In this comprehensive guide, we compare Azure AI 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 Azure AI 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 Azure AI if: you value comprehensive ai platform with 200+ services
Choose Glean if: you value permissions-aware ai is genuinely differentiated - real-time enforcement across 100+ datasources addresses critical enterprise concern
About Azure AI
Azure AI is microsoft's comprehensive ai platform for enterprise solutions. Azure AI is Microsoft's suite of AI services offering pre-built APIs, custom model development, and enterprise-grade infrastructure for building intelligent applications across vision, language, speech, and decision-making domains. Founded in 1975, headquartered in Redmond, WA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
88/100
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, Azure AI starts at a lower price point. The platforms also differ in their primary focus: AI 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
Azure AI
Glean
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Lets you pull data from almost anywhere—databases, blob storage, or common file types like PDF, DOCX, and HTML—as shown in the Azure AI Search overview.
Uses Azure pipelines and connectors to tap into a wide range of content sources, so you can set up indexing exactly the way you need.
Keeps everything in sync through Azure services, ensuring your information stays current without extra effort.
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.
Integrations & Channels
Provides full-featured SDKs and REST APIs that slot right into Azure’s ecosystem—including Logic Apps and PowerApps (Azure Connectors).
Supports easy embedding via web widgets and offers native hooks for Slack, Microsoft Teams, and other channels.
Lets you build custom workflows with Azure’s low-code tools or dive deeper with the full API for more control.
Slack: Official Marketplace app, Gleanbot auto-responses, Real-Time Search API
Microsoft Teams: Native Teams app and agent integration
Zoom: Custom AI Companion integration
No WhatsApp: No native integration
No Telegram: No native integration
No Zapier: No native integration (different product "Glean.ly" exists)
Combines semantic search with LLM generation to serve up context-rich, source-grounded answers.
Uses hybrid search (keyword + semantic) and optional semantic ranking to surface the most relevant results.
Offers multilingual support and conversation-history management, all from inside the Azure portal.
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
Gives you full control over the search interface—tweak CSS, swap logos, or craft welcome messages to fit your brand.
Supports domain restrictions and white-labeling through straightforward Azure configuration settings.
Lets you fine-tune search behavior with custom analyzers and synonym maps (Azure Index Configuration).
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
Backed by Microsoft’s extensive support network, with in-depth docs, Microsoft Learn modules, and active community forums.
Offers enterprise support plans featuring SLAs and dedicated channels for mission-critical deployments.
Benefits from a large community of Azure developers and partners who regularly share best practices.
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
Deep Azure integration lets you craft end-to-end solutions without leaving the platform.
Combines fine-grained tuning capabilities with the reliability you’d expect from an enterprise-grade service.
Best suited for organizations already invested in Azure, thanks to unified billing and familiar cloud management tools.
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
Provides an intuitive Azure portal where you can create indexes, tweak analyzers, and monitor performance.
Low-code tools like Logic Apps and PowerApps connectors help non-developers add search features without heavy coding.
More advanced setups—complex indexing or fine-grained configuration—may still call for technical expertise versus fully turnkey options.
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-grade cloud AI platform deeply integrated with Microsoft ecosystem, offering production-ready search and RAG capabilities at global scale
Target customers: Organizations already invested in Azure infrastructure, Microsoft enterprise customers, and companies requiring enterprise compliance (SOC, ISO, GDPR, HIPAA, FedRAMP) with 99.999% uptime SLAs
Key competitors: AWS Bedrock, Google Vertex AI, OpenAI Enterprise, Coveo, and Vectara.ai for enterprise search and RAG
Competitive advantages: Seamless Azure ecosystem integration (Logic Apps, PowerApps, Microsoft Teams), hybrid search with semantic ranking, native Azure OpenAI integration, global infrastructure for low latency, and unified billing/management through Azure portal
Pricing advantage: Pay-as-you-go model with free tier for development; competitive for Azure customers who can leverage existing enterprise agreements and volume discounts; scales efficiently with consumption-based pricing
Use case fit: Best for organizations already using Azure infrastructure, Microsoft enterprise customers needing tight Office 365/Teams integration, and companies requiring global scalability with enterprise-grade compliance and regional data residency options
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
Azure OpenAI Service: Access to GPT-4, GPT-4o, GPT-3.5 Turbo through native Azure integration
Anthropic Claude: Available through Microsoft Foundry, bringing frontier intelligence to Azure (late 2024/early 2025)
Multi-Model Platform: Azure is the only cloud providing access to both Claude and GPT frontier models to customers on one platform
Model Selection Flexibility: Choose between Azure-hosted models or external LLMs accessed via API
Prompt Templates: Customizable system prompts and prompt templates to shape model behavior for specific use cases
Enterprise Integration: All models integrated with Azure security, compliance, and governance frameworks
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
Agentic Retrieval (New 2024): Specialized pipeline using LLMs to intelligently break down complex queries into focused subqueries, executing them in parallel with structured responses optimized for chat completion models
Hybrid Search: Combines vector search, keyword search, and semantic search in the same corpus with sophisticated relevance tuning
Vector Store Functionality: Functions as long-term memory, knowledge base, or grounding data repository for RAG applications
Semantic Kernel Integration: Supports Azure Semantic Kernel and LangChain for coordinating RAG workflows
Import Wizard Automation: Built-in Azure portal wizard automates RAG pipeline with parsing, chunking, enrichment, and embedding in one flow
Enterprise Scale: Designed for millisecond-level responses under heavy load with global infrastructure (Microsoft Mechanics)
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
Enterprise Search: Centralizes documents and policies into searchable repository, improving productivity by up to 40% (saving nearly 9 hours per week per employee)
Customer Service Automation: Powers self-service chatbots, real-time agent counsel, agent coaching, and automated conversation summarization
RAG Applications: Over half of Fortune 500 companies use Azure AI Search for mission-critical RAG workloads (OpenAI, Otto Group, KPMG, PETRONAS)
Knowledge Management: Enables employees to quickly find information in vast organizational knowledge bases with AI-driven insights
Personalized Customer Interactions: Delivers relevant, real-time responses through self-service portals and chatbots based on customer data
Content Discovery: Dynamic content generation through chat completion models for AI-powered customer experiences
Multi-Industry Applications: Proven across retail, financial services, healthcare, manufacturing, and government sectors
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
Microsoft Support Network: Extensive support backed by Microsoft's enterprise support infrastructure with dedicated channels for mission-critical deployments
Enterprise SLA Plans: Dedicated support plans with guaranteed response times and uptime commitments
Microsoft Learn: Comprehensive in-depth documentation, Microsoft Learn modules, and step-by-step tutorials (Azure AI Search Documentation)
Community Forums: Active community of Azure developers and partners sharing best practices and solutions
Azure Portal Dashboard: Integrated monitoring and management through Azure portal for index tracking, query performance, and usage analytics
Official SDKs: Robust REST APIs and SDKs for C#, Python, Java, JavaScript with comprehensive sample code (Azure SDKs)
Azure Monitor Integration: Custom alerts, dashboards, and analytics through Azure Monitor and Application Insights (Azure Monitor)
Standard support: 24x5 (Mon-Fri) via portal, email, Slack Connect channels
Premium support: 24x7 for critical issues with additional fee
Dedicated CSMs: Enterprise accounts with hands-on onboarding and regular business reviews
Excellent documentation: developers.glean.com with OpenAPI specs, CodeSandbox demos, comprehensive API references
Official SDKs: Python (pip install glean), Java (Maven), Go, TypeScript with async support and framework integrations
Web SDK: @gleanwork/web-sdk for embeddable components (chat, search, autocomplete, recommendations)
GitHub repositories: github.com/gleanwork with SDK repositories and sample projects
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
Free Tier Constraints: 50 MB storage limit, shared resources with other subscribers, no fixed partitions or replicas
Tier Immutability (Legacy): Cannot change tier after creation on older services, though new 2024 feature allows tier changes
Vector Search Limitations: Vector index sizes restricted by memory reserved for service tier, some regions lack required infrastructure for improved limits
No Pause/Stop: Cannot pause search service - computing resources allocated when created, pay continuous fixed rate
Index Portability: No native backup/restore support for porting indexes between services
Query Complexity: Partial term searches (prefix, fuzzy, regex) more computationally expensive than keyword searches, may impact performance
Field Size Limits: Facetable/filterable/searchable fields limited to 16 KB text storage vs 16 MB for searchable-only fields; maximum document size ~16 MB; record limit 50,000 characters
Schema Flexibility: Updating existing indexes can be difficult and disrupt workflows in some cases, requiring workarounds
Learning Curve: Advanced customizations require steep learning curve with trial-and-error for fine-tuning search experience
Cost Considerations: Pricing structure restrictive for smaller teams/individual developers; costs quickly add up with higher usage tiers and complex pricing models
Latency Trade-offs: AI enrichment and image analysis computationally intensive, consuming disproportionate processing power
Language Support: Some features (speller, query rewrite) limited to subset of languages
Offline Documentation: Lack of offline documentation frustrating for limited internet environments
Azure Ecosystem Lock-In: Best suited for organizations already invested in Azure, less competitive for non-Azure customers
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 Retrieval (2024): Multi-query pipeline designed for complex questions in chat and copilot apps using LLMs to break queries into smaller, focused subqueries for better coverage (Agentic Retrieval)
Query Decomposition: Deconstructs complex queries containing multiple "asks" into component parts with LLM-generated paraphrasing and synonym mapping
Parallel Execution: Subqueries run in parallel with semantic reranking to promote most relevant matches, then combined into unified response
Performance Enhancement: Up to 40% improvement in answer relevance in conversational AI compared to traditional RAG approaches
Knowledge Base Integration: Knowledge bases ground agents with multiple data sources without siloed retrieval pipelines, available in Azure AI Foundry portal
Chat History Context: Reads conversation history as input to retrieval pipeline for contextually aware responses
Automatic Corrections: Corrects spelling mistakes and rewrites queries using synonym maps for improved retrieval accuracy
API Availability: Supported through Knowledge Base object in 2025-11-01-preview and Azure SDK preview packages (public preview)
Agent-to-Agent Workflows: Designed for RAG patterns and agent-to-agent communication in enterprise AI systems
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 - End-to-end RAG systems built for app excellence, enterprise-readiness, and speed to market with native Azure integration
AI-Assisted Metrics: 3 AI-assisted metrics in prompt flow requiring no ground truth - breaks queries into intents, assesses relevant information, calculates affirmative response fractions
Hybrid Search Optimization: Combines vector search, keyword search, and semantic search with sophisticated relevance tuning for improved retrieval performance
Answer Optimization: Built-in capabilities for retrieval steering, reasoning effort optimization, and answer synthesis for production RAG applications
Query Planning: Leverages knowledge bases and AI models for query planning, decomposition, reranking, and structured answer synthesis
Enterprise Scale Analytics: Insights into user search behavior, query performance, and search result effectiveness through built-in analytics and monitoring
Import Wizard Automation: Azure portal wizard automates RAG pipeline with parsing, chunking, enrichment, and embedding in single flow
Azure AI Studio Integration: Unified platform for exploring APIs/models, comprehensive tooling, responsible design, deployment at scale with continuous monitoring
40% Accuracy Improvement: Studies demonstrate RAG can increase base model accuracy by 40% compared to standalone LLMs (RAG Performance)
Production-Ready Excellence: Rigorously tested AI technology with high-performance RAG applications without compromising scale or cost
Global Infrastructure: Designed for millisecond-level responses under heavy load with globally distributed infrastructure
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 Azure AI and Glean are capable platforms that serve different market segments and use cases effectively.
When to Choose Azure AI
You value comprehensive ai platform with 200+ services
Deep integration with Microsoft ecosystem
Enterprise-grade security and compliance
Best For: Comprehensive AI platform with 200+ services
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 Azure AI 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
Azure AI 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 Azure AI 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 14, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
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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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