In this comprehensive guide, we compare Glean and Langchain 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 Langchain, 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 Langchain if: you value most popular llm framework (72m+ downloads/month)
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 Langchain
Langchain is the most popular open-source framework for building llm applications. LangChain is a comprehensive AI development framework that simplifies building applications with LLMs through modular components, chains, and agent orchestration, offering both open-source tools and commercial platforms. Founded in 2022, headquartered in San Francisco, CA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
87/100
Starting Price
Custom
Key Differences at a Glance
In terms of user ratings, Glean in overall satisfaction. From a cost perspective, Langchain offers more competitive entry pricing. The platforms also differ in their primary focus: Enterprise RAG versus AI Framework. 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
Langchain
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
Indexing API: 10 requests/second for bulk operations, ProcessAll limited to once per 3 hours
Takes a code-first approach: plug in document-loader modules for just about any file type—from PDFs with PyPDF to CSV, JSON, or HTML via Unstructured.
Lets developers craft custom ingestion and indexing pipelines, so niche or proprietary data sources are no problem.
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
Is completely model-agnostic—swap between OpenAI, Anthropic, Cohere, Hugging Face, and more through the same interface.
Easily adjust parameters and pick your embeddings or vector DB (FAISS, Pinecone, Weaviate) in just a few lines of code.
Taps into top models—OpenAI’s GPT-4, GPT-3.5 Turbo, and even Anthropic’s Claude for enterprise needs.
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
Embeddings control: Via Indexing API and custom datasources
Performance benchmarks: Strong (Forrester TEI, customer case studies)
Permissions & governance: Best-in-class (real-time enforcement, Active Data Governance)
Best for: Large enterprises requiring permissions-aware RAG with compliance needs
Not ideal for: SMBs with budget constraints, teams needing consumer messaging channels
Platform Type: NOT RAG-AS-A-SERVICE - LangChain is an open-source framework/library for building RAG applications, not a managed service
Core Focus: Developer framework providing building blocks (chains, agents, retrievers) for custom RAG implementation - complete flexibility and control
No Managed Infrastructure: Unlike true RaaS platforms (CustomGPT, Vectara, Nuclia), LangChain provides code libraries not hosted infrastructure
Self-Deployment Required: Organizations must deploy, host, and manage all components - vector databases, LLM APIs, application servers all separate
Framework vs Platform: Comparison to RAG-as-a-Service platforms invalid - fundamentally different category (SDK/library vs managed platform)
LangSmith Exception: Only LangSmith (separate paid product $39+/month) provides managed observability/monitoring - not full RAG service
Best Comparison Category: Developer frameworks (LlamaIndex, Haystack) or direct LLM APIs (OpenAI, Anthropic) NOT managed RAG platforms
Use Case Fit: Development teams building custom RAG from ground up wanting maximum control vs organizations wanting turnkey RAG deployment
Infrastructure Responsibility: Users responsible for vector DB hosting (Pinecone, Weaviate), LLM API costs, scaling, monitoring, security - no managed service abstraction
Hosted Alternatives: For managed RAG-as-a-Service, consider CustomGPT, Vectara, Nuclia, or cloud vendor offerings (Azure AI Search, AWS Kendra)
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 barrier: ~$50/user/month with ~$60K minimum excludes SMBs
Enterprise focus: Security, governance, compliance over plug-and-play simplicity
Market position: Leading open-source framework for building LLM applications with the largest community building the future of LLM apps, plus enterprise offering (LangSmith) for observability and production deployment
Target customers: Developers and ML engineers building custom LLM applications, startups wanting maximum flexibility without vendor lock-in, and enterprises needing full control over LLM orchestration logic with model-agnostic architecture
Key competitors: Haystack/Deepset, LlamaIndex, OpenAI Assistants API, and custom-built solutions using direct LLM APIs
Competitive advantages: Open-source and free with no vendor lock-in, completely model-agnostic (OpenAI, Anthropic, Cohere, Hugging Face, etc.), largest LLM developer community with extensive tutorials and plugins, future portability enabling easy migration between providers, LangSmith for turnkey observability and debugging, and modular architecture enabling custom workflows with chains and agents
Pricing advantage: Framework is open-source and free; costs come only from chosen LLM APIs and infrastructure; LangSmith has separate pricing for observability/monitoring; best value for teams with development resources who want to minimize SaaS subscription costs and retain full control
Use case fit: Perfect for developers building highly customized LLM applications requiring specific workflows, teams wanting to avoid vendor lock-in with model-agnostic architecture, and organizations needing multi-step reasoning agents with tool use and external API calls that can't be achieved with turnkey platforms
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
Completely Model-Agnostic: Swap between any LLM provider through unified interface - no vendor lock-in or migration friction
OpenAI Integration: GPT-4, GPT-4 Turbo, GPT-3.5 Turbo, o1, o3 with full parameter control (temperature, max tokens, top-p)
Anthropic Claude: Claude 3 Opus, Claude 3.5 Sonnet, Claude 3 Haiku with extended context window support (200K tokens)
Google Gemini: Gemini Pro, Gemini Ultra, PaLM 2 for multimodal capabilities and cost-effective processing
Cohere: Command, Command-Light, Command-R for specialized enterprise use cases and retrieval-focused applications
Hugging Face Models: 100,000+ open-source models including Llama 2, Mistral, Falcon, BLOOM, T5 with local deployment options
Azure OpenAI: Enterprise-grade OpenAI models with Microsoft compliance, data residency, and dedicated capacity
AWS Bedrock: Claude, Llama, Jurassic, Titan models via AWS infrastructure with regional deployment
Self-Hosted Models: Run Llama.cpp, GPT4All, Ollama locally for complete data privacy and cost control
Custom Fine-Tuned Models: Integrate organization-specific fine-tuned models through adapter interfaces
Embedding Model Flexibility: OpenAI embeddings, Cohere embeddings, Hugging Face sentence transformers, custom embeddings
Model Switching: Change providers with minimal code changes - swap LLM configuration in single parameter
Multi-Model Pipelines: Use different models for different tasks (GPT-4 for reasoning, GPT-3.5 for simple queries) in same application
Future-Proof Architecture: New models integrate immediately through community contributions - no waiting for platform support
Primary models: GPT-4, GPT-3.5 Turbo from OpenAI, and Anthropic's Claude 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
RAG Framework Foundation: Purpose-built for retrieval-augmented generation with modular document loaders, text splitters, vector stores, retrievers, and chains
Document Loaders: 100+ loaders for PDF (PyPDF, PDFPlumber, Unstructured), CSV, JSON, HTML, Markdown, Word, PowerPoint, Excel, Notion, Confluence, GitHub, arXiv, Wikipedia
Text Splitters: Character-based, recursive character, token-based, semantic splitters with configurable chunk size (default 1000 chars) and overlap (default 200 chars)
Embedding Models: OpenAI embeddings (text-embedding-3-small/large), Cohere, Hugging Face sentence transformers, custom embeddings with full parameter control
Hybrid Search: Combine vector similarity with keyword search (BM25) through Elasticsearch or custom retrievers
RAG Evaluation: Integration with LangSmith for retrieval precision/recall, answer relevance, faithfulness metrics, human-in-the-loop evaluation
Custom Retrieval Pipelines: Build specialized retrievers for niche data formats or proprietary systems - complete flexibility
Multi-Vector Stores: Query multiple knowledge bases simultaneously with ensemble retrieval and weighted ranking
Developer Control: Full transparency and configurability of RAG pipeline vs black-box implementations - tune every parameter
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: Developers and ML engineers building production-grade LLM applications requiring custom workflows and complete control
Custom RAG Applications: Enterprise knowledge bases, semantic search engines, document Q&A systems, research assistants with proprietary data integration
Multi-Step Reasoning Agents: Customer support automation with tool use, data analysis agents with code execution, research agents with web search and synthesis
Chatbots & Conversational AI: Context-aware dialogue systems, multi-turn conversations with memory, personalized assistants with user history
Content Generation: Blog writing, marketing copy, product descriptions, documentation generation with brand voice customization
Data Processing: Structured data extraction from unstructured text, document classification, entity recognition, sentiment analysis at scale
Team Sizes: Individual developers to enterprise teams (1-500+ engineers) - scales with organizational complexity
Industries: Technology, finance, healthcare, legal, retail, education, media - any industry requiring custom LLM integration
Implementation Timeline: Basic prototype: hours to days, production application: weeks to months depending on complexity and team experience
NOT Ideal For: Non-technical users needing no-code interfaces, teams wanting fully managed solutions without development, organizations without in-house engineering resources, rapid prototyping without coding
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)
E-commerce: Product recommendations, order assistance, customer inquiries with API integration to 5,000+ apps via Zapier
SaaS onboarding: User guides, feature explanations, troubleshooting with multi-agent support for different teams
Security & Compliance
SOC 2 Type II certified: Annual audits ensuring enterprise security standards
ISO 27001 certified: International information security management compliance
HIPAA compliant: Healthcare data protection standards for sensitive medical information
GDPR compliant: European data protection regulation adherence with data subject rights
TX-RAMP Level 2 certified: Texas state government security standard
NO FedRAMP certification: Not authorized for US federal government use
AES-256 encryption at rest, TLS 1.2+ in transit with automatic key rotation
Single-tenant infrastructure: Isolated environment per customer for maximum security
Zero data retention for LLMs: Customer data never used for model training with formal agreements
Cloud-Prem deployment: Customer-hosted in AWS or GCP for complete data residency control
Active Data Governance: Continuous scanning with 100+ predefined infotypes (PII, PCI, M&A), customizable policies, auto-hide
Permissions-aware AI: Real-time access control enforcement with zero-trust architecture meeting regulatory requirements
Security Model: Framework is open-source library - security responsibility lies with deployment infrastructure and LLM provider selection
On-Premise Deployment: Deploy entirely within your own infrastructure (VPC, on-prem data centers) for maximum data sovereignty and air-gapped environments
Self-Hosted Models: Run Llama 2, Mistral, Falcon locally via Ollama/GPT4All - data never leaves your network for ultimate privacy
Data Privacy: No data sent to LangChain company unless using LangSmith - framework processes locally with chosen LLM provider
Encryption: Implement custom encryption at rest (AES-256 for databases) and in transit (TLS for API calls) based on deployment requirements
Authentication & Authorization: Build custom RBAC (Role-Based Access Control), integrate with existing IAM systems, SSO via SAML/OAuth
Audit Logging: Implement comprehensive logging of LLM calls, user queries, data access with custom retention policies
Secrets Management: Integration with AWS Secrets Manager, Azure Key Vault, HashiCorp Vault instead of hardcoded API keys
Compliance Framework Agnostic: Achieve SOC 2, ISO 27001, HIPAA, GDPR, CCPA compliance through proper deployment architecture - not platform-enforced
GDPR Compliance: Data minimization through ephemeral processing, right to deletion via custom data handling, consent management in application layer
HIPAA Compliance: Use Azure OpenAI or AWS Bedrock with BAAs, implement PHI anonymization, audit trails, encryption for healthcare applications
PII Management: Anonymize/pseudonymize PII before LLM processing - avoid storing sensitive data in vector databases or memory
Input Validation: Sanitize user inputs to prevent injection attacks, validate LLM outputs before execution, implement rate limiting
Security Best Practices: Principle of least privilege for API access, sandboxing for code execution agents, prompt filtering for manipulation detection
Vendor Risk Management: Choose LLM providers based on security posture - Azure OpenAI (enterprise SLAs), AWS Bedrock (AWS security), self-hosted (no vendor risk)
CRITICAL - DIY Security: No built-in security stack - teams must implement encryption, authentication, compliance tooling themselves vs managed platforms
Encryption: SSL/TLS for data in transit, 256-bit AES encryption for data at rest
SOC 2 Type II certification: Industry-leading security standards with regular third-party audits
Security Certifications
GDPR compliance: Full compliance with European data protection regulations, ensuring data privacy and user rights
Access controls: Role-based access control (RBAC), two-factor authentication (2FA), SSO integration for enterprise security
Data isolation: Customer data stays isolated and private - platform never trains on user data
Domain allowlisting: Ensures chatbot appears only on approved sites for security and brand protection
Secure deployments: ChatGPT Plugin support for private use cases with controlled access
Pricing & Plans
No public pricing - enterprise sales only with custom quotes
Estimated cost: ~$45-50+ per user/month based on third-party reports
Minimum ACV: ~$60K (approximately 100 users minimum for entry)
Per-seat model: Annual contracts based on number of users
No free trial: Paid POCs reportedly up to $70K for large enterprises
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
Framework - FREE (Open Source): LangChain library is completely free under MIT license - no usage limits, no subscription fees, unlimited commercial use
LangSmith Developer - FREE: 1 seat, 5,000 traces/month included, 14-day trace retention, community Discord support for development and testing
LangSmith Plus - $39/seat/month: Up to 10 seats, 10,000 traces/month included, email support, security controls, annotation queues for team collaboration
Total Cost of Ownership: Framework free + LLM API costs + infrastructure + developer time - highly variable based on usage and architecture
Cost Optimization Strategies: Use smaller models (GPT-3.5 vs GPT-4), implement caching, prompt compression, batch processing, self-hosted models for privacy-insensitive tasks
No Vendor Lock-In Savings: Switch between LLM providers freely - negotiate better API pricing, avoid sudden price increases from single vendor
Developer Time Investment: Initial setup: 1-4 weeks, ongoing maintenance: 10-20% of dev time for complex applications
ROI Calculation: Best value for teams with in-house developers wanting to minimize SaaS subscriptions and retain full control vs managed platforms ($500-5,000/month)
Hidden Costs: Developer salaries, learning curve, infrastructure management, monitoring/debugging tools, ongoing maintenance - factor into total budget
Pricing Transparency: Framework is free forever (MIT license), LangSmith pricing publicly documented, LLM costs from providers, infrastructure costs predictable
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
MCP Server: 5-minute setup for Claude Desktop, Cursor, VS Code, Windsurf, ChatGPT with pre-built tools
Regular business reviews: Quarterly check-ins for enterprise customers with strategic planning
Documentation Quality: Extensive official docs at python.langchain.com and js.langchain.com with tutorials, API reference, conceptual guides, integration examples
Getting Started Tutorials: Step-by-step guides for RAG, agents, chatbots, summarization, extraction covering 80% of common use cases
API Reference: Complete API documentation for every class, method, parameter with type signatures and usage examples
Conceptual Guides: Deep dives into chains, agents, memory, retrievers, callbacks explaining architectural patterns and best practices
Community Support: Active Discord server (50,000+ members), GitHub Discussions (7,000+ threads), Stack Overflow (3,000+ questions) for peer support
GitHub Repository: 100,000+ stars, 500+ contributors, weekly releases, public roadmap, transparent issue tracking for open development
Community Plugins: 700+ integrations contributed by community - vast ecosystem of tools, vector stores, LLMs, utilities
Video Tutorials: Official YouTube channel, community content creators, conference talks, webinars for visual learning
Rapid Changes: Frequent breaking changes in 2023-2024 as framework matured - documentation sometimes lagged behind code updates
Community Strengths: Largest LLM developer community means extensive peer support, Stack Overflow answers, third-party tutorials compensate for doc gaps
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
Requires Programming Skills: Python or JavaScript/TypeScript knowledge mandatory - no no-code interface or visual builders available
Excessive Abstraction: Critics cite "too many layers", "difficult to understand underlying code", "hard to modify low-level behavior" when customization needed
Dependency Bloat: Framework pulls in many extra libraries (100+ dependencies) - even basic features require excessive packages vs lightweight alternatives
Poor Documentation Quality: "Confusing and lacking key details", "omits default parameters", "too simplistic examples" according to developer reviews
API Instability: Frequent breaking changes throughout 2023-2024 as framework evolved - migration friction for production applications
Inflexibility for Complex Architectures: Abstractions "too inflexible" for advanced agent architectures like agents spawning sub-agents - forces design downgrades
Memory and Scalability Issues: Heavy reliance on in-memory operations creates bottlenecks for large volumes - not optimized for enterprise scale
Sequential Processing Latency: Chaining multiple operations introduces latency - no built-in parallelization for independent steps
Limited Big Data Integration: No native Apache Hadoop, Apache Spark support - requires custom loaders for big data environments
No Standard Data Types: Lacks common data format for LLM inputs/outputs - hinders integration with other libraries and frameworks
Learning Curve: Despite being "developer-friendly", extensive features and integrations overwhelming for beginners - weeks to months to master
No Observability by Default: Requires LangSmith integration ($39+/month) for debugging, monitoring, tracing - not included in free framework
Reliability Concerns: Users found framework "unreliable and difficult to fix" due to complex structure - production issues and maintainability risks
Framework Fragility: Unexpected production issues as applications become more complex - stability concerns for mission-critical systems
DIY Everything: Security, compliance, UI, monitoring, deployment all require custom development - high engineering overhead vs managed platforms
NOT Ideal For: Non-technical users, teams without Python/JS expertise, rapid prototyping without coding, organizations preferring managed services, projects needing stable APIs without breaking changes
When to Avoid: "When projects move beyond trivial prototypes" per critics who argue it becomes "a liability" due to complexity and productivity drag
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-4, GPT-3.5) and Anthropic (Claude) - 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
LangGraph Agentic Framework: Launched early 2024 as low-level, controllable agentic framework - 43% of LangSmith organizations now sending LangGraph traces since March 2024 release
Autonomous Decision-Making: Agents use LLMs to decide control flow of applications with spectrum of agentic capabilities - not wide-ranging AutoGPT-style but vertical, narrowly scoped agents
Tool Calling: 21.9% of traces now involve tool calls (up from 0.5% in 2023) - models autonomously invoke functions and external resources signaling agentic behavior
Multi-Step Workflows: Average steps per trace doubled from 2.8 (2023) to 7.7 (2024) - increasingly complex multi-step workflows becoming standard
Parallel Tool Execution: create_tool_calling_agent() works with any tool-calling model providing flexibility across different providers
Custom Cognitive Architectures: Highly controllable agents with custom architectures for production use - lessons learned from LangChain incorporated into LangGraph
Agent Types: ReAct agents (reasoning + acting), conversational agents with memory, plan-and-execute agents, multi-agent systems with specialized roles
External Resource Integration: Agents interact with databases, files, APIs, web search, and other external tools through function calling
Production-Ready (2024): Year agents started working in production at scale - narrowly scoped, highly controllable vs purely autonomous experimental agents
Top Use Cases: Research and summarization (58%), personal productivity/assistance (53.5%), task automation, data analysis with code execution
State Management: Comprehensive conversation memory, context preservation across multi-turn interactions, stateful agent workflows
Agent Monitoring: LangSmith provides debugging, monitoring, and tracing for agent decision-making and tool execution flows
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
Total freedom to pick and swap models, embeddings, and vector stores—great for fast-evolving solutions.
Can power innovative, multi-step, tool-using agents, but reaching enterprise-grade polish takes serious engineering time.
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
Provides retrieval-augmented QA chains that blend LLM answers with data fetched from vector stores.
Supports multi-turn dialogue through configurable memory modules; you’ll add source citations manually if you need them.
Lets you build agents that call external APIs or tools for more advanced reasoning.
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.
After analyzing features, pricing, performance, and user feedback, both Glean and Langchain 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 Langchain
You value most popular llm framework (72m+ downloads/month)
Extensive integration ecosystem (600+)
Strong developer community
Best For: Most popular LLM framework (72M+ downloads/month)
Migration & Switching Considerations
Switching between Glean and Langchain 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 Langchain 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 Langchain 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 4, 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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