In this comprehensive guide, we compare Glean and Lindy.ai 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 Glean and Lindy.ai, 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 Glean if: you value permissions-aware ai is genuinely differentiated - real-time enforcement across 100+ datasources addresses critical enterprise concern
Choose Lindy.ai if: you value exceptional no-code usability: 4.9/5 g2 rating, 30-second setup vs 15-60 min with zapier/make
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
About Lindy.ai
Lindy.ai is ai-powered personal assistant for workflow automation. No-code AI agent platform positioning as 'AI employees' for workflow automation, NOT developer-focused RAG platform. 5,000+ integrations via Pipedream, Claude Sonnet 4.5 default, $5.1M revenue (Oct 2024), 4.9/5 G2 rating. Critical limitation: No public API or SDKs available. Founded in 2023, headquartered in San Francisco, CA, USA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
81/100
Starting Price
Custom
Key Differences at a Glance
In terms of user ratings, Glean in overall satisfaction. From a cost perspective, Lindy.ai offers more competitive entry pricing. The platforms also differ in their primary focus: Enterprise RAG versus AI Assistant. 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
Glean
Lindy.ai
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
100+ native connectors covering major enterprise categories
Cloud Storage: Google Drive, SharePoint, OneDrive, Dropbox, Box
Communication: Slack, Microsoft Teams, Gmail, Outlook, Zoom
Search Constraint: When search fuzziness drops below 100, searches limited to first 1,500 files - meaningful constraint for large enterprise deployments
Marketing vs Reality: Documentation claims 'no limit to data you can feed' but practical constraints exist around character limits and file counts
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.
L L M Model Options
Model Hub supports 15+ LLMs across multiple hosting providers
OpenAI: GPT-3.5, GPT-4
Azure OpenAI: GPT models
Google Vertex AI: Gemini 1.5 Pro
Amazon Bedrock: Claude 3 Sonnet
Per-step model selection: Different LLMs for each workflow step
Temperature controls: Factual, balanced, or creative output settings
Model tiers: Basic, Standard, Premium (premium consumes FlexCredits on Enterprise Flex)
Two access options: Glean Universal Key (managed) or Customer Key (BYOK)
Zero data retention: Customer data never used for model training
Automatic model updates: Deprecated models replaced with latest versions
Automatic routing: Optimizes using best-in-class models per query type
Anthropic Claude: Sonnet 4.5 (default - 'almost no one overrides' per Anthropic case study), Sonnet 3.7, Haiku 3.5
Google Gemini: Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 2.0 Flash for varied performance/cost trade-offs
Default Selection Rationale: Claude Sonnet 4.5 excels at 'navigating ambiguity in large context windows' and handling 'deeply nested data structures requiring nuanced reasoning'
Business Impact: Lindy achieved 10x customer growth after implementing Claude as default LLM
Per-Action Granularity: Users manually select models per workflow step through visual builder interface
Credit Impact: Model selection affects credit consumption - larger models (Sonnet 4.5) consume more credits than smaller models (Haiku 3.5)
No Automatic Routing: No dynamic model switching or automatic model selection based on query complexity
Manual Configuration: Each workflow action requires explicit model selection vs intelligent automatic routing
Taps into top models—OpenAI’s GPT-5.1 series, GPT-4 series, and even Anthropic’s Claude for enterprise needs (4.5 opus and sonnet, etc ).
Automatically balances cost and performance by picking the right model for each request.
Model Selection Details
Uses proprietary prompt engineering and retrieval tweaks to return high-quality, citation-backed answers.
Handles all model management behind the scenes—no extra API keys or fine-tuning steps for you.
Performance & Accuracy
74% human-agreement rate on AI Evaluator benchmarks
25% precision increases reported in customer case studies
20% response time decreases documented
141% ROI over 3 years (Forrester Total Economic Impact study)
$15.6M NPV for composite organizations
110 hours saved per employee annually
AI Evaluator metrics: Context relevance, recall, answer relevance, completeness, groundedness
Documentation: Excellent at developers.glean.com with OpenAPI specs, CodeSandbox demos
GitHub: github.com/gleanwork with SDK repositories and examples
CRITICAL LIMITATION: Lindy deliberately prioritizes no-code accessibility over developer tooling - most significant gap for RAG platform comparison
NO Public REST API: Cannot manage agents, create workflows, or query knowledge base programmatically
NO GraphQL Endpoint: No alternative API architecture available for data querying
NO Official SDKs: No Python, JavaScript, Ruby, Go, or any other language SDK exists
NO OpenAPI/Swagger: No machine-readable API specification for automated client generation
NO CLI Tools: No command-line interface for automation or scripting
NO Developer Console: No API sandbox or testing environment available
Available Workarounds: Inbound webhooks (external systems trigger workflows via POST with bearer token), HTTP Request actions (call external APIs from workflows), Code Action (run Python/JavaScript in E2B sandboxes ~150ms startup), Callback URLs (bidirectional webhook communication)
Minimal GitHub Presence: github.com/lindy-ai contains only 3 repositories - build caching utility, ML engineer hiring challenge, no public SDKs or integration libraries
Documentation Quality: User-focused Lindy Academy with step-by-step tutorials, but NO API reference, code samples, or technical architecture documentation
Developer Path: For programmatic RAG control, custom retrieval pipelines, or embedding integration - Lindy offers no viable path forward
Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat.
API Documentation
Server-to-server auth: Enables SSO bypass when needed
Widget Customization: Display name (e.g., 'Technical Support Assistant'), accent color for brand alignment, logo/icon upload for expanded/collapsed states
Messaging Customization: Custom greeting and callout messages for initial engagement prompts
Domain Restrictions: Specify allowed deployment domains for access control and security
White-Labeling Uncertainty: Documentation doesn't explicitly confirm complete Lindy branding removal - unclear if available outside enterprise agreements
No Deep CSS Control: Limited to essential branding elements vs full CSS customization or brandless deployments on standard plans
Persona Customization: Agent-level prompts define personality, tone, and expertise areas
Settings Context: Persists across all task runs for consistent agent behavior
Per-Run Context: Allows dynamic customization per execution for adaptive responses
Memory Snippets: Learning capability saves preferences like 'Don't schedule meetings before 11am' across all sessions
RBAC Controls: Admins can lock configurations and set credit allocation limits per user or team
Fully white-labels the widget—colors, logos, icons, CSS, everything can match your brand.
White-label Options
Provides a no-code dashboard to set welcome messages, bot names, and visual themes.
Lets you shape the AI’s persona and tone using pre-prompts and system instructions.
Uses domain allowlisting to ensure the chatbot appears only on approved sites.
Core R A G Features
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
Error Tracking: Built-in retry mechanisms with detailed failure monitoring and debugging
Trigger History: Task completion logs track every workflow execution and result
Qualification Metrics: Lead conversion rates and response time tracking for sales/marketing workflows
Completion Rates: Workflow success measurement and handling time analysis
Weekly Digests: Automated email summaries of task usage delivered to administrators
Agent Evals: Benchmarking feature against quality standards with regression prevention
Log Retention: 1 day (Free tier - severely constrains troubleshooting) to 30+ days (Enterprise tier)
Audit Logs: User actions, data access, configuration changes tracked on Business/Enterprise plans
Export Capabilities: Available but SIEM integration specifics require sales confirmation
No RAG-Specific Metrics: Cannot track retrieval precision, recall, embedding quality, or vector similarity scores
Workflow-Centric: Focuses on output quality rather than retrieval precision - notable gap for RAG-specific monitoring vs platforms like LangSmith or Arize
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.
FlexCredits: For premium LLM usage on Enterprise Flex plan
Support tiers: Standard (24x5, included), Premium (24x7 critical, additional fee)
Dedicated CSMs: Assigned to enterprise accounts with regular business reviews
Free Plan: $0/month, 400 credits, 1M character knowledge base, basic automations with 100+ integrations
Pro Plan: $49.99/month, 5,000 credits, 20M character knowledge base, phone calls, full integrations, Lindy branding on embed
Business Plans: $199.99-$299.99/month, 20,000-30,000 credits, 50M character knowledge base, custom branding, 30+ languages, unlimited calls
Enterprise Plan: Custom pricing with SSO, SCIM provisioning, dedicated support, custom training
Additional Costs: Phone calls $0.19/minute (GPT-4o), team members $19.99/member/month (Pro/Business), custom automation building $500 one-time, credits $19-$1,199/month (10,000-1,000,000 credits)
Credit Consumption: Varies by model choice and complexity - larger models (Claude Sonnet 4.5) consume more credits than smaller models
Primary User Complaint: Unpredictable costs - credit depletion speed consistently frustrating in reviews, particularly for complex workflows with premium actions
Pricing Transparency Issue: Credit system creates forecasting difficulty vs fixed per-seat or usage-based pricing
Scalability: Character limits constrain large knowledge bases - 50M character cap on Business tier may limit enterprise deployments
Runs on straightforward subscriptions: Standard (~$99/mo), Premium (~$449/mo), and customizable Enterprise plans.
Gives generous limits—Standard covers up to 60 million words per bot, Premium up to 300 million—all at flat monthly rates.
View Pricing
Handles scaling for you: the managed cloud infra auto-scales with demand, keeping things fast and available.
Security & Privacy
Yes SOC 2 Type II (annual audits)
Yes ISO 27001
Yes HIPAA compliant
Yes GDPR compliant
Yes TX-RAMP Level 2
Note: No FedRAMP certification
AES-256 encryption at rest, TLS 1.2+ in transit
Single-tenant infrastructure
Zero data retention for LLMs - customer data never used for training
Cloud-Prem deployment: Customer-hosted in AWS or GCP for full data residency control
Active Data Governance: Continuous scanning with 100+ predefined infotypes (PII, PCI, M&A)
Customizable policies, auto-hide for sensitive content
Governance API for programmatic control
SOC 2 Type II: Certified by Johanson Group audit - independently validated security controls
HIPAA Compliant: Business Associate Agreement (BAA) available for healthcare deployments
GDPR Compliant: EU data protection and privacy rights compliance
PIPEDA Compliant: Canadian Personal Information Protection and Electronic Documents Act
CCPA Compliant: California Consumer Privacy Act compliance
No AI Training: Customer data NEVER used for AI model training - explicitly stated in privacy policy
Encryption: AES-256 at rest, TLS 1.2+ in transit for comprehensive data protection
Infrastructure: Google Cloud Platform hosting with multi-zone redundancy for high availability
Backups: Daily encrypted backups with secure key management
Access Controls: RBAC (Role-Based Access Control), MFA (Multi-Factor Authentication), Enterprise SSO via existing identity providers, SCIM provisioning for automated user lifecycle
Audit Logs: Track agent activity, data access, configuration changes - available on Business/Enterprise plans
Data Residency Limitation: US-based only - no explicit EU data residency option documented (enterprise inquiries required for region-specific deployments)
No ISO 27001: Information security management certification not documented
Protects data in transit with SSL/TLS and at rest with 256-bit AES encryption.
Holds SOC 2 Type II certification and complies with GDPR, so your data stays isolated and private.
Security Certifications
Offers fine-grained access controls—RBAC, two-factor auth, and SSO integration—so only the right people get in.
No- Code Interface & Usability
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
Exceptional Ease of Use: 4.9/5 G2 rating across 109+ reviews validates user-friendly design
Drag-and-Drop Builder: Visual workflow construction requires zero coding knowledge
Agent Builder ('Vibe Coding'): Create complex agents from natural language prompts in minutes
Setup Speed Advantage: 30 seconds with Lindy vs 15-60 minutes with Zapier/Make for equivalent workflows (user testimonials)
Pre-Built Templates: 100+ templates for sales outreach, meeting management, email triage, customer support, lead qualification, CRM updates
Natural Language Configuration: Describe automations in plain English through Agent Builder vs manual workflow construction
Role-Based Access Controls: Admins lock configurations and set credit allocation limits per user/team
Tradeoff Clarity: Exceptional ease-of-use for business users comes at cost of developer flexibility
No Technical Prerequisite: Operations teams can deploy sophisticated automations without IT department involvement
Developer Limitation: For custom RAG pipelines, retrieval optimization, or programmatic integration - Lindy offers no viable path
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.
Support & Ecosystem
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
Benchmarks: No published RAG accuracy, latency, or performance metrics available
Target Audience: Operations teams automating workflows vs developers building custom RAG applications
Use Case Mismatch: Comparing Lindy to CustomGPT.ai is architecturally misleading - fundamentally different product categories serving different user personas
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
Competitive Positioning
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 Unpredictability: Credit-based model most common user complaint - costs difficult to forecast vs fixed tiers
Data Residency Limitation: US-only hosting blocks EU customers with strict data localization requirements
Market Position: Competes with Zapier, Make, n8n for workflow automation budget vs RAG API platforms (CustomGPT.ai, Pinecone Assistant)
Use Case Fit: Exceptional for business users automating workflows without developers; poor fit for developers requiring programmatic RAG capabilities
Comparison Warning: Direct feature comparison with RAG-as-a-Service platforms is misleading - different product categories, target audiences, and value propositions
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 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
Default Model - Claude Sonnet 4.5: Primary LLM 'almost no one overrides' according to Anthropic case study - excels at navigating ambiguity in large context windows
Anthropic Claude Family: Sonnet 4.5 (default, best performance), Sonnet 3.7 (balanced), Haiku 3.5 (fast, cost-effective) with 200K token context windows
Claude Sonnet 4.5 Rationale: Selected for 'navigating ambiguity in large context windows' and handling 'deeply nested data structures requiring nuanced reasoning'
Business Impact: Lindy achieved 10x customer growth after implementing Claude as default LLM - significant competitive advantage
Model Switching: Each workflow action requires explicit model selection - no automatic routing based on query complexity or cost optimization
No Dynamic Model Routing: Cannot intelligently switch between models based on task requirements - manual configuration only vs AI-powered model selection
Limited Model Experimentation: No A/B testing capabilities or automatic model performance comparison across different LLMs
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
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
Search Fuzziness: 100 = pure semantic search (no file limit), lower values add keyword matching but limit to first 1,500 files - trade-off between precision and scale
Default Retrieval: 4 search results returned per query (adjustable up to 10 maximum) for context-aware responses
Document Processing: PDF, DOCX, XLSX, CSV, TXT, HTML with 20MB per-file size limit and automatic text extraction
Audio & Video: Full audio file support with automatic transcription, YouTube transcript extraction via dedicated action
Website Crawling: Single page or full-site crawling with automatic link following and sitemap discovery
Cloud Integration: Google Drive (shared drives), OneDrive, Dropbox, Notion, SharePoint, Intercom, Freshdesk with automatic 24-hour sync
Manual Refresh: 'Resync Knowledge Base' actions for immediate updates when 24-hour sync insufficient
Vector Database: NOT disclosed - no documentation mentions Pinecone, Chroma, Qdrant, or proprietary implementation
Embedding Models: Undocumented - no information about which embedding models power semantic search or customization options
Chunking Strategy: Not configurable - automatic text segmentation with undisclosed chunk size and overlap parameters
Hallucination Reduction: 'Agents on rails' philosophy constrains LLM behavior through predefined workflow steps - architectural constraints vs retrieval optimization
Learning Integration: Human feedback corrections embedded in vector storage for future retrieval improvement
CRITICAL LIMITATION - Black Box Implementation: RAG treated as opaque system - no transparency into vector similarity scores, embedding quality, retrieval mechanisms
CRITICAL LIMITATION - No Published Benchmarks: No RAG accuracy metrics, retrieval precision/recall scores, or latency measurements available
CRITICAL LIMITATION - No Developer Control: Cannot customize embedding models, similarity thresholds, reranking, or retrieval parameters
Enterprise Concern: Opacity may concern organizations requiring transparency into AI decision-making for compliance auditing or regulatory requirements
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 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)
Primary Use Case: No-code workflow automation for operations teams, sales teams, marketing teams requiring AI-powered task execution without developers
Sales Automation: Lead qualification with real-time scoring, email/phone validation, firmographic enrichment, CRM syncing (Salesforce, HubSpot, Pipedrive)
Customer Support: Email triage, ticket routing, FAQ responses, escalation workflows with human handoff and context transfer
Healthcare: Patient appointment scheduling, medical record processing (HIPAA-compliant), insurance verification, billing automation
Legal: Document review, contract analysis, case research, deadline tracking with confidentiality controls
Voice Agents (Gaia): Phone call automation with 30+ language support, call transcription in 50+ languages, call transfer to humans
Team Sizes: Individual contributors to enterprise teams (1-500+ users) - scales from solopreneurs to Fortune 500 companies
Industries: Technology, professional services, healthcare, legal, financial services, e-commerce, real estate - any industry with repetitive workflows
Implementation Speed: 30 seconds with Agent Builder ('vibe coding') vs 15-60 minutes with Zapier/Make - fastest setup in automation category
NOT Ideal For: Developers needing programmatic RAG APIs, custom retrieval pipeline tuning, embedding model experimentation, transparent RAG implementation details, organizations requiring EU data residency
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
Free Plan - $0/month: 400 credits, 1M character knowledge base, 100+ integrations, basic automations, 1-day log retention for evaluation
Pro Plan - $49.99/month: 5,000 credits, 20M character knowledge base, phone calls, full integrations, Lindy branding on embed, 7-day logs
Business Plan - $199.99-$299.99/month: 20,000-30,000 credits, 50M character knowledge base, custom branding, 30+ languages, unlimited calls, 30-day logs
Enterprise Plan - Custom Pricing: Unlimited credits/users, custom knowledge base limits, SSO, SCIM provisioning, dedicated support, custom SLAs, custom training
Additional Team Members: $19.99/member/month on Pro/Business plans for expanding user access and collaboration
Phone Calls: $0.19/minute using GPT-4o for voice interactions - additional cost on top of plan credits
Custom Automation Building: $500 one-time fee for professional automation development by Lindy team
Credit Add-Ons: $19-$1,199/month for 10,000-1,000,000 credits for high-volume usage beyond plan limits
Credit Consumption Variability: Varies by model choice (Claude Sonnet 4.5 vs Haiku 3.5), workflow complexity, premium actions - unpredictable costs
Billing Cycle: Monthly subscription with automatic renewal, credit rollover not documented (likely use-it-or-lose-it monthly)
Payment Methods: Credit card, Enterprise invoicing with wire transfer options for annual contracts
Comparison: vs Zapier ($19.99-$69/month), Make ($9-$29/month), n8n (self-hosted free) - Lindy premium pricing justified by AI capabilities
PRIMARY USER COMPLAINT - Unpredictable Costs: Credit depletion speed consistently frustrating in reviews - 'credits consumed quickly and unpredictably'
CRITICAL LIMITATION - Pricing Transparency: Credit system creates forecasting difficulty vs fixed per-seat or usage-based pricing - budget planning challenging
LIMITATION - Character Limits: 50M character cap on Business tier may limit large enterprise deployments vs unlimited competitors
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
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
Onboarding: Self-service for Free/Pro, guided onboarding for Business, white-glove implementation for Enterprise with custom training
User-Focused Resources: Strong for business user adoption with non-technical language, visual guides, practical examples
CRITICAL GAP - No Developer Documentation: No API reference, code samples, technical architecture documentation, OpenAPI specs
CRITICAL GAP - No Phone Support: Email and community only for Free/Pro/Business tiers - phone access restricted to Enterprise only
LIMITATION - Support Quality Inconsistency: User reviews note 'inconsistent responsiveness on lower tiers' - common Trustpilot criticism
LIMITATION - Slow Response Times: Some users report 'writing to support twice with no response' - support quality concerns for non-enterprise customers
Documentation hub: Rich docs, tutorials, cookbooks, FAQs, API references for rapid onboarding
Developer Docs
Email and in-app support: Quick support via email and in-app chat for all users
Premium support: Premium and Enterprise plans include dedicated account managers and faster SLAs
Code samples: Cookbooks, step-by-step guides, and examples for every skill level
API Documentation
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
NO Public REST API: Cannot manage agents, create workflows, or query knowledge base programmatically - blocks developer integration
NO Official SDKs: No Python, JavaScript, Ruby, Go, or any language SDK for programmatic access - workflow automation only
NO CLI Tools: No command-line interface for automation or scripting - dashboard-only management
NO Developer Console: No API sandbox, testing environment, or technical documentation for developers
Black Box RAG Implementation: Vector database, embedding models, similarity thresholds completely undisclosed - no transparency
No RAG Benchmarks: No published accuracy metrics, retrieval precision/recall, or latency measurements for evaluation
Search Fuzziness Constraint: Lower fuzziness values limit searches to first 1,500 files - meaningful limitation for large deployments
Character Storage Limits: 50M character maximum on Business tier - may constrain large enterprise knowledge bases vs unlimited competitors
Unpredictable Credit Consumption: Most common user complaint - 'credits depleted quickly and unpredictably' makes budgeting difficult
US-Only Data Residency: No documented EU data residency option - blocks customers with strict data localization requirements (GDPR, Digital Sovereignty)
No ISO 27001 Certification: Only SOC 2 Type II documented - ISO 27001 absence may limit enterprise procurement in regulated industries
1-Day Free Tier Log Retention: Severely limits troubleshooting and security incident investigation vs 30+ day industry standard
Learning Curve for Complex Workflows: Despite 'vibe coding' simplicity, sophisticated multi-agent systems and delegation rules require workflow design expertise
Support Quality Inconsistency: Mixed reviews noting slow/unresponsive support for non-enterprise tiers - support quality varies significantly by plan
No Manual Model Performance Comparison: Cannot A/B test different LLMs or compare model performance - manual experimentation required
Credit-Based Pricing Opacity: Difficult to forecast costs vs fixed per-seat or per-query pricing - budget planning challenging
NOT Ideal For: Developers needing RAG APIs, teams requiring transparent RAG implementation, EU data residency requirements, organizations needing predictable pricing, technical teams wanting embedding/retrieval control
Platform Category Mismatch: Fundamentally a workflow automation platform (competes with Zapier/Make) NOT a RAG-as-a-Service platform - architectural comparison to CustomGPT.ai is misleading
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
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
Agent Autonomy Focus: Differentiates through autonomous operation rather than traditional chatbot conversation functionality
Multi-Lingual Support: Voice agents (Gaia) support 30+ languages, transcription covers 50+ languages, text agents operate in 85+ languages with automatic detection
Lead Capture Excellence: Real-time qualification, email/phone validation, firmographic enrichment, UTM attribution, automatic CRM syncing - claims up to 70% higher conversion vs traditional forms
Human Handoff: Configurable escalation conditions with phone agents able to transfer calls directly to human team members with full context
Conversation Memory: Tracks conversation history within and across sessions through memory feature, but differs from typical RAG retrieval - context persists through workflow execution vs vector similarity search
Weekly Digests: Automated email summaries of task usage and agent performance
Agent Evals: Dedicated feature for benchmarking agent performance against quality standards and preventing regression
Workflow-Centric: Emphasizes autonomous task execution over conversational interaction - fundamentally different from chatbot platforms
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
Additional Considerations
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
Best Use Cases: Operations teams automating repetitive workflows without developer resources - lead qualification, email triage, meeting scheduling, CRM updates, customer support routing excel
Primary Strength: Zero-training deployment with Agent Builder ('vibe coding') creates sophisticated automations in 30 seconds vs 15-60 minutes with Zapier/Make for equivalent workflows
Unique Capabilities: Autopilot (Computer Use) enables automations impossible through traditional integrations - can interact with any web-based application without published APIs through AI-powered browser control
Multi-Agent Societies: Multiple specialized Lindies collaborate on complex tasks through delegation rules - Sales (SDR → AE → CS), Support (Triage → Technical → Escalation), Research with specialized investigators
Credit-Based Pricing Reality: Most common user complaint is unpredictable costs - 'credits consumed quickly and unpredictably' makes budget forecasting difficult vs fixed per-seat or usage-based pricing in competitors
Enterprise Limitations: Character limits (50M cap on Business tier) may constrain large deployments, US-only data residency blocks EU customers with strict localization requirements, no ISO 27001 certification may limit procurement
Developer Friction: Deliberately prioritizes no-code accessibility over developer tooling - NO public REST API, NO SDKs, NO CLI tools, NO programmatic RAG control makes it unsuitable for API-first use cases
Support Inconsistency: User reviews note 'inconsistent responsiveness on lower tiers' and 'writing to support twice with no response' - support quality varies significantly by plan tier
Platform Comparison Warning: Fundamentally different architecture from RAG-as-a-Service platforms - comparing Lindy to CustomGPT is misleading as they serve different product categories (workflow automation vs knowledge retrieval)
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.
Core Chatbot Features
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
Chatbot vs Agent Philosophy: Lindy differentiates through autonomous agent operation rather than traditional chatbot conversation - emphasizes task execution over conversational interaction
Multi-Lingual Voice Agents (Gaia): 30+ language support for voice agents, transcription covers 50+ languages, text agents operate in 85+ languages with automatic detection - no manual language configuration required
Lead Capture Excellence: Real-time qualification with email/phone validation, firmographic enrichment, UTM attribution tracking, automatic CRM syncing - claims up to 70% higher conversion vs traditional forms
Human Handoff Logic: Configurable escalation conditions with phone agents able to transfer calls directly to human team members with full conversation context and history preservation
Conversation Memory System: Tracks conversation history within and across sessions through memory feature - context persists through workflow execution vs vector similarity search in traditional RAG systems
Analytics & Performance Tracking: Qualification rates, response times, completion rates, handling times monitored comprehensively with weekly automated email summaries of task usage and agent performance
Agent Evals Feature: Dedicated benchmarking system for measuring agent performance against quality standards and preventing regression over time with automated quality monitoring
Workflow-Centric Design: Emphasizes autonomous task execution over conversational chatbot patterns - structured workflows with 'agents on rails' philosophy constraining LLM behavior through predefined steps
Hallucination Prevention: Architectural constraints vs retrieval optimization - 'poor man's RLHF' with human confirmation before action execution prevents costly mistakes
Learning Integration: Corrections from user feedback embedded in vector storage for future retrieval improvement - system learns from mistakes through Memory Snippets saving preferences like scheduling constraints
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.
Natural language configuration: Build and configure agents by describing goals in plain language without technical expertise
Visual builder option: Alternative drag-and-drop workflow creation for those preferring visual interface
AI-assisted creation: Glean suggests starting points and auto-generates draft agents based on description
Agent Library templates: 30+ prebuilt agents for sales, engineering, IT, HR use cases as starting points
Per-step model selection: Different LLMs for each workflow step with temperature controls (factual, balanced, creative)
Model tiers: Basic, Standard, Premium models with FlexCredits for premium consumption on Enterprise Flex
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
RBAC hierarchy: Setup Admin, Admin, Super Admin roles with granular permissions
Process knowledge integration: Glean uses underlying process knowledge to inform agent design and workflow optimization
Behavior Customization Layers: Settings Context (agent-level configuration persisting across all task runs), Per-Run Context (dynamic customization per execution for adaptive responses), Memory Snippets (learning preferences saved across sessions)
Workflow Flexibility: Visual builder allows business users to modify agent logic without coding - drag-and-drop interface for conversation flows, conditional logic, API integrations, data transformations
Agent Personality Configuration: Configurable tone, expertise areas, communication style through prompt configuration - define professional vs casual voice, technical depth, response verbosity
Knowledge Base Management: Automatic refresh every 24 hours for all connected cloud sources (Google Drive, OneDrive, Dropbox, Notion, SharePoint, Intercom, Freshdesk) with manual 'Resync Knowledge Base' actions for immediate updates
Search Fuzziness Controls: Configurable slider (0-100 scale) balancing semantic vs keyword search - at 100 (pure semantic) no file limit, lower values add keyword matching but constrain to 1,500 files
Retrieval Configuration: Default 4 search results returned (adjustable up to 10 maximum) with hybrid search combining semantic similarity and keyword matching for precision
RBAC Controls: Admins can lock configurations and set credit allocation limits per user or team - prevents unauthorized changes and controls spending across organization
CRITICAL LIMITATION - No Embedding Control: Cannot customize embedding models, vector similarity thresholds, or retrieval parameters - black-box RAG implementation prevents optimization of retrieval pipeline
Developer Flexibility Gap: No programmatic access to knowledge base management, no API for document upload or retrieval configuration, no ability to tune vector search parameters or chunking strategies
Lets you add, remove, or tweak content on the fly—automatic re-indexing keeps everything current.
Shapes agent behavior through system prompts and sample Q&A, ensuring a consistent voice and focus.
Learn How to Update Sources
Supports multiple agents per account, so different teams can have their own bots.
Balances hands-on control with smart defaults—no deep ML expertise required to get tailored behavior.
Customization & Flexibility
N/A
Knowledge Updates: Automatic refresh every 24 hours for all connected cloud sources
Manual Resync: 'Resync Knowledge Base' actions available for immediate updates when needed
Cloud Source Syncing: Google Drive, OneDrive, Dropbox, Notion, SharePoint, Intercom, Freshdesk automatically stay current
Settings Context: Agent-level configuration persists across all task runs for consistent behavior
Per-Run Context: Dynamic customization per execution allows adaptive agent responses
Memory Snippets: Learning preferences saved across sessions (e.g., scheduling constraints, communication style preferences)
Workflow Customization: Visual builder allows business users to modify agent logic without coding
Agent Personality: Configurable tone, expertise areas, and communication style through prompt configuration
No Embedding Control: Cannot customize embedding models, vector similarity thresholds, or retrieval parameters
Limited Developer Flexibility: Black-box RAG implementation prevents optimization of retrieval pipeline or tuning of vector search
N/A
Autopilot & Computer Use
N/A
Unique Capability: AI agents operate cloud-based virtual computers for any website/application interaction
No API Required: Enables automations impossible through traditional integrations - can interact with platforms without published APIs
Computer Vision: Agents 'see' and interact with UIs just like humans - click buttons, fill forms, navigate applications
Workflow Expansion: Breaks beyond 5,000+ integration catalog to access literally any web-based application
Use Cases: Legacy system automation, platforms without APIs, visual task completion, web scraping with context
After analyzing features, pricing, performance, and user feedback, both Glean and Lindy.ai are capable platforms that serve different market segments and use cases effectively.
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
When to Choose Lindy.ai
You value exceptional no-code usability: 4.9/5 g2 rating, 30-second setup vs 15-60 min with zapier/make
Massive integration ecosystem: 5,000+ apps via Pipedream Connect with 2,500+ pre-built actions
Claude Sonnet 4.5 default drives 10x customer growth - best-in-class language understanding
Best For: Exceptional no-code usability: 4.9/5 G2 rating, 30-second setup vs 15-60 min with Zapier/Make
Migration & Switching Considerations
Switching between Glean and Lindy.ai 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
Glean starts at $50/month, while Lindy.ai begins at custom pricing. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.
Our Recommendation Process
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 Glean and Lindy.ai 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 11, 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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