktop, Cursor, ChatGPT, Windsurf OpenAI SDK compatible – Drop-in replacement for OpenAI API endpoints LiveChat + Slack – Native chat widgets with human handoff capabilities
Core Chatbot Features
✅ Agentic architecture – Multi-step reasoning, tool use, dynamic decision-making [Agentic RAG] ✅ Intelligent routing – Agents decide knowledge base vs live DB vs API ✅ Complex workflows – Fetch structured data, retrieve docs, blend answers automatically	Glean Chat interface – Natural conversations with company knowledge Multi-turn conversations – Context awareness across turns Streaming responses – Real-time with source citations	✅ #1 accuracy – Median 5/5 in independent benchmarks, 10% lower hallucination than OpenAI ✅ Source citations – Every response includes clickable links to original documents ✅ 93% resolution rate – Handles queries autonomously, reducing human workload ✅ 92 languages – Native multilingual support without per-language config ✅ Lead capture – Built-in email collection, custom forms, real-time notifications ✅ Human handoff – Escalation with full conversation context preserved
Customization & Branding
✅ 100% front-end control – No built-in UI means complete look and feel ownership ✅ Deep behavior tweaks – Customize prompt templates and scenario configs extensively ✅ Multiple personas – Create unlimited agent personas with different rule sets	UI customization – Custom logos, color schemes, background images Custom subdomains – your-company.glean.com Chat widget styling – CSS positioning, width/height, custom containers ⚠️ Limited white-labeling – Glean branding may remain visible	Full white-labeling included – Colors, logos, CSS, custom domains at no extra cost 2-minute setup – No-code wizard with drag-and-drop interface Persona customization – Control AI personality, tone, response style via pre-prompts Visual theme editor – Real-time preview of branding changes Domain allowlisting – Restrict embedding to approved sites only
L L M Model Options
✅ Model-agnostic – Plug in GPT-4, Claude, open-source models freely ✅ Full stack control – Choose embedding model, vector DB, orchestration logic ⚠️ More setup required – Power and flexibility trade-off vs turnkey solutions	✅ 15+ LLMs supported – GPT-4, Gemini 1.5 Pro, Claude 3 Sonnet Per-step model selection – Different LLMs for each workflow step Temperature controls – Factual, balanced, or creative output settings BYOK support – Glean Universal Key or Customer Key options Automatic routing – Zero data retention, optimizes top models per query	GPT-5.1 models – Latest thinking models (Optimal & Smart variants) GPT-4 series – GPT-4, GPT-4 Turbo, GPT-4o available Claude 4.5 – Anthropic's Opus available for Enterprise Auto model routing – Balances cost/performance automatically Zero API key management – All models managed behind the scenes
Developer Experience ( A P I & S D Ks)
✅ No-code pipeline builder – Design pipelines visually, deploy to single API endpoint ✅ Sandbox testing – Rapid iteration and tweaking before production launch ⚠️ No official SDK – REST/GraphQL integration straightforward but no client libraries	✅ Client & Indexing APIs – Search, Chat, Agents, Documents, Governance Official SDKs – Python, Java, Go, TypeScript with async support Web SDK – Embeddable chat, search, autocomplete, recommendations components Framework integrations – LangChain, CrewAI, OpenAI Assistants, MCP Server Authentication – OAuth 2.0, user-scoped tokens, impersonation support	REST API – Full-featured for agents, projects, data ingestion, chat queries Python SDK – Open-source `customgpt-client` with full API coverage Postman collections – Pre-built requests for rapid prototyping Webhooks – Real-time event notifications for conversations and leads OpenAI compatible – Use existing OpenAI SDK code with minimal changes
Performance & Accuracy
✅ Hybrid retrieval – Mix semantic, lexical, or graph search for sharper context ✅ Threshold tuning – Balance precision vs recall for your domain requirements ✅ Enterprise scaling – Vector DBs and stores handle high-volume workloads efficiently	✅ 74% human-agreement rate – 25% precision increases 141% ROI over 3 years – Forrester Total Economic Impact 110 hours saved per employee annually – Productivity gains Hallucination prevention – RAG grounding, permissions, citations	Sub-second responses – Optimized RAG with vector search and multi-layer caching Benchmark-proven – 13% higher accuracy, 34% faster than OpenAI Assistants API Anti-hallucination tech – Responses grounded only in your provided content OpenGraph citations – Rich visual cards with titles, descriptions, images 99.9% uptime – Auto-scaling infrastructure handles traffic spikes
Customization & Flexibility ( Behavior & Knowledge)
✅ Multi-step reasoning – Scenario logic, tool calls, unified agent workflows ✅ Data blending – Combine structured APIs/DBs with unstructured docs seamlessly ✅ Full retrieval control – Customize chunking, metadata, and retrieval algorithms completely	Natural language config – Build agents without technical expertise Visual builder – Drag-and-drop workflow creation 30+ templates – Sales, engineering, IT, HR	Live content updates – Add/remove content with automatic re-indexing System prompts – Shape agent behavior and voice through instructions Multi-agent support – Different bots for different teams Smart defaults – No ML expertise required for custom behavior
Pricing & Scalability
⚠️ Custom contracts only – No public tiers, typically usage-based enterprise pricing ✅ Massive scalability – Leverage your own infrastructure for huge data and concurrency ✅ Best for large orgs – Ideal for flexible architecture and pricing at scale	⚠️ No public pricing – Enterprise sales only Estimated cost – ~$45-50+ per user/month Minimum ACV – ~$60K (approximately 100 users minimum) No free trial – Paid POCs up to $70K Annual contracts – Per-seat model, 7-12% renewal increases, FlexCredits for premium	Standard: $99/mo – 60M words, 10 bots Premium: $449/mo – 300M words, 100 bots Auto-scaling – Managed cloud scales with demand Flat rates – No per-query charges
Security & Privacy
✅ Enterprise-grade security – Encryption, compliance, access controls included [MongoDB: Enterprise Security] ✅ Data sovereignty – Keep data in your environment with bring-your-own infrastructure ✅ Single-tenant VPC – Supports strict isolation for regulatory compliance requirements	✅ SOC 2 Type II, ISO 27001, HIPAA, GDPR ⚠️ No FedRAMP – Not for US federal government AES-256 at rest, TLS 1.2+ in transit Single-tenant – Isolated per customer, Cloud-Prem option	SOC 2 Type II + GDPR – Third-party audited compliance Encryption – 256-bit AES at rest, SSL/TLS in transit Access controls – RBAC, 2FA, SSO, domain allowlisting Data isolation – Never trains on your data
Observability & Monitoring
✅ Pipeline-stage monitoring – Track chunking, embeddings, queries with detailed visibility [MongoDB: Lifecycle Tools] ✅ Step-by-step debugging – See which tools agent used and why decisions made ✅ External logging integration – Hooks for logging systems and A/B testing capabilities	Insights Dashboard – DAU/WAU/MAU, Search Session Satisfaction metrics Usage metrics – Searches and chats per user per week 270-day data retention – Department-level filtering Audit logging – User activity, access patterns, SIEM export	Real-time dashboard – Query volumes, token usage, response times Customer Intelligence – User behavior patterns, popular queries, knowledge gaps Conversation analytics – Full transcripts, resolution rates, common questions Export capabilities – API export to BI tools and data warehouses
Support & Ecosystem
✅ Tailored onboarding – Enterprise-focused with solution engineering for large customers ✅ MongoDB partnership – Tight integrations with Atlas Vector Search and enterprise support [Case Study] ⚠️ Limited public forums – Direct engineer-to-engineer support vs broad community resources	Standard support – 24x5 via portal, email, Slack Connect Premium support – 24x7 critical issues with additional fee ✅ Excellent documentation – developers.glean.com with OpenAPI specs GitHub repositories – SDK examples and sample projects	Comprehensive docs – Tutorials, cookbooks, API references Email + in-app support – Under 24hr response time Premium support – Dedicated account managers for Premium/Enterprise Open-source SDK – Python SDK, Postman, GitHub examples 5,000+ Zapier apps – CRMs, e-commerce, marketing integrations
Additional Considerations
✅ Graph-optimized retrieval – Specialized for interlinked docs with relationships [MongoDB Reference] ✅ AI orchestration layer – Call APIs or trigger actions as part of answers ⚠️ Requires LLMOps expertise – Best for teams wanting deep customization, not prefab chatbots ✅ Tailor-made agents – Focuses on custom AI agents vs out-of-box chat tool	⚠️ Cannot create content directly – Focuses on search/retrieval Large organization focus – May be overkill for smaller teams Training investment – Upskilling employees needed	Time-to-value – 2-minute deployment vs weeks with DIY Always current – Auto-updates to latest GPT models Proven scale – 6,000+ organizations, millions of queries Multi-LLM – OpenAI + Claude reduces vendor lock-in
No- Code Interface & Usability
✅ Low-code builder – Set up pipelines, chunking, data sources without heavy coding ⚠️ Technical knowledge needed – Understanding embeddings and prompts helps significantly ⚠️ No end-user UI – You build front-end while Dataworkz handles back-end logic	✅ Natural language configuration – Describe goals in plain language Visual builder – Drag-and-drop workflow creation AI-assisted creation – Auto-generates draft agents 30+ prebuilt agents – Sales, engineering, IT, HR	2-minute deployment – Fastest time-to-value in the industry Wizard interface – Step-by-step with visual previews Drag-and-drop – Upload files, paste URLs, connect cloud storage In-browser testing – Test before deploying to production Zero learning curve – Productive on day one
Competitive Positioning
Market position – Enterprise agentic RAG platform with point-and-click pipeline builder Target customers – Large enterprises with LLMOps expertise building complex AI agents Key competitors – Deepset Cloud, LangChain/LangSmith, Haystack, Vectara.ai, custom RAG solutions Core advantages – Model-agnostic, agentic architecture, graph retrieval, no-code builder, MongoDB partnership Best for – High-volume complex use cases with existing infrastructure and orchestration needs	✅ Unique strength – Real-time permissions-aware AI across 100+ datasources Proven ROI – 141% ROI, 110 hours/employee saved ⚠️ Pricing barrier – ~$50/user/month, ~$60K minimum	Market position – Leading RAG platform balancing enterprise accuracy with no-code usability. Trusted by 6,000+ orgs including Adobe, MIT, Dropbox. Key differentiators – #1 benchmarked accuracy • 1,400+ formats • Full white-labeling included • Flat-rate pricing vs OpenAI – 10% lower hallucination, 13% higher accuracy, 34% faster vs Botsonic/Chatbase – More file formats, source citations, no hidden costs vs LangChain – Production-ready in 2 min vs weeks of development
A I Models
✅ Model-agnostic – GPT-4, Claude, Llama, open-source models fully supported ✅ Public APIs – AWS Bedrock and OpenAI API integration for managed access ✅ Private hosting – Host open-source models in your VPC for sovereignty ✅ Composable stack – Choose embedding, vector DB, chunking, LLM independently ✅ No lock-in – Switch models without platform migration for cost or compliance	✅ 15+ LLMs – GPT-4, Gemini 1.5 Pro, Claude 3 with per-step selection Temperature controls – Factual, balanced, or creative output BYOK support – Customer Key for data sovereignty	OpenAI – GPT-5.1 (Optimal/Smart), GPT-4 series Anthropic – Claude 4.5 Opus/Sonnet (Enterprise) Auto-routing – Intelligent model selection for cost/performance Managed – No API keys or fine-tuning required
R A G Capabilities
✅ Advanced pipeline builder – Point-and-click RAG configuration with fine-grained control RAG-as-a-Service ✅ Agentic architecture – Multi-step tasks, external tool calls, adaptive reasoning [Agentic RAG] ✅ Hybrid retrieval – Semantic, lexical, graph search for accuracy and context ✅ Graph-optimized – Relationship-aware context for interlinked documents [Graph Capabilities] ✅ Dynamic tool selection – Agents choose knowledge base, DB, or API automatically	✅ Hybrid search – Semantic + lexical for accuracy Knowledge Graph Framework – Proprietary anchors across data 74% human-agreement – 25% precision increases	GPT-4 + RAG – Outperforms OpenAI in independent benchmarks Anti-hallucination – Responses grounded in your content only Automatic citations – Clickable source links in every response Sub-second latency – Optimized vector search and caching Scale to 300M words – No performance degradation at scale
Use Cases
Retail – Product recommendations, inventory queries with structured/unstructured data blending [Retail Case Study] Banking – Regulatory compliance, risk assessment with enterprise security and auditability Healthcare – Clinical decision support, medical knowledge bases with HIPAA compliance Enterprise knowledge – Documentation, policy queries with multi-source integration (SharePoint, Confluence, databases) Customer support – Multi-step troubleshooting, automated responses with tool calling and APIs Legal – Contract analysis, regulatory research with audit trails and traceability	Enterprise knowledge – Unified search across 100+ datasources Permissions-aware – Healthcare, finance, legal hierarchies AI agents – 30+ prebuilt for sales, engineering, IT	Customer support – 24/7 AI handling common queries with citations Internal knowledge – HR policies, onboarding, technical docs Sales enablement – Product info, lead qualification, education Documentation – Help centers, FAQs with auto-crawling E-commerce – Product recommendations, order assistance
Security & Compliance
✅ Enterprise-grade – Encryption, compliance, access controls for large organizations [Security Features] ✅ Audit trails – Every interaction, tool call, data access audited for transparency ✅ Data sovereignty – Bring-your-own-infrastructure keeps data in your environment completely ✅ Compliance ready – Architecture supports GDPR, HIPAA, SOC 2 through flexible deployment	✅ SOC 2 Type II, ISO 27001, HIPAA, GDPR ⚠️ NO FedRAMP – Not for US federal government Single-tenant – Isolated per customer, Cloud-Prem option	SOC 2 Type II + GDPR – Regular third-party audits, full EU compliance 256-bit AES encryption – Data at rest; SSL/TLS in transit SSO + 2FA + RBAC – Enterprise access controls with role-based permissions Data isolation – Never trains on customer data Domain allowlisting – Restrict chatbot to approved domains
Pricing & Plans
⚠️ Custom contracts – Tailored pricing, no public tiers, requires sales engagement ✅ Credit-based usage – 2M rows per credit for data movement, usage-based model ✅ AWS Marketplace – Available for streamlined enterprise procurement [AWS Marketplace] ✅ BYOI savings – Use existing infrastructure (databases, vector stores) to reduce costs	⚠️ No public pricing – Enterprise sales only ~$45-50+/user/month – Estimated cost from reports ~$60K minimum ACV – Approximately 100 users minimum	Standard: $99/mo – 10 chatbots, 60M words, 5K items/bot Premium: $449/mo – 100 chatbots, 300M words, 20K items/bot Enterprise: Custom – SSO, dedicated support, custom SLAs 7-day free trial – Full Standard access, no charges Flat-rate pricing – No per-query charges, no hidden costs
Support & Documentation
✅ Enterprise onboarding – Tailored solution engineering for large organizations with complex needs ✅ Direct engineering support – Engineer-to-engineer technical implementation and optimization assistance ✅ Product documentation – Platform setup, pipeline config, agentic workflows covered [Product Docs] ✅ MongoDB partnership – Joint support for Atlas Vector Search and enterprise deployments	24x5 standard support – Portal, email, Slack Connect ✅ Excellent documentation – developers.glean.com with OpenAPI Official SDKs – Python, Java, Go, TypeScript	Documentation hub – Docs, tutorials, API references Support channels – Email, in-app chat, dedicated managers (Premium+) Open-source – Python SDK, Postman, GitHub examples Community – User community + 5,000 Zapier integrations
Limitations & Considerations
⚠️ No built-in UI – API-first platform requires you to build front-end interface ⚠️ Technical expertise required – Best for LLMOps teams understanding embeddings, prompts, RAG architecture ⚠️ Custom pricing only – No transparent public tiers, requires sales engagement for quotes ⚠️ Enterprise focus – May be overkill for small teams or simple chatbot cases ⚠️ Infrastructure requirements – BYOI model needs existing cloud infrastructure and data engineering capabilities	⚠️ High cost barrier – ~$50/user/month, ~$60K minimum ⚠️ NO FedRAMP – Not for US federal government ⚠️ No consumer channels – No WhatsApp, Telegram Complex implementation – Indexing takes days	Managed service – Less control over RAG pipeline vs build-your-own Model selection – OpenAI + Anthropic only; no Cohere, AI21, open-source Real-time data – Requires re-indexing; not ideal for live inventory/prices Enterprise features – Custom SSO only on Enterprise plan
Core Agent Features
✅ Agentic RAG – Multi-step reasoning, external tools, adaptive context-based operation [Agentic Capabilities] ✅ Agent memory – Conversational history, user preferences, business context via RAG pipelines ✅ DAG task execution – Complex tasks decomposed into interdependent sub-tasks with parallelization [Multi-Step Reasoning] ✅ LLM Compiler – Identifies optimal sub-task sequence with parallel execution when possible ✅ External API integration – Create CRM leads, support tickets, trigger actions dynamically [Agent Builder] ✅ Continuous learning – Agent frameworks support context switching and adaptation over time	✅ Autonomous AI agents – Understand tasks, execute autonomously Natural language builder – Describe goals, auto-designs workflows 100+ native actions – Slack, Teams, Salesforce, Jira, GitHub	Custom AI Agents – Autonomous GPT-4/Claude agents for business tasks Multi-Agent Systems – Specialized agents for support, sales, knowledge Memory & Context – Persistent conversation history across sessions Tool Integration – Webhooks + 5,000 Zapier apps for automation Continuous Learning – Auto re-indexing without manual retraining
R A G-as-a- Service Assessment
Platform type – TRUE RAG-AS-A-SERVICE: Enterprise agentic orchestration layer for custom agents Core architecture – Model-agnostic with full control over LLM, embeddings, vector DB, chunking Agentic focus – Autonomous agents with multi-step reasoning, not simple Q&A chatbots [Agentic RAG] Developer experience – Point-and-click builder, sandbox testing, REST/GraphQL API, agent builder UI Target market – Large enterprises with data teams building sophisticated agents requiring deep customization RAG differentiation – Graph retrieval, hybrid search, threshold tuning, agentic DAG execution	✅ TRUE RAG PLATFORM – API-first with comprehensive tools Data flexibility – 100+ connectors, Indexing API Best for – Large enterprises requiring permissions-aware RAG	Platform type – TRUE RAG-AS-A-SERVICE with managed infrastructure API-first – REST API, Python SDK, OpenAI compatibility, MCP Server No-code option – 2-minute wizard deployment for non-developers Hybrid positioning – Serves both dev teams (APIs) and business users (no-code) Enterprise ready – SOC 2 Type II, GDPR, WCAG 2.0, flat-rate pricing
Core R A G Features
N/A	✅ Hybrid search – Semantic (vector-based) + lexical (keyword) approaches Knowledge Graph Framework – Proprietary anchors across enterprise data LLM Control Layer – Optimizes outputs with context-aware query rewriting Grounded answers – Source citations with every response	N/A
Permissions- Aware A I ( Core Differentiator)
N/A	✅ Real-time access control – Across all 100+ datasources Identity crawling – Users, groups, memberships, permission models Connector-level mirroring – Respects native permissions (Salesforce, GDrive) Zero-trust architecture – Users only see authorized content	N/A
Multi- Language & Localization
N/A	Full support – English, German, Japanese (semantic, assistant, UI) Partial support – French, Spanish 20+ languages – Early access or keyword search	N/A
Conversation & Agent Features
N/A	Conversation history – Thread tracking in Slack, History tab Version control – All agent versions auto-saved ⚠️ No lead capture – Designed for internal enterprise use	N/A
Deployment Options
N/A	Cloud (SaaS) – Standard Glean infrastructure deployment Cloud-Prem – Customer-hosted in AWS or GCP Single-tenant architecture – Isolated per customer	N/A

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Final Thoughts

Final Verdict: Dataworkz vs Glean

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
Strong developer experience - comprehensive APIs, 4-language SDKs (Python, Java, Go, TypeScript), LangChain integration
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: February 16, 2026 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.

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Priyansh Khodiyar

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

Dataworkz vs Glean