Deepset vs Glean

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

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Priyansh Khodiyar's avatar
Priyansh KhodiyarDevRel at CustomGPT.ai

Fact checked and reviewed by Bill Cava

Published: 01.04.2025Updated: 25.04.2025

In this comprehensive guide, we compare Deepset and Glean across various parameters including features, pricing, performance, and customer support to help you make the best decision for your business needs.

Overview

When choosing between Deepset and Glean, understanding their unique strengths and architectural differences is crucial for making an informed decision. Both platforms serve the RAG (Retrieval-Augmented Generation) space but cater to different use cases and organizational needs.

Quick Decision Guide

  • Choose Deepset if: you value mature open-source framework (since 2020)
  • Choose Glean if: you value permissions-aware ai is genuinely differentiated - real-time enforcement across 100+ datasources addresses critical enterprise concern

About Deepset

Deepset Landing Page Screenshot

Deepset is open-source framework and enterprise platform for llm orchestration. Deepset is the creator of Haystack, the leading open-source framework for building production-ready LLM applications, and offers an enterprise AI platform for developing and deploying custom AI agents and applications. Founded in 2018, headquartered in Berlin, Germany, the platform has established itself as a reliable solution in the RAG space.

Overall Rating
83/100
Starting Price
Custom

About Glean

Glean Landing Page Screenshot

Glean is enterprise work ai with permissions-aware rag across 100+ apps. Glean is a premium enterprise RAG platform with permissions-aware AI as its core differentiator. Founded by ex-Google Search engineers, Glean achieved $100M ARR in three years and a $7.2B valuation (2025). It connects 100+ enterprise apps with real-time access controls, supports 15+ LLMs, and offers comprehensive APIs with 4-language SDKs. Trade-offs: enterprise-only sales (~$50/user/month, ~$60K minimum), no consumer messaging channels, and premium positioning over plug-and-play simplicity. Founded in 2019, headquartered in Palo Alto, CA, the platform has established itself as a reliable solution in the RAG space.

Overall Rating
96/100
Starting Price
$50/mo

Key Differences at a Glance

In terms of user ratings, Glean in overall satisfaction. From a cost perspective, Deepset starts at a lower price point. The platforms also differ in their primary focus: AI Development Platform versus Enterprise RAG. These differences make each platform better suited for specific use cases and organizational requirements.

⚠️ What This Comparison Covers

We'll analyze features, pricing, performance benchmarks, security compliance, integration capabilities, and real-world use cases to help you determine which platform best fits your organization's needs. All data is independently verified from official documentation and third-party review platforms.

Detailed Feature Comparison

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Deepset
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Glean
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CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
  • Gives developers a flexible framework to wire up connectors and process nearly any file type or data source with libraries like Unstructured.
  • Lets you push content into vector stores such as OpenSearch, Pinecone, Weaviate, or Snowflake—pick the backend that fits best. Learn more
  • Setup is hands-on, but the payoff is deep, domain-specific customization of your ingestion pipelines.
  • 100+ native connectors covering major enterprise categories
  • Cloud Storage: Google Drive, SharePoint, OneDrive, Dropbox, Box
  • Communication: Slack, Microsoft Teams, Gmail, Outlook, Zoom
  • Collaboration: Confluence, Notion, Jira, GitHub, GitLab, Miro
  • CRM/Support: Salesforce, ServiceNow, Zendesk
  • Custom sources: Indexing API for proprietary systems, web crawling for internal sites
  • File formats: PDFs, Word documents, HTML, spreadsheets, structured data
  • Note: Video/YouTube ingestion not explicitly documented as core capability
  • Real-time sync: Content appears within minutes via API, permission changes reflect immediately
  • Initial indexing: Few days depending on data volume
  • Scale: 10,000-100,000 users managing hundreds of millions of documents
  • Metadata ingestion: Content, metadata, identity data, permissions, activity signals
  • Indexing API: 10 requests/second for bulk operations, ProcessAll limited to once per 3 hours
  • Lets you ingest more than 1,400 file formats—PDF, DOCX, TXT, Markdown, HTML, and many more—via simple drag-and-drop or API.
  • Crawls entire sites through sitemaps and URLs, automatically indexing public help-desk articles, FAQs, and docs.
  • Turns multimedia into text on the fly: YouTube videos, podcasts, and other media are auto-transcribed with built-in OCR and speech-to-text. View Transcription Guide
  • Connects to Google Drive, SharePoint, Notion, Confluence, HubSpot, and more through API connectors or Zapier. See Zapier Connectors
  • Supports both manual uploads and auto-sync retraining, so your knowledge base always stays up to date.
Integrations & Channels
  • API-first approach—drop the RAG system into your own app through REST endpoints or the Haystack SDK.
  • Shareable pipeline prototypes are great for demos, but production channels (Slack bots, web chat, etc.) need a bit of custom code. See prototype feature
  • Slack: Official Marketplace app, Gleanbot auto-responses, Real-Time Search API
  • Microsoft Teams: Native Teams app and agent integration
  • Zoom: Custom AI Companion integration
  • No WhatsApp: No native integration
  • No Telegram: No native integration
  • No Zapier: No native integration (different product "Glean.ly" exists)
  • Browser extensions: Chrome (300K+ users), Firefox, Safari, Edge with sidebar search, Command+J access
  • Web SDK embedding: Components for chat, search, autocomplete, recommendations
  • MCP Server: Single server URL enables integration with multiple AI assistants
  • Identity providers: Okta, Microsoft Entra ID, Google Workspace, OneLogin, Shibboleth, ADFS, Duo, Ping Federate
  • SSO protocols: OIDC (strongly recommended), SAML 2.0
  • Embeds easily—a lightweight script or iframe drops the chat widget into any website or mobile app.
  • Offers ready-made hooks for Slack, Zendesk, Confluence, YouTube, Sharepoint, 100+ more. Explore API Integrations
  • Connects with 5,000+ apps via Zapier and webhooks to automate your workflows.
  • Supports secure deployments with domain allowlisting and a ChatGPT Plugin for private use cases.
  • Hosted CustomGPT.ai offers hosted MCP Server with support for Claude Web, Claude Desktop, Cursor, ChatGPT, Windsurf, Trae, etc. Read more here.
  • Supports OpenAI API Endpoint compatibility. Read more here.
Core Chatbot Features
  • Builds RAG agents as modular pipelines—retriever + reader, plus optional rerankers or multi-step logic.
  • Multi-turn chat? Source attributions? Fine-grained retrieval tweaks? All possible with the right config. Pipeline overview
  • Advanced users can layer in tool use and external API calls for richer agent behavior.
  • Glean Chat interface: Primary interface for interacting with Glean Assistant offering familiar chat-like experience enabling natural conversations with company knowledge base
  • Multi-turn conversations: Supports conversational AI with natural language and context awareness maintaining context across conversation turns
  • Streaming responses: Real-time response streaming for better user experience with automatic source citations for transparency
  • Chatbot context understanding: Understands thread and sequence of conversations tracking references like "their" and "they" across multiple exchanges
  • Enterprise knowledge integration: Works across all company apps and knowledge sources including Microsoft 365, Google Workspace, Salesforce, Jira, GitHub and nearly 100 more applications
  • Personalization and security: Delivers answers highly customized to each user based on deep understanding of company content, employees, and activity while adhering to real-time enterprise data permissions and governance rules
  • Citation and transparency: Provides full linking to source information across documents, conversations and applications for transparency and trust
  • Simple chatbot API: Powerful tool for integrating conversational AI into products creating custom conversational interfaces leveraging Glean's AI capabilities
  • Use case flexibility: Build chatbots answering customer questions using help documentation, FAQs, knowledge bases or create internal tools helping employees find company policies, procedures, documentation
  • Reduces hallucinations by grounding replies in your data and adding source citations for transparency. Benchmark Details
  • Handles multi-turn, context-aware chats with persistent history and solid conversation management.
  • Speaks 90+ languages, making global rollouts straightforward.
  • Includes extras like lead capture (email collection) and smooth handoff to a human when needed.
Customization & Branding
  • No drag-and-drop theming here—you’ll craft your own front end if you need branded UI.
  • That also means full freedom to shape the visuals and conversational tone any way you like. Custom components
  • UI customization: Custom logos (wordmark + symbol), color schemes, background images
  • Home page: Configurable widgets and quick actions
  • Welcome messages and feature toggles
  • Custom subdomains: your-company.glean.com
  • Note: Complete white-labeling not documented - Glean branding may remain
  • Chat widget styling: CSS positioning, width/height, custom containers
  • Theme customization: Colors, borders, shadows
  • Domain restrictions, SSO enforcement, channel-specific response controls
  • Server-to-server auth: Enables SSO bypass when needed
  • 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.
L L M Model Options
  • Model-agnostic: plug in GPT-4, Llama 2, Claude, Cohere, and more—whatever works for you.
  • Switch models or embeddings through the “Connections” UI with just a few clicks. View supported models
  • 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
  • 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.
Developer Experience ( A P I & S D Ks)
  • Comprehensive REST API plus the open-source Haystack SDK for building, running, and querying pipelines.
  • Deepset Studio’s visual editor lets you drag-and-drop components, then export YAML for version control. Studio overview
  • Client API: Search, Chat, Agents, Documents, Collections, Answers, Shortcuts, Insights, Governance
  • Indexing API: Document operations, People/Teams, Permissions management
  • Official SDKs: Python (pip install glean), Java (Maven), Go, TypeScript
  • Web SDK: @gleanwork/web-sdk for embeddable components (chat, search, autocomplete, recommendations)
  • Python features: Async support, FastAPI/Django/Streamlit integrations
  • Java features: POJOs, fluent builders, Reactive Streams
  • Go features: Context-based, standard net/http
  • Authentication: OAuth 2.0 (recommended), user-scoped tokens, global tokens with X-Glean-ActAs impersonation
  • Indexing API auth: Glean-issued tokens only (OAuth not supported, Super Admin creation only)
  • Rate limits: Agent Runs 30/min, /indexdocument 10/sec, /processalldocuments once per 3 hours
  • Framework integrations: LangChain (langchain-glean), Agent Toolkit (OpenAI Assistants, CrewAI, Google ADK)
  • MCP Server: 5-minute setup for Claude Desktop, Cursor, VS Code, Windsurf, ChatGPT
  • Pre-built tools: glean_search, employee_search, calendar_search, gmail_search, code_search
  • Documentation: Excellent at developers.glean.com with OpenAPI specs, CodeSandbox demos
  • GitHub: github.com/gleanwork with SDK repositories and examples
  • Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat. API Documentation
  • Offers open-source SDKs—like the Python customgpt-client—plus Postman collections to speed integration. Open-Source SDK
  • Backs you up with cookbooks, code samples, and step-by-step guides for every skill level.
Performance & Accuracy
  • Tune for max accuracy with multi-step retrieval, hybrid search, and custom rerankers.
  • Mix and match components to hit your latency targets—even at large scale. Benchmark insights
  • 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
  • Hallucination prevention: RAG grounding, permission-aware retrieval, citation/source attribution
  • Note: Uptime SLA not publicly disclosed
  • Delivers sub-second replies with an optimized pipeline—efficient vector search, smart chunking, and caching.
  • Independent tests rate median answer accuracy at 5/5—outpacing many alternatives. Benchmark Results
  • Always cites sources so users can verify facts on the spot.
  • Maintains speed and accuracy even for massive knowledge bases with tens of millions of words.
Customization & Flexibility ( Behavior & Knowledge)
  • Build anything: multi-hop retrieval, custom logic, bespoke prompts—your pipeline, your rules.
  • Create multiple datastores, add role-based filters, or pipe in external APIs as extra tools. Component templates
  • 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
  • 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.
Pricing & Scalability
  • Start free in Deepset Studio, then move to usage-based Enterprise plans as you scale.
  • Deploy in cloud, hybrid, or on-prem setups to handle huge corpora and heavy traffic. Pricing overview
  • Note: No public pricing - enterprise sales only
  • Estimated cost: ~$45-50+ per user/month
  • Minimum ACV: ~$60K (approximately 100 users minimum)
  • Model: Per-seat, annual contracts
  • Free trial: Not available; paid POCs reportedly up to $70K
  • Renewal increases: 7-12% annually unless caps negotiated
  • 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
  • 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
  • SOC 2 Type II, ISO 27001, GDPR, HIPAA—you’re covered for enterprise compliance.
  • Choose cloud, VPC, or on-prem to keep data exactly where you need it. Security compliance
  • 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
  • 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.
Observability & Monitoring
  • Deepset Studio dashboard shows latency, error rates, resource use—everything you’d expect.
  • Detailed logs integrate with Prometheus, Splunk, and more for deep observability. Monitoring features
  • Insights Dashboard: DAU/WAU/MAU metrics, Search Session Satisfaction (SSAT)
  • Usage metrics: Searches and chats per user per week
  • Department-level filtering with 270-day data retention
  • Coverage metrics: Signups/employees percentage
  • Search behavior analysis: Popular queries, patterns
  • Content engagement metrics
  • Insights API: POST /rest/api/v1/insights for programmatic access
  • Comprehensive audit logging: User activity, access patterns, permission changes, LLM responses
  • SIEM export capability
  • Comes with a real-time analytics dashboard tracking query volumes, token usage, and indexing status.
  • Lets you export logs and metrics via API to plug into third-party monitoring or BI tools. Analytics API
  • Provides detailed insights for troubleshooting and ongoing optimization.
Support & Ecosystem
  • Lean on the Haystack open-source community (Discord, GitHub) or paid enterprise support. Community insights
  • Wide ecosystem of vector DBs, model providers, and ML tools means plenty of plug-ins and extensions.
  • Standard support: 24x5 (Mon-Fri) via portal, email, Slack Connect
  • Premium support: 24x7 (critical only) with additional fee
  • Dedicated CSMs: Enterprise accounts with hands-on onboarding
  • Documentation: Excellent at developers.glean.com
  • GitHub repositories: SDK examples and sample projects
  • Regular business reviews for enterprise customers
  • Supplies rich docs, tutorials, cookbooks, and FAQs to get you started fast. Developer Docs
  • Offers quick email and in-app chat support—Premium and Enterprise plans add dedicated managers and faster SLAs. Enterprise Solutions
  • Benefits from an active user community plus integrations through Zapier and GitHub resources.
Additional Considerations
  • Perfect for teams that need heavily customized, domain-specific RAG solutions.
  • Full control and future portability—but expect a steeper learning curve and more dev effort. More details
  • Cannot create content directly: Glean focuses purely on search and retrieval - not suitable for organizations needing content creation within platform
  • Platform designed for large organizations: Feature set and pricing optimized for large enterprises - smaller teams may find it overkill and less cost-effective
  • AI production challenges: 68% of organizations report moving only 30% or fewer AI experiments into full production highlighting persistent scaling difficulties beyond proof-of-concept
  • Integration complexity: Requires strategic overhaul of processes to ensure seamless technology incorporation into existing workflows
  • Change management: Overcoming resistance to change demands strong leadership and commitment to fostering innovation and adaptability environment
  • Data reliability monitoring: Potential for inaccuracies in AI outputs necessitates rigorous monitoring frameworks to ensure data reliability and trustworthiness
  • Cybersecurity concerns: As AI deployment expands, cybersecurity threats become more pronounced requiring enhanced protective measures for sensitive information
  • Bias in AI models: Models can inadvertently learn and replicate biases in training data leading to unfair or discriminatory outcomes particularly in hiring, customer service, legal decisions
  • Training investment required: Enterprises must invest in training workforce to effectively use AI tools - upskilling employees, hiring AI talent, or partnering with consultants
  • Security risks and shadow IT: Many organizations hesitate due to uncertainties from security risks and shadow IT - ad hoc generative AI adoption comes with heavy risks and costs
  • Slashes engineering overhead with an all-in-one RAG platform—no in-house ML team required.
  • Gets you to value quickly: launch a functional AI assistant in minutes.
  • Stays current with ongoing GPT and retrieval improvements, so you’re always on the latest tech.
  • Balances top-tier accuracy with ease of use, perfect for customer-facing or internal knowledge projects.
No- Code Interface & Usability
  • Deepset Studio offers low-code drag-and-drop, yet it's still aimed at developers and ML engineers.
  • Non-tech users may need help, and production UIs will be custom-built.
  • Natural language agent configuration: Describe goals in plain language
  • Visual builder: Drag-and-drop workflow creation
  • AI-assisted creation: Glean suggests starting points and auto-generates draft agents
  • Agent Library: Pre-built templates for common use cases
  • 30+ prebuilt agents: Sales, engineering, IT, HR use cases
  • RBAC hierarchy: Setup Admin, Admin, Super Admin with granular permissions
  • 4.8/5 ease of use rating on G2
  • Offers a wizard-style web dashboard so non-devs can upload content, brand the widget, and monitor performance.
  • Supports drag-and-drop uploads, visual theme editing, and in-browser chatbot testing. User Experience Review
  • Uses role-based access so business users and devs can collaborate smoothly.
Competitive Positioning
  • Market position: Developer-first RAG framework (Haystack) with enterprise cloud offering (Deepset Cloud) for heavily customized, domain-specific RAG solutions
  • Target customers: ML engineers and development teams needing deep RAG customization, enterprises requiring domain-specific solutions with modular pipeline architecture, and organizations wanting future portability with open-source foundation
  • Key competitors: LangChain/LangSmith, Contextual.ai, Dataworkz, Vectara.ai, and custom implementations using Pinecone/Weaviate
  • Competitive advantages: Open-source Haystack framework for full portability, model-agnostic with easy model switching via Connections UI, Deepset Studio visual pipeline editor with YAML export for version control, modular components (retriever, reader, reranker) for maximum flexibility, wide ecosystem of vector DB integrations (OpenSearch, Pinecone, Weaviate, Snowflake), and SOC 2/ISO 27001/GDPR/HIPAA compliance with cloud/VPC/on-prem deployment
  • Pricing advantage: Free Deepset Studio for development, then usage-based Enterprise plans; competitive for teams wanting deep customization without vendor lock-in; best value comes from open-source foundation enabling future migration if needed
  • Use case fit: Perfect for teams needing heavily customized, domain-specific RAG with multi-hop retrieval and custom rerankers, organizations requiring modular pipeline architecture for complex workflows, and ML engineers wanting developer-friendly APIs with future portability through open-source Haystack foundation
  • 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
  • Proven ROI: 141% ROI, $15.6M NPV, 110 hours saved per employee (Forrester)
  • Pricing barrier: ~$50/user/month with ~$60K minimum excludes SMBs
  • Enterprise focus: Security, governance, compliance over plug-and-play simplicity
  • Market position: Leading all-in-one RAG platform balancing enterprise-grade accuracy with developer-friendly APIs and no-code usability for rapid deployment
  • Target customers: Mid-market to enterprise organizations needing production-ready AI assistants, development teams wanting robust APIs without building RAG infrastructure, and businesses requiring 1,400+ file format support with auto-transcription (YouTube, podcasts)
  • Key competitors: OpenAI Assistants API, Botsonic, Chatbase.co, Azure AI, and custom RAG implementations using LangChain
  • Competitive advantages: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, SOC 2 Type II + GDPR compliance, full white-labeling included, OpenAI API endpoint compatibility, hosted MCP Server support (Claude, Cursor, ChatGPT), generous data limits (60M words Standard, 300M Premium), and flat monthly pricing without per-query charges
  • Pricing advantage: Transparent flat-rate pricing at $99/month (Standard) and $449/month (Premium) with generous included limits; no hidden costs for API access, branding removal, or basic features; best value for teams needing both no-code dashboard and developer APIs in one platform
  • Use case fit: Ideal for businesses needing both rapid no-code deployment and robust API capabilities, organizations handling diverse content types (1,400+ formats, multimedia transcription), teams requiring white-label chatbots with source citations for customer-facing or internal knowledge projects, and companies wanting all-in-one RAG without managing ML infrastructure
A I Models
  • Model-agnostic architecture: Supports GPT-4, GPT-3.5, Claude (Anthropic), Llama 2, Cohere, and 80+ model providers through unified interface
  • Easy model switching: Change models via Connections UI with just a few clicks without code changes
  • Embedding models: OpenAI, Cohere, Sentence Transformers, and custom embedding models supported
  • Multiple LLMs per pipeline: Use different models for different pipeline components (retrieval vs generation)
  • Custom model fine-tuning: Fine-tune on proprietary data for domain-specific terminology and accuracy
  • Baseline models available: Pre-configured with common models for quick prototyping
  • Model Hub supports 15+ LLMs across multiple hosting providers with per-step model selection
  • OpenAI: GPT-3.5, GPT-4 via OpenAI or Azure OpenAI endpoints
  • Google Vertex AI: Gemini 1.5 Pro with multimodal capabilities
  • Amazon Bedrock: Claude 3 Sonnet for high-accuracy enterprise use cases
  • Temperature controls: Factual, balanced, or creative output settings per workflow
  • Model tiers: Basic, Standard, Premium (premium consumes FlexCredits on Enterprise Flex plan)
  • Two access options: Glean Universal Key (managed) or Customer Key (BYOK) for data sovereignty
  • Zero data retention: Customer data never used for model training with automatic model updates
  • Automatic routing: Optimizes using best-in-class models per query type for accuracy and cost
  • Primary models: GPT-5.1 and 4 series from OpenAI, and Anthropic's Claude 4.5 (opus and sonnet) for enterprise needs
  • Automatic model selection: Balances cost and performance by automatically selecting the appropriate model for each request Model Selection Details
  • Proprietary optimizations: Custom prompt engineering and retrieval enhancements for high-quality, citation-backed answers
  • Managed infrastructure: All model management handled behind the scenes - no API keys or fine-tuning required from users
  • Anti-hallucination technology: Advanced mechanisms ensure chatbot only answers based on provided content, improving trust and factual accuracy
R A G Capabilities
  • Advanced RAG architecture: Multi-step retrieval, hybrid search (semantic + keyword), and custom rerankers for maximum accuracy
  • Modular pipeline design: Flexible retriever + reader + optional reranker components for customized workflows
  • Multi-hop retrieval: Chain multiple retrieval steps for complex queries requiring deep context
  • Vector database flexibility: OpenSearch, Pinecone, Weaviate, Snowflake, Qdrant, and more - choose your preferred backend
  • Benchmark-proven performance: Published performance metrics on MTEB and domain-specific benchmarks
  • Source attribution: Full citation tracking with document references and confidence scores
  • Haystack framework: Open-source foundation enables complete RAG customization and future portability
  • Hybrid search: Combines semantic (vector-based) and lexical (keyword) approaches for maximum accuracy
  • Knowledge Graph Framework: Proprietary anchors and signals across enterprise data with rich, scalable crawler
  • LLM Control Layer: Optimizes and controls LLM outputs with permission-safe document retrieval and ranking
  • Real-time permissions enforcement: Users only see authorized content with identity crawling and connector-level permission mirroring
  • Context-aware query rewriting: LLM determines optimal query set with enterprise-specific rewrites
  • Hallucination prevention: RAG grounding, permission-aware retrieval, citation/source attribution for every answer
  • 74% human-agreement rate on AI Evaluator benchmarks with 25% precision increases in customer case studies
  • 141% ROI over 3 years: $15.6M NPV for composite organizations, 110 hours saved per employee annually (Forrester)
  • Permissions-aware AI (unique): Real-time access control enforcement across all 100+ datasources - no competitor matches this capability
  • Core architecture: GPT-4 combined with Retrieval-Augmented Generation (RAG) technology, outperforming OpenAI in RAG benchmarks RAG Performance
  • Anti-hallucination technology: Advanced mechanisms reduce hallucinations and ensure responses are grounded in provided content Benchmark Details
  • Automatic citations: Each response includes clickable citations pointing to original source documents for transparency and verification
  • Optimized pipeline: Efficient vector search, smart chunking, and caching for sub-second reply times
  • Scalability: Maintains speed and accuracy for massive knowledge bases with tens of millions of words
  • Context-aware conversations: Multi-turn conversations with persistent history and comprehensive conversation management
  • Source verification: Always cites sources so users can verify facts on the spot
Use Cases
  • Domain-specific Q&A: Enterprise knowledge bases with specialized terminology requiring fine-tuned models
  • Research and analysis: Multi-hop retrieval for complex research questions across large document corpora
  • Technical documentation: Developer-focused RAG for code documentation, API references, and technical guides
  • Compliance and legal: HIPAA/GDPR-compliant RAG systems for regulated industries requiring on-prem deployment
  • Custom AI agents: Build specialized agents with external API calls, tool use, and multi-step reasoning
  • Enterprise search: Large-scale search across millions of documents with hybrid retrieval and reranking
  • Future-proof AI: Migrate between LLM providers, vector databases, and hosting options without vendor lock-in
  • Enterprise knowledge retrieval: Unified search across 100+ datasources (Google Drive, SharePoint, Confluence, Salesforce, Zendesk, GitHub, Slack) for 10K-100K user organizations
  • Permissions-aware search: Complex permission hierarchies requiring real-time enforcement - healthcare, finance, legal industries with sensitive data access controls
  • AI agents and automation: 30+ prebuilt agents for sales, engineering, IT, HR use cases with workflow automation capabilities
  • Developer-friendly RAG: Official SDKs (Python, Java, Go, TypeScript), LangChain integration, MCP Server for Claude Desktop/Cursor/VS Code
  • Active Data Governance: Continuous scanning with 100+ predefined infotypes (PII, PCI, M&A) and customizable policies with auto-hide
  • Cloud-Prem deployment: Customer-hosted in AWS or GCP for regulated industries requiring full data residency control
  • NOT suitable for: SMBs with <100 users or <$60K budgets, simple document Q&A without permission requirements, consumer messaging channels (WhatsApp, Telegram)
  • Customer support automation: AI assistants handling common queries, reducing support ticket volume, providing 24/7 instant responses with source citations
  • Internal knowledge management: Employee self-service for HR policies, technical documentation, onboarding materials, company procedures across 1,400+ file formats
  • Sales enablement: Product information chatbots, lead qualification, customer education with white-labeled widgets on websites and apps
  • Documentation assistance: Technical docs, help centers, FAQs with automatic website crawling and sitemap indexing
  • Educational platforms: Course materials, research assistance, student support with multimedia content (YouTube transcriptions, podcasts)
  • Healthcare information: Patient education, medical knowledge bases (SOC 2 Type II compliant for sensitive data)
  • Financial services: Product guides, compliance documentation, customer education with GDPR compliance
  • 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 certification: Annual audits ensuring enterprise security standards
  • ISO 27001 certification: International information security management compliance
  • GDPR compliance: European data protection regulation adherence with data sovereignty options
  • HIPAA compliance: Healthcare data protection standards for sensitive medical information
  • Flexible deployment: Cloud, hybrid, VPC, or on-premises deployment for complete data control
  • Data residency options: Choose where data is stored and processed (US, EU, on-prem)
  • No model training on customer data: Customer data never used to train third-party models
  • Audit trails: Comprehensive logging of all queries, retrievals, and system access
  • 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
  • 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
  • Deepset Studio (Free): Development environment with unlimited files and core features for prototyping
  • Enterprise pricing: Custom usage-based pricing based on queries, documents indexed, and compute resources
  • Deployment options pricing: Cloud (managed SaaS), hybrid, or on-premises with separate pricing tiers
  • No per-seat charges: Usage-based model scales with actual platform usage, not team size
  • Professional services: Optional consulting, integration support, and custom pipeline development available
  • Scaling flexibility: Enterprise plans handle huge corpora (millions of documents) and heavy traffic loads
  • Open-source advantage: Haystack framework free forever - only pay for managed cloud services if needed
  • 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
  • Renewal increases: 7-12% annually unless renewal caps negotiated upfront
  • FlexCredits (Enterprise Flex): For premium LLM usage with consumption-based billing
  • Support tiers: Standard (24x5, included) or Premium (24x7 critical, additional fee)
  • Dedicated CSMs: Assigned to enterprise accounts with regular business reviews and hands-on onboarding
  • Pricing barrier: Excludes SMBs and startups - targets Fortune 500 and mid-market enterprises with 1K-100K users
  • Standard Plan: $99/month or $89/month annual - 10 custom chatbots, 5,000 items per chatbot, 60 million words per bot, basic helpdesk support, standard security View Pricing
  • Premium Plan: $499/month or $449/month annual - 100 custom chatbots, 20,000 items per chatbot, 300 million words per bot, advanced support, enhanced security, additional customization
  • Enterprise Plan: Custom pricing - Comprehensive AI solutions, highest security and compliance, dedicated account managers, custom SSO, token authentication, priority support with faster SLAs Enterprise Solutions
  • 7-Day Free Trial: Full access to Standard features without charges - available to all users
  • Annual billing discount: Save 10% by paying upfront annually ($89/mo Standard, $449/mo Premium)
  • Flat monthly rates: No per-query charges, no hidden costs for API access or white-labeling (included in all plans)
  • Managed infrastructure: Auto-scaling cloud infrastructure included - no additional hosting or scaling fees
Support & Documentation
  • Haystack community: Active Discord server and GitHub community (14,000+ stars) with responsive maintainers
  • Enterprise support tiers: Email, Slack Connect channels, and dedicated support engineers for paid customers
  • Comprehensive documentation: docs.cloud.deepset.ai with tutorials, API references, and integration guides
  • Video tutorials: YouTube channel with pipeline building guides and best practices
  • GitHub examples: Open-source example projects and starter templates for common use cases
  • Integration ecosystem: Wide community of vector DB providers, model vendors, and tool developers
  • Professional services: Custom development, architecture consulting, and hands-on implementation support available
  • 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
  • Framework integrations: LangChain (langchain-glean), Agent Toolkit (OpenAI Assistants, CrewAI, Google ADK)
  • 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 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
  • Open-source resources: Python SDK (customgpt-client), Postman collections, GitHub integrations Open-Source SDK
  • Active community: User community plus 5,000+ app integrations through Zapier ecosystem
  • Regular updates: Platform stays current with ongoing GPT and retrieval improvements automatically
Limitations & Considerations
  • Steeper learning curve: Developer-first platform requires ML/engineering skills - not ideal for non-technical users
  • Custom UI required: No drag-and-drop chat widget - must build production interfaces from scratch
  • Hands-on setup: More initial configuration effort compared to plug-and-play SaaS platforms
  • Deepset Studio limitations: Visual editor still aimed at technical users - requires understanding of RAG concepts
  • Production readiness: Moving from Studio prototype to production deployment requires additional DevOps work
  • Enterprise costs: Usage-based pricing can become expensive at high query volumes without careful optimization
  • Best for technical teams: Maximum value requires ML engineers and developers - not suited for business users seeking no-code solutions
  • Integration effort: Native integrations like Slack bots require custom code vs turnkey options from competitors
  • High cost barrier: ~$50/user/month with ~$60K minimum ACV excludes SMBs, startups, and budget-conscious teams
  • No public pricing: Requires sales contact creating friction for evaluation and budget planning vs transparent competitors
  • Paid POCs: No free trial, POCs reportedly cost up to $70K for large enterprise pilots
  • Renewal increases: 7-12% annual price increases unless renewal caps negotiated upfront
  • NO FedRAMP certification: Not suitable for US federal government deployments
  • Limited consumer channels: No native WhatsApp, Telegram integrations - designed for internal enterprise use only
  • Complex implementation: Initial indexing takes "few days" depending on data volume, requires enterprise IT coordination
  • Cross-language queries in early access: English query finding Spanish documents still in testing phase
  • Best for: Large enterprises (1K-100K users) with complex permission hierarchies, $60K+ budgets, and need for permissions-aware AI across 100+ datasources
  • NOT suitable for: SMBs, startups, simple document Q&A without permission requirements, organizations prioritizing transparent pricing
  • Managed service approach: Less control over underlying RAG pipeline configuration compared to build-your-own solutions like LangChain
  • Vendor lock-in: Proprietary platform - migration to alternative RAG solutions requires rebuilding knowledge bases
  • Model selection: Limited to OpenAI (GPT-5.1 and 4 series) and Anthropic (Claude, opus and sonnet 4.5) - no support for other LLM providers (Cohere, AI21, open-source models)
  • Pricing at scale: Flat-rate pricing may become expensive for very high-volume use cases (millions of queries/month) compared to pay-per-use models
  • Customization limits: While highly configurable, some advanced RAG techniques (custom reranking, hybrid search strategies) may not be exposed
  • Language support: Supports 90+ languages but performance may vary for less common languages or specialized domains
  • Real-time data: Knowledge bases require re-indexing for updates - not ideal for real-time data requirements (stock prices, live inventory)
  • Enterprise features: Some advanced features (custom SSO, token authentication) only available on Enterprise plan with custom pricing
Core Agent Features
  • AI Agents with Haystack: Build LLM-powered autonomous agents that can reason, reflect, and act using tools, data, and critical introspection into their own decision-making processes Building Agents
  • Spectrum Approach: Combines structured workflows with autonomous capabilities - AI systems exist on a spectrum between linearity and autonomy based on decision-making capability needs Agentic Spectrum
  • Planning Mechanisms: Agents break tasks into steps using chain-of-thought or tree-of-thought planning, enabling complex multi-step reasoning and execution
  • Dynamic Routing: LLMs serve as "brains" of decision systems, using reasoning capabilities to evaluate and choose among multiple tools, courses of action, databases, and resources based on context and goals
  • Reflection & Self-Correction: Agents analyze intermediate results through reflection mechanisms, improving accuracy and adapting strategies based on outcomes
  • Tool Integration: Modular pipeline design allows agents to use retriever, reader, reranker components, external API calls, and custom tools for richer autonomous behavior
  • Agentic RAG Enhancement: Build agentic RAG pipelines in Deepset Studio that combine graphs, agentic properties, multimodal capabilities, and innovations to significantly reduce inaccurate or misleading information Agentic RAG Guide
  • Custom Workflows: Create anything from multi-hop retrieval to custom logic to bespoke prompts - modular components enable building specialized agents for domain-specific autonomous workflows
  • Autonomous AI agents: Agents use AI to understand tasks and take action on behalf of users from answering questions and retrieving information to executing work autonomously
  • Natural language agent builder: Build agents by describing desired output in simple natural language - Glean understands goal and designs complex multi-step workflows
  • Agentic reasoning engine: LLM-agnostic engine enables agents to go beyond retrieval and generation - powers sophisticated automation and decision-making by understanding outcomes, building multi-step plans, and using action library
  • 100+ native actions: Supports 100+ new native actions across Slack, Microsoft Teams, Salesforce, Jira, GitHub, Google Workspace and other applications
  • MCP host support: Gives agents dramatically larger surface area to operate across enterprise applications
  • Human-in-the-loop design: Agents can autonomously do work end-to-end with human review checkpoints - process customer support tickets, conduct research, prepare responses for employee review before execution
  • Vibe coding: Upgraded builder makes agent creation as simple as chatting - anyone (not just developers) can create and refine agents without understanding or interacting with code
  • Grounded in enterprise data: Autonomous agents grounded in most relevant authoritative information for confident work automation
  • Automatic agent triggering: Orchestrates agents automatically based on schedules or events and surfaces agent recommendations across enterprise
  • Visual and conversational workflow design: Turn ideas into structured workflows using simple natural language prompts or visual builder
  • Custom AI Agents: Build autonomous agents powered by GPT-4 and Claude that can perform tasks independently and make real-time decisions based on business knowledge
  • Decision-Support Capabilities: AI agents analyze proprietary data to provide insights, recommendations, and actionable responses specific to your business domain
  • Multi-Agent Systems: Deploy multiple specialized AI agents that can collaborate and optimize workflows in areas like customer support, sales, and internal knowledge management
  • Memory & Context Management: Agents maintain conversation history and persistent context for coherent multi-turn interactions View Agent Documentation
  • Tool Integration: Agents can trigger actions, integrate with external APIs via webhooks, and connect to 5,000+ apps through Zapier for automated workflows
  • Hyper-Accurate Responses: Leverages advanced RAG technology and retrieval mechanisms to deliver context-aware, citation-backed responses grounded in your knowledge base
  • Continuous Learning: Agents improve over time through automatic re-indexing of knowledge sources and integration of new data without manual retraining
R A G-as-a- Service Assessment
  • Platform Type: HYBRID RAG FRAMEWORK + CLOUD SERVICE - open-source Haystack foundation with enterprise Deepset Cloud offering for heavily customized, domain-specific RAG solutions
  • Core Architecture: Modular pipeline architecture with retriever + reader + optional reranker components, full control over embedding models, vector databases (OpenSearch, Pinecone, Weaviate, Snowflake), and chunking strategies
  • Agentic Capabilities: Build autonomous AI agents with planning, routing, reflection mechanisms using Haystack framework - supports agentic RAG pipelines with graphs and multimodal capabilities Agent Development
  • Developer Experience: Comprehensive REST API, open-source Haystack SDK, Deepset Studio visual pipeline editor with YAML export for version control - targets ML engineers and development teams Studio Overview
  • No-Code Capabilities: Deepset Studio offers drag-and-drop visual editor for pipeline building, but still aimed at developers and ML engineers - not accessible to non-technical users
  • Target Market: ML engineers and development teams needing deep RAG customization, enterprises requiring domain-specific solutions with modular pipeline architecture, organizations wanting future portability with open-source foundation
  • RAG Technology Leadership: Advanced RAG with multi-step retrieval, hybrid search (semantic + keyword), custom rerankers for maximum accuracy, model-agnostic support (GPT-4, Llama 2, Claude, Cohere, 80+ providers), and benchmark-proven performance on MTEB Benchmark Insights
  • Deployment Flexibility: Free Deepset Studio for development, usage-based Enterprise plans, cloud/VPC/on-prem deployment options, and SOC 2/ISO 27001/GDPR/HIPAA compliance with flexible data residency
  • Enterprise Readiness: SOC 2 Type II, ISO 27001, GDPR, HIPAA compliance, cloud/hybrid/on-prem deployment, no model training on customer data, and comprehensive audit trails
  • Use Case Fit: Perfect for teams needing heavily customized domain-specific RAG with multi-hop retrieval and custom rerankers, organizations requiring modular pipeline architecture for complex workflows, ML engineers wanting developer-friendly APIs with future portability
  • Open-Source Advantage: Haystack framework (14,000+ GitHub stars) free forever with full portability - only pay for managed Deepset Cloud services if needed, avoiding vendor lock-in
  • NOT Suitable For: Non-technical teams seeking turnkey chatbots, business users wanting no-code deployment, organizations needing pre-built chat widgets or Slack/WhatsApp integrations
  • Competitive Positioning: Competes with LangChain/LangSmith, Contextual.ai, Dataworkz - differentiates through open-source Haystack foundation, model-agnostic flexibility, visual pipeline editor, and wide vector DB ecosystem
  • Yes TRUE RAG PLATFORM - API-first architecture with comprehensive developer tools
  • Data source flexibility: Excellent (100+ native connectors, Indexing API)
  • LLM model options: Excellent (15+ models with per-step selection, BYOK)
  • API-first architecture: Excellent (Client + Indexing APIs, 4-language SDKs)
  • 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: TRUE RAG-AS-A-SERVICE PLATFORM - all-in-one managed solution combining developer APIs with no-code deployment capabilities
  • Core Architecture: Serverless RAG infrastructure with automatic embedding generation, vector search optimization, and LLM orchestration fully managed behind API endpoints
  • API-First Design: Comprehensive REST API with well-documented endpoints for creating agents, managing projects, ingesting data (1,400+ formats), and querying chat API Documentation
  • Developer Experience: Open-source Python SDK (customgpt-client), Postman collections, OpenAI API endpoint compatibility, and extensive cookbooks for rapid integration
  • No-Code Alternative: Wizard-style web dashboard enables non-developers to upload content, brand widgets, and deploy chatbots without touching code
  • Hybrid Target Market: Serves both developer teams wanting robust APIs AND business users seeking no-code RAG deployment - unique positioning vs pure API platforms (Cohere) or pure no-code tools (Jotform)
  • RAG Technology Leadership: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, proprietary anti-hallucination mechanisms, and citation-backed responses Benchmark Details
  • Deployment Flexibility: Cloud-hosted SaaS with auto-scaling, API integrations, embedded chat widgets, ChatGPT Plugin support, and hosted MCP Server for Claude/Cursor/ChatGPT
  • Enterprise Readiness: SOC 2 Type II + GDPR compliance, full white-labeling, domain allowlisting, RBAC with 2FA/SSO, and flat-rate pricing without per-query charges
  • Use Case Fit: Ideal for organizations needing both rapid no-code deployment AND robust API capabilities, teams handling diverse content types (1,400+ formats, multimedia transcription), and businesses requiring production-ready RAG without building ML infrastructure from scratch
  • Competitive Positioning: Bridges the gap between developer-first platforms (Cohere, Deepset) requiring heavy coding and no-code chatbot builders (Jotform, Kommunicate) lacking API depth - offers best of both worlds
Core R A G Features
N/A
  • Hybrid search: Combines semantic (vector-based) and lexical (keyword) approaches
  • Knowledge Graph Framework: Proprietary anchors and signals across enterprise data
  • Rich, Scalable Crawler: Permission rule synchronization at scale
  • LLM Control Layer: Optimizes and controls LLM outputs
  • RAG pipeline: Query Planning → Retrieval (permission-safe) → Generation (grounded, cited)
  • Hallucination prevention: RAG grounding, permission-aware retrieval, citation attribution
  • Context-aware query rewriting: LLM determines optimal query set with enterprise-specific rewrites
  • Permission-safe document retrieval with ranking
  • Grounded answers with source citations
N/A
Permissions- Aware A I ( Core Differentiator)
N/A
  • Real-time access control enforcement across all 100+ datasources
  • Identity crawling: Periodic capture of users, groups, memberships, permission models
  • Connector-level permission mirroring: Respects each source's native model (Salesforce, GDrive, etc.)
  • Real-time enforcement: Permission changes reflect immediately in search results
  • Technical implementation: allowedUsers/allowedGroups fields, nested group support
  • Debugging endpoints: Tools for troubleshooting permission issues
  • Zero-trust architecture: Users only see authorized content
  • Compliance-friendly: Meets regulatory requirements for data access control
N/A
Multi- Language & Localization
N/A
  • English: Full support (keyword search, semantic search, assistant, UI)
  • German: Full support
  • Japanese: Full support
  • French: Partial support
  • Spanish: Partial support
  • 20+ languages: Early access or keyword search only
  • Note: Cross-language queries in early access (e.g., English query finding Spanish documents)
N/A
Conversation & Agent Features
N/A
  • Conversation history: Thread tracking in Slack, History tab in interface
  • Version control: All agent versions automatically saved
  • Note: Lead capture not a native feature - designed for internal enterprise use
  • Note: Human handoff requires external integration - ClearFeed, ServiceNow, Zendesk escalation
N/A
Deployment Options
N/A
  • Cloud (SaaS): Standard deployment on Glean infrastructure
  • Cloud-Prem: Customer-hosted in AWS or GCP environment for full data residency control
  • Single-tenant architecture: Isolated infrastructure per customer
  • Browser extensions: Chrome, Firefox, Safari, Edge
  • Web SDK embedding: Custom integration in customer applications
N/A

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Final Thoughts

Final Verdict: Deepset vs Glean

After analyzing features, pricing, performance, and user feedback, both Deepset and Glean are capable platforms that serve different market segments and use cases effectively.

When to Choose Deepset

  • You value mature open-source framework (since 2020)
  • Production-ready from day one
  • Highly modular and customizable

Best For: Mature open-source framework (since 2020)

When to Choose Glean

  • You value permissions-aware ai is genuinely differentiated - real-time enforcement across 100+ datasources addresses critical enterprise concern
  • Strong developer experience - comprehensive APIs, 4-language SDKs (Python, Java, Go, TypeScript), LangChain integration
  • Model flexibility without vendor lock-in - 15+ LLMs with per-step selection and bring-your-own-key option

Best For: Permissions-aware AI is genuinely differentiated - real-time enforcement across 100+ datasources addresses critical enterprise concern

Migration & Switching Considerations

Switching between Deepset and Glean requires careful planning. Consider data export capabilities, API compatibility, and integration complexity. Both platforms offer migration support, but expect 2-4 weeks for complete transition including testing and team training.

Pricing Comparison Summary

Deepset starts at custom pricing, while Glean begins at $50/month. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.

Our Recommendation Process

  1. Start with a free trial - Both platforms offer trial periods to test with your actual data
  2. Define success metrics - Response accuracy, latency, user satisfaction, cost per query
  3. Test with real use cases - Don't rely on generic demos; use your production data
  4. Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
  5. Check vendor stability - Review roadmap transparency, update frequency, and support quality

For most organizations, the decision between Deepset and Glean comes down to specific requirements rather than overall superiority. Evaluate both platforms with your actual data during trial periods, focusing on accuracy, latency, ease of integration, and total cost of ownership.

📚 Next Steps

Ready to make your decision? We recommend starting with a hands-on evaluation of both platforms using your specific use case and data.

  • • Review: Check the detailed feature comparison table above
  • • Test: Sign up for free trials and test with real queries
  • • Calculate: Estimate your monthly costs based on expected usage
  • • Decide: Choose the platform that best aligns with your requirements

Last updated: December 14, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.

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Priyansh Khodiyar's avatar

Priyansh Khodiyar

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

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