In this comprehensive guide, we compare Cohere and Lindy.ai across various parameters including features, pricing, performance, and customer support to help you make the best decision for your business needs.
Overview
When choosing between Cohere and Lindy.ai, understanding their unique strengths and architectural differences is crucial for making an informed decision. Both platforms serve the RAG (Retrieval-Augmented Generation) space but cater to different use cases and organizational needs.
Quick Decision Guide
Choose Cohere if: you value industry-leading deployment flexibility: saas, vpc (<1 day), air-gapped on-premise with zero cohere infrastructure access - unmatched among major ai providers
Choose Lindy.ai if: you value exceptional no-code usability: 4.9/5 g2 rating, 30-second setup vs 15-60 min with zapier/make
About Cohere
Cohere is enterprise rag api platform with unmatched deployment flexibility. Enterprise-first RAG API platform founded 2019 by Transformer co-author Aidan Gomez with $1.54B raised at $7B valuation. Offers Command A (256K context), Embed v4.0 (multimodal), Rerank 3.5 (128K), and 100+ connectors via Compass. Unmatched deployment flexibility: SaaS, VPC, air-gapped on-premise with zero Cohere data access. SOC 2/ISO 27001/ISO 42001 certified. NO native chat widgets, Slack/WhatsApp integrations, or visual builders—API-first for developers building custom solutions. Token-based pricing from free trials to enterprise. Founded in 2019, headquartered in Toronto, Canada / San Francisco, CA, USA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
89/100
Starting Price
Custom
About Lindy.ai
Lindy.ai is ai-powered personal assistant for workflow automation. No-code AI agent platform positioning as 'AI employees' for workflow automation, NOT developer-focused RAG platform. 5,000+ integrations via Pipedream, Claude Sonnet 4.5 default, $5.1M revenue (Oct 2024), 4.9/5 G2 rating. Critical limitation: No public API or SDKs available. Founded in 2023, headquartered in San Francisco, CA, USA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
81/100
Starting Price
Custom
Key Differences at a Glance
In terms of user ratings, Cohere in overall satisfaction. From a cost perspective, pricing is comparable. The platforms also differ in their primary focus: RAG Platform versus AI Assistant. These differences make each platform better suited for specific use cases and organizational requirements.
⚠️ What This Comparison Covers
We'll analyze features, pricing, performance benchmarks, security compliance, integration capabilities, and real-world use cases to help you determine which platform best fits your organization's needs. All data is independently verified from official documentation and third-party review platforms.
Multimodal Embed v4.0: Images (PNG, JPEG, WebP, GIF) embedded alongside text - screenshots of PDFs, slide decks, business documents without text extraction pipelines
96 Images Per Batch: Embed Jobs API handles large-scale multimodal processing asynchronously
100+ Prebuilt Connectors: Google Drive, Slack, Notion, Salesforce, GitHub, Pinecone, Qdrant, MongoDB Atlas, Milvus (open-source on GitHub)
Build-Your-Own-Connector: Framework for custom data sources requiring development effort
Automatic Retraining: Connectors fetch documents at query time - source changes reflect immediately without reindexing (Command model retrained weekly)
Search Constraint: When search fuzziness drops below 100, searches limited to first 1,500 files - meaningful constraint for large enterprise deployments
Marketing vs Reality: Documentation claims 'no limit to data you can feed' but practical constraints exist around character limits and file counts
Lets you ingest more than 1,400 file formats—PDF, DOCX, TXT, Markdown, HTML, and many more—via simple drag-and-drop or API.
Crawls entire sites through sitemaps and URLs, automatically indexing public help-desk articles, FAQs, and docs.
Turns multimedia into text on the fly: YouTube videos, podcasts, and other media are auto-transcribed with built-in OCR and speech-to-text.
View Transcription Guide
Connects to Google Drive, SharePoint, Notion, Confluence, HubSpot, and more through API connectors or Zapier.
See Zapier Connectors
Supports both manual uploads and auto-sync retraining, so your knowledge base always stays up to date.
Integrations & Channels
Developer Frameworks: LangChain, LlamaIndex, Haystack official integrations for RAG orchestration
Zapier: 8,000+ app connections for workflow automation and third-party integrations
Webhooks: Full REST API support for custom real-time integrations
Cohere Toolkit: Open-source (3,150+ GitHub stars, MIT license) Next.js web app with SQL database, full customization access
CRITICAL: CRITICAL LIMITATION - NO Native Messaging: NO Slack chatbot widget, WhatsApp, Telegram, Microsoft Teams integrations for conversational deployment
North Platform Context: Connects to Slack/Teams as DATA SOURCES for retrieval, NOT messaging endpoints for chatbot deployment
CRITICAL: NO Embeddable Chat Widget: Requires custom development using SDKs or deploying Cohere Toolkit - no iframe/JavaScript widget out-of-box
Conservative Marketing: Platform claims '200+ integrations' but actually offers 5,000+ apps via Pipedream Connect partnership
Pre-Built Actions: 2,500+ ready-to-use actions across Pipedream integration ecosystem
Messaging Platforms: Slack (full integration with triggers/actions), WhatsApp (Personal/Business APIs with templates), Microsoft Teams, Telegram, Discord, Twilio SMS
CRM Systems: Salesforce (24 actions, 8 triggers with SOQL/SOSL queries), HubSpot (deep integration for contacts/tickets/deals), Pipedrive, Zoho CRM
Productivity Tools: Notion (16 actions, 7 triggers), Airtable (full CRUD with webhooks), Google Workspace (Gmail, Calendar, Docs, Sheets, Drive complete integration)
Embedding Options: Popup chat widgets, iFrame embeds, unique public links with domain restriction capabilities
Platform Deployment: Specific instructions available for Webflow, WordPress, Squarespace, Wix, Framer implementations
Webhook Support: Inbound webhooks trigger workflows via POST requests with bearer token authentication
HTTP Actions: Call external APIs from within workflows for custom integration needs
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.
Conversation History: Chat API chat_history parameter with prompt_truncation for context management, Cohere Toolkit SQL storage for persistence
Grounded Generation: Inline citations showing exact document spans that informed each response part - built-in hallucination reduction
Document-Level Security: Enterprise controls for access permissions on sensitive data
Compass Connectors: 100+ prebuilt integrations fetch data at query time for real-time knowledge access
CRITICAL: NO Lead Capture, Analytics Dashboards, or Human Handoff: Must implement at application layer - platform focuses on knowledge retrieval, NOT marketing automation or customer service escalation
Agent Autonomy Focus: Differentiates through autonomous operation rather than traditional chatbot conversation functionality
Multi-Lingual Support: Voice agents (Gaia) support 30+ languages, transcription covers 50+ languages, text agents operate in 85+ languages with automatic detection
Lead Capture Excellence: Real-time qualification, email/phone validation, firmographic enrichment, UTM attribution, automatic CRM syncing - claims up to 70% higher conversion vs traditional forms
Human Handoff: Configurable escalation conditions with phone agents able to transfer calls directly to human team members with full context
Conversation Memory: Tracks conversation history within and across sessions through memory feature, but differs from typical RAG retrieval - context persists through workflow execution vs vector similarity search
Weekly Digests: Automated email summaries of task usage and agent performance
Agent Evals: Dedicated feature for benchmarking agent performance against quality standards and preventing regression
Workflow-Centric: Emphasizes autonomous task execution over conversational interaction - fundamentally different from chatbot platforms
Custom AI Agents: Build autonomous agents powered by GPT-4 and Claude that can perform tasks independently and make real-time decisions based on business knowledge
Decision-Support Capabilities: AI agents analyze proprietary data to provide insights, recommendations, and actionable responses specific to your business domain
Multi-Agent Systems: Deploy multiple specialized AI agents that can collaborate and optimize workflows in areas like customer support, sales, and internal knowledge management
Memory & Context Management: Agents maintain conversation history and persistent context for coherent multi-turn interactions
View Agent Documentation
Tool Integration: Agents can trigger actions, integrate with external APIs via webhooks, and connect to 5,000+ apps through Zapier for automated workflows
Hyper-Accurate Responses: Leverages advanced RAG technology and retrieval mechanisms to deliver context-aware, citation-backed responses grounded in your knowledge base
Continuous Learning: Agents improve over time through automatic re-indexing of knowledge sources and integration of new data without manual retraining
White-Labeling: Fully supported via self-hosted deployments, NO Cohere branding required for API-built applications
System Prompts (Preambles): Structured Markdown for persona customization, tone, language preferences (American vs British English), formatting rules
Safety Modes: CONTEXTUAL (recommended), STRICT (more restrictive), OFF (no filtering) - granular control
Fine-Tuning via LoRA: Command R models with up to 16,384 tokens training context for domain-specific optimization
Playground: Visual model testing with parameter tuning, system message customization, 'View Code' export button
Cloud-Agnostic Deployment: Choose AWS, Azure, GCP, Oracle OCI, VPC, or on-premise with full control
CRITICAL: CRITICAL LIMITATION - NO Visual Agent Builder: Agent creation requires code via Python SDK - not accessible to non-technical users
CRITICAL: Limited RBAC: Owner (full access) and User (shared keys/models) roles only - NO granular permissions or custom roles
Widget Customization: Display name (e.g., 'Technical Support Assistant'), accent color for brand alignment, logo/icon upload for expanded/collapsed states
Messaging Customization: Custom greeting and callout messages for initial engagement prompts
Domain Restrictions: Specify allowed deployment domains for access control and security
White-Labeling Uncertainty: Documentation doesn't explicitly confirm complete Lindy branding removal - unclear if available outside enterprise agreements
No Deep CSS Control: Limited to essential branding elements vs full CSS customization or brandless deployments on standard plans
Persona Customization: Agent-level prompts define personality, tone, and expertise areas
Settings Context: Persists across all task runs for consistent agent behavior
Per-Run Context: Allows dynamic customization per execution for adaptive responses
Memory Snippets: Learning capability saves preferences like 'Don't schedule meetings before 11am' across all sessions
RBAC Controls: Admins can lock configurations and set credit allocation limits per user or team
Fully white-labels the widget—colors, logos, icons, CSS, everything can match your brand.
White-label Options
Provides a no-code dashboard to set welcome messages, bot names, and visual themes.
Lets you shape the AI’s persona and tone using pre-prompts and system instructions.
Uses domain allowlisting to ensure the chatbot appears only on approved sites.
L L M Model Options
Command A: 256K context, $2.50 in/$10.00 out per 1M tokens - most performant, complex RAG, agents, 2-GPU deployment, 75% faster than GPT-4o
Command A Reasoning (August 2025): First enterprise reasoning LLM with 256K context for multi-step problem solving
Command R: 128K context, $0.15 in/$0.60 out - simple RAG, cost-conscious apps (66x cheaper than Command A for output)
Command R7B: 128K context, $0.0375 in/$0.15 out - fastest, lowest cost for chatbots and simple tasks
Cost-Performance Flexibility: 66x price difference enables matching model to use case complexity for optimization
23 Optimized Languages: Command A supports English, French, Spanish, German, Japanese, Korean, Chinese, Arabic, and more
Fine-Tuning: LoRA for Command R models, up to 16,384 tokens training context for domain adaptation
CRITICAL: NO Automatic Model Routing: Developers must implement own logic for query complexity-based selection or use LangChain/third-party orchestration
Anthropic Claude: Sonnet 4.5 (default - 'almost no one overrides' per Anthropic case study), Sonnet 3.7, Haiku 3.5
Google Gemini: Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 2.0 Flash for varied performance/cost trade-offs
Default Selection Rationale: Claude Sonnet 4.5 excels at 'navigating ambiguity in large context windows' and handling 'deeply nested data structures requiring nuanced reasoning'
Business Impact: Lindy achieved 10x customer growth after implementing Claude as default LLM
Per-Action Granularity: Users manually select models per workflow step through visual builder interface
Credit Impact: Model selection affects credit consumption - larger models (Sonnet 4.5) consume more credits than smaller models (Haiku 3.5)
No Automatic Routing: No dynamic model switching or automatic model selection based on query complexity
Manual Configuration: Each workflow action requires explicit model selection vs intelligent automatic routing
Taps into top models—OpenAI’s GPT-5.1 series, GPT-4 series, and even Anthropic’s Claude for enterprise needs (4.5 opus and sonnet, etc ).
Automatically balances cost and performance by picking the right model for each request.
Model Selection Details
Uses proprietary prompt engineering and retrieval tweaks to return high-quality, citation-backed answers.
Handles all model management behind the scenes—no extra API keys or fine-tuning steps for you.
Developer Experience ( A P I & S D Ks)
Four Official SDKs: Python, TypeScript/JavaScript, Java, Go with comprehensive multi-cloud support
REST API v2: Chat, Embed, Rerank, Classify, Tokenize, Fine-tuning endpoints with OpenAPI specifications
Streaming Support: Server-Sent Events for real-time response rendering
Tool Use API: Multi-step reasoning with parallel execution capabilities for agent workflows
Native RAG: documents parameter in Chat API for grounded generation with inline citations
Structured Outputs: JSON Schema compliance for reliable parsing and validation
Interactive Documentation: docs.cohere.com with 'Try it' API testing, code examples in all SDKs, Playground 'View Code' export
LLM University (LLMU): Structured learning paths for LLM fundamentals, embeddings, deployment on AWS SageMaker
Cookbook Library: Practical code examples for agents, RAG, semantic search, summarization with working implementations
Cohere Toolkit (3,150+ GitHub Stars): Open-source Next.js foundation with MIT license for rapid application development
CRITICAL LIMITATION: Lindy deliberately prioritizes no-code accessibility over developer tooling - most significant gap for RAG platform comparison
NO Public REST API: Cannot manage agents, create workflows, or query knowledge base programmatically
NO GraphQL Endpoint: No alternative API architecture available for data querying
NO Official SDKs: No Python, JavaScript, Ruby, Go, or any other language SDK exists
NO OpenAPI/Swagger: No machine-readable API specification for automated client generation
NO CLI Tools: No command-line interface for automation or scripting
NO Developer Console: No API sandbox or testing environment available
Available Workarounds: Inbound webhooks (external systems trigger workflows via POST with bearer token), HTTP Request actions (call external APIs from workflows), Code Action (run Python/JavaScript in E2B sandboxes ~150ms startup), Callback URLs (bidirectional webhook communication)
Minimal GitHub Presence: github.com/lindy-ai contains only 3 repositories - build caching utility, ML engineer hiring challenge, no public SDKs or integration libraries
Documentation Quality: User-focused Lindy Academy with step-by-step tutorials, but NO API reference, code samples, or technical architecture documentation
Developer Path: For programmatic RAG control, custom retrieval pipelines, or embedding integration - Lindy offers no viable path forward
Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat.
API Documentation
North vs Competitors: Internal benchmarks claim superiority over Microsoft Copilot and Google Vertex AI on RAG accuracy
Hallucination Acknowledgment: Documentation candidly notes "RAG does not guarantee accuracy... RAG greatly reduces the risk but doesn't necessarily eliminate it altogether"
Automatic Retraining: Command model retrained weekly, connectors fetch at query time for immediate source updates without reindexing
Binary Embeddings: 8x storage reduction (1024 dim → 128 bytes) with minimal accuracy loss for large-scale deployments
Hybrid Search: Semantic + keyword search with configurable 'Search Fuzziness' (0-100 scale) - at 100 (pure semantic) no file limit, lower values add keyword matching but limit to 1,500 files
Default Results: 4 search results returned (adjustable up to 10 maximum)
Vector Database: NOT disclosed - no documentation mentions Pinecone, Chroma, Qdrant, or any specific vector store
Embedding Models: Undocumented - no information about which embedding models power semantic search
Hallucination Reduction: Architectural constraints vs retrieval optimization - 'poor man's RLHF' with human confirmation before action execution
Learning Integration: Corrections from feedback embedded in vector storage for future retrieval improvement
Structured Workflows: 'Agents on rails' philosophy constrains LLM behavior through predefined workflow steps
NO Published Benchmarks: No RAG accuracy metrics, retrieval precision/recall scores, or latency measurements available
Black Box Implementation: RAG treated as opaque system - no transparency into vector similarity scores, embedding quality, or retrieval mechanisms
Enterprise Concern: Opacity may concern organizations requiring transparency into AI decision-making for compliance or auditing
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.
Connector Customization: Build-Your-Own-Connector framework for non-standard data sources with full control
Multi-Cloud Deployment: Choose provider based on latency, cost, data residency, or compliance requirements
Document-Level Security: Enterprise controls for granular access permissions on sensitive knowledge
Behavior Customization Layers: Settings Context (agent-level configuration persisting across all task runs), Per-Run Context (dynamic customization per execution for adaptive responses), Memory Snippets (learning preferences saved across sessions)
Workflow Flexibility: Visual builder allows business users to modify agent logic without coding - drag-and-drop interface for conversation flows, conditional logic, API integrations, data transformations
Agent Personality Configuration: Configurable tone, expertise areas, communication style through prompt configuration - define professional vs casual voice, technical depth, response verbosity
Knowledge Base Management: Automatic refresh every 24 hours for all connected cloud sources (Google Drive, OneDrive, Dropbox, Notion, SharePoint, Intercom, Freshdesk) with manual 'Resync Knowledge Base' actions for immediate updates
Search Fuzziness Controls: Configurable slider (0-100 scale) balancing semantic vs keyword search - at 100 (pure semantic) no file limit, lower values add keyword matching but constrain to 1,500 files
Retrieval Configuration: Default 4 search results returned (adjustable up to 10 maximum) with hybrid search combining semantic similarity and keyword matching for precision
RBAC Controls: Admins can lock configurations and set credit allocation limits per user or team - prevents unauthorized changes and controls spending across organization
CRITICAL LIMITATION - No Embedding Control: Cannot customize embedding models, vector similarity thresholds, or retrieval parameters - black-box RAG implementation prevents optimization of retrieval pipeline
Developer Flexibility Gap: No programmatic access to knowledge base management, no API for document upload or retrieval configuration, no ability to tune vector search parameters or chunking strategies
Lets you add, remove, or tweak content on the fly—automatic re-indexing keeps everything current.
Shapes agent behavior through system prompts and sample Q&A, ensuring a consistent voice and focus.
Learn How to Update Sources
Supports multiple agents per account, so different teams can have their own bots.
Balances hands-on control with smart defaults—no deep ML expertise required to get tailored behavior.
Pricing & Scalability
Trial/Free: Rate-limited - 20 chat requests/min, 1,000 calls/month total for evaluation
Production Pay-Per-Token: Command A $2.50 in/$10.00 out, Command R+ $2.50 in/$10.00 out, Command R $0.15 in/$0.60 out, Command R7B $0.0375 in/$0.15 out per 1M tokens
66x Cost Difference: Command R7B output tokens 66x cheaper than Command A - match model to use case complexity
Embed v4.0: $0.12 per 1M tokens (text), $0.47 per 1M tokens (images) for multimodal embeddings
Rerank 3.5: $2.00 per 1,000 queries for production RAG reranking
Enterprise Custom Pricing: North platform, Compass, dedicated instances, private deployments, custom model development require sales engagement
NO Fixed Subscription Tiers: Pay-as-you-go token-based pricing for standard API usage - predictable based on volume
Production Unlimited Monthly: No monthly usage caps once on production tier - only per-minute rate limits (500 chat/min)
Free Plan: $0/month, 400 credits, 1M character knowledge base, basic automations with 100+ integrations
Pro Plan: $49.99/month, 5,000 credits, 20M character knowledge base, phone calls, full integrations, Lindy branding on embed
Business Plans: $199.99-$299.99/month, 20,000-30,000 credits, 50M character knowledge base, custom branding, 30+ languages, unlimited calls
Enterprise Plan: Custom pricing with SSO, SCIM provisioning, dedicated support, custom training
Additional Costs: Phone calls $0.19/minute (GPT-4o), team members $19.99/member/month (Pro/Business), custom automation building $500 one-time, credits $19-$1,199/month (10,000-1,000,000 credits)
Credit Consumption: Varies by model choice and complexity - larger models (Claude Sonnet 4.5) consume more credits than smaller models
Primary User Complaint: Unpredictable costs - credit depletion speed consistently frustrating in reviews, particularly for complex workflows with premium actions
Pricing Transparency Issue: Credit system creates forecasting difficulty vs fixed per-seat or usage-based pricing
Scalability: Character limits constrain large knowledge bases - 50M character cap on Business tier may limit enterprise deployments
Runs on straightforward subscriptions: Standard (~$99/mo), Premium (~$449/mo), and customizable Enterprise plans.
Gives generous limits—Standard covers up to 60 million words per bot, Premium up to 300 million—all at flat monthly rates.
View Pricing
Handles scaling for you: the managed cloud infra auto-scales with demand, keeping things fast and available.
Security & Privacy
SOC 2 Type II Certified: Annual audits with reports available under NDA via Trust Center
ISO 27001 Certified: Information Security Management System compliance
ISO 42001 Certified: AI Management System - industry-leading standard for AI governance
GDPR Compliant: Data Processing Addendums, EU data residency options for compliance
CCPA Compliant: California Consumer Privacy Act requirements met
UK Cyber Essentials: Government-backed cybersecurity certification
Zero Data Retention (ZDR): Available upon approval - enterprise customers opt out of training via dashboard
30-Day Deletion: Logged prompts and generations deleted after 30 days automatically
Third-Party Content: Google Drive and other connected app content NEVER used for model training automatically
Encryption: TLS in transit, AES-256 at rest for comprehensive data protection
Air-Gapped Deployment: Full private on-premise deployment behind customer firewall with ZERO Cohere access to infrastructure or data
VPC Deployment: <1 day setup within customer virtual private cloud for network isolation
Document-Level Security: Enterprise controls for granular access permissions on sensitive knowledge
CRITICAL: NO HIPAA Certification: Healthcare organizations processing PHI must verify compliance with sales team - no explicit BAA documentation like competitors
SOC 2 Type II: Certified by Johanson Group audit - independently validated security controls
HIPAA Compliant: Business Associate Agreement (BAA) available for healthcare deployments
GDPR Compliant: EU data protection and privacy rights compliance
PIPEDA Compliant: Canadian Personal Information Protection and Electronic Documents Act
CCPA Compliant: California Consumer Privacy Act compliance
No AI Training: Customer data NEVER used for AI model training - explicitly stated in privacy policy
Encryption: AES-256 at rest, TLS 1.2+ in transit for comprehensive data protection
Infrastructure: Google Cloud Platform hosting with multi-zone redundancy for high availability
Backups: Daily encrypted backups with secure key management
Access Controls: RBAC (Role-Based Access Control), MFA (Multi-Factor Authentication), Enterprise SSO via existing identity providers, SCIM provisioning for automated user lifecycle
Audit Logs: Track agent activity, data access, configuration changes - available on Business/Enterprise plans
Data Residency Limitation: US-based only - no explicit EU data residency option documented (enterprise inquiries required for region-specific deployments)
No ISO 27001: Information security management certification not documented
Protects data in transit with SSL/TLS and at rest with 256-bit AES encryption.
Holds SOC 2 Type II certification and complies with GDPR, so your data stays isolated and private.
Security Certifications
Offers fine-grained access controls—RBAC, two-factor auth, and SSO integration—so only the right people get in.
Observability & Monitoring
Native Dashboard: Billing and usage tracking, API key management, spending limits, token counts per response
North Platform: Audit-ready logs, traceability for enterprise compliance workflows
API Response Metadata: Token counts, billed units included in every API response for tracking
Error Tracking: Built-in retry mechanisms with detailed failure monitoring and debugging
Trigger History: Task completion logs track every workflow execution and result
Qualification Metrics: Lead conversion rates and response time tracking for sales/marketing workflows
Completion Rates: Workflow success measurement and handling time analysis
Weekly Digests: Automated email summaries of task usage delivered to administrators
Agent Evals: Benchmarking feature against quality standards with regression prevention
Log Retention: 1 day (Free tier - severely constrains troubleshooting) to 30+ days (Enterprise tier)
Audit Logs: User actions, data access, configuration changes tracked on Business/Enterprise plans
Export Capabilities: Available but SIEM integration specifics require sales confirmation
No RAG-Specific Metrics: Cannot track retrieval precision, recall, embedding quality, or vector similarity scores
Workflow-Centric: Focuses on output quality rather than retrieval precision - notable gap for RAG-specific monitoring vs platforms like LangSmith or Arize
Comes with a real-time analytics dashboard tracking query volumes, token usage, and indexing status.
Lets you export logs and metrics via API to plug into third-party monitoring or BI tools.
Analytics API
Provides detailed insights for troubleshooting and ongoing optimization.
Support & Ecosystem
Discord Community: 21,691+ members with API discussions, troubleshooting, 'Maker Spotlight' developer sessions
Cohere Labs: 4,500+ research community members, 100+ publications including Aya multilingual model (101 languages)
Interactive Documentation: docs.cohere.com with 'Try it' API testing, code examples in all SDKs, Playground code export
LLM University (LLMU): Structured learning paths for fundamentals, embeddings, AWS SageMaker deployment
Cookbook Library: Practical working examples for agents, RAG, semantic search, summarization
Trust Center: SOC 2 Type II reports (requires NDA), penetration test reports, Data Processing Addendums
Rerank 3.5 Integration: 128K context window filters emails, tables, JSON, code to most relevant passages
Native RAG API: documents parameter in Chat API enables grounded generation without external orchestration
Transparent Limitations: Documentation candidly states "RAG does not guarantee accuracy... RAG greatly reduces the risk but doesn't necessarily eliminate it altogether"
Competitive Advantage: Most RAG platforms require custom citation implementation - Cohere provides built-in with Command models
N/A
N/A
Multimodal Embed v4.0 ( Differentiator)
Text + Images: Single vectors combining text and images eliminate complex extraction pipelines
96 Images Per Batch: Embed Jobs API handles large-scale multimodal processing asynchronously
Document Understanding: Embed screenshots of PDFs, slide decks, business documents without OCR or text extraction
Matryoshka Learning: Flexible dimensionality (256/512/1024/1536) for cost-performance optimization
100+ Languages: Cross-lingual retrieval without translation for global content
Binary Embeddings: 8x storage reduction (1024 dim → 128 bytes) for large-scale vector databases
Deployment Flexibility: SaaS, VPC, air-gapped on-premise - unmatched among major AI providers for enterprise control
CRITICAL: CRITICAL GAPS vs No-Code Platforms: NO native chat widgets, Slack/WhatsApp integrations, visual agent builders, analytics dashboards
Comparison Validity: Architectural comparison to CustomGPT.ai is VALID but highlights different priorities - Cohere backend API infrastructure vs CustomGPT likely more accessible deployment tools
Use Case Fit: Enterprises with developer resources building custom RAG integrations, regulated industries requiring air-gapped deployment, multilingual global knowledge retrieval
Platform Type: NOT A RAG-AS-A-SERVICE PLATFORM - No-code AI agent/workflow automation platform targeting business users vs developers
Critical Distinction: Lindy prioritizes business user accessibility over programmatic RAG control - fundamentally different design philosophy
RAG Implementation: Black-box hybrid search (semantic + keyword) with configurable fuzziness but no exposed retrieval controls
Vector Database: Undisclosed - no documentation of Pinecone, Chroma, Qdrant, or specific vector store
Embedding Models: Undocumented - no information about which models power semantic search
API Availability: NO public REST API, GraphQL endpoint, or official SDKs for programmatic access
Developer Tools: NO OpenAPI spec, CLI tools, developer console, API sandbox, or technical documentation
Benchmarks: No published RAG accuracy, latency, or performance metrics available
Target Audience: Operations teams automating workflows vs developers building custom RAG applications
Use Case Mismatch: Comparing Lindy to CustomGPT.ai is architecturally misleading - fundamentally different product categories serving different user personas
Core Architecture: Serverless RAG infrastructure with automatic embedding generation, vector search optimization, and LLM orchestration fully managed behind API endpoints
API-First Design: Comprehensive REST API with well-documented endpoints for creating agents, managing projects, ingesting data (1,400+ formats), and querying chat
API Documentation
Developer Experience: Open-source Python SDK (customgpt-client), Postman collections, OpenAI API endpoint compatibility, and extensive cookbooks for rapid integration
No-Code Alternative: Wizard-style web dashboard enables non-developers to upload content, brand widgets, and deploy chatbots without touching code
Hybrid Target Market: Serves both developer teams wanting robust APIs AND business users seeking no-code RAG deployment - unique positioning vs pure API platforms (Cohere) or pure no-code tools (Jotform)
RAG Technology Leadership: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, proprietary anti-hallucination mechanisms, and citation-backed responses
Benchmark Details
Deployment Flexibility: Cloud-hosted SaaS with auto-scaling, API integrations, embedded chat widgets, ChatGPT Plugin support, and hosted MCP Server for Claude/Cursor/ChatGPT
Enterprise Readiness: SOC 2 Type II + GDPR compliance, full white-labeling, domain allowlisting, RBAC with 2FA/SSO, and flat-rate pricing without per-query charges
Use Case Fit: Ideal for organizations needing both rapid no-code deployment AND robust API capabilities, teams handling diverse content types (1,400+ formats, multimedia transcription), and businesses requiring production-ready RAG without building ML infrastructure from scratch
Competitive Positioning: Bridges the gap between developer-first platforms (Cohere, Deepset) requiring heavy coding and no-code chatbot builders (Jotform, Kommunicate) lacking API depth - offers best of both worlds
Competitive Positioning
Market Position: Enterprise-first RAG API platform with unmatched deployment flexibility and security certifications
Deployment Differentiator: Air-gapped on-premise option with ZERO Cohere data access vs SaaS-only competitors (OpenAI, Anthropic, Google)
Security Leadership: SOC 2 + ISO 27001 + ISO 42001 (AI Management System - rare) + GDPR + CCPA + UK Cyber Essentials
Multimodal Strength: Embed v4.0 text + images in single vectors, 96 images/batch vs text-only competitors
Multilingual Excellence: 100+ languages (Embed/Rerank), 23 optimized (Command A) with cross-lingual retrieval
Cost Optimization: Command R7B 66x cheaper than Command A enables matching model to use case complexity
Research Pedigree: Founded by Transformer co-author Aidan Gomez with $1.54B funding, major enterprise customers (RBC, Dell, Oracle, LG)
vs. CustomGPT: Cohere superior RAG technology + enterprise security + deployment flexibility vs likely more accessible no-code tools from CustomGPT
vs. OpenAI: Cohere air-gapped deployment + enterprise focus vs OpenAI consumer accessibility
vs. Anthropic: Cohere deployment flexibility + multimodal embeddings vs Anthropic Claude quality
vs. Chatling/Jotform: Cohere API-first developer platform vs no-code SMB chatbot tools - fundamentally different markets
vs. Progress: Cohere enterprise deployment + citations vs Progress REMi quality monitoring + open-source NucliaDB
CRITICAL: SMB Accessibility Gap: NO chat widgets, visual builders, omnichannel messaging disqualifies Cohere for non-technical teams vs Chatling, Jotform, Drift
CRITICAL: HIPAA Gap: No explicit certification vs competitors with documented BAA - healthcare requires sales verification
Primary Advantage: Exceptional no-code usability (4.9/5 G2) with 5,000+ integrations via Pipedream and Autopilot (Computer Use) unique capabilities
Claude Sonnet 4.5 Default: Best-in-class language understanding driving 10x customer growth - 'almost no one overrides' per Anthropic
Multi-Agent Sophistication: Societies of Lindies enable complex task delegation impossible with single-bot platforms
Strong Compliance: SOC 2 Type II, HIPAA with BAA, GDPR, PIPEDA, CCPA enables regulated industry adoption
Financial Validation: $5.1M revenue (Oct 2024), $50M+ funding from Menlo Ventures, Battery Ventures, Coatue validates market fit
Setup Speed: 30 seconds vs 15-60 minutes with Zapier/Make - dramatic productivity advantage for business users
Primary Challenge: NOT a developer-focused RAG platform - no API, no SDKs, opaque RAG implementation blocks technical evaluation
Pricing Unpredictability: Credit-based model most common user complaint - costs difficult to forecast vs fixed tiers
Data Residency Limitation: US-only hosting blocks EU customers with strict data localization requirements
Market Position: Competes with Zapier, Make, n8n for workflow automation budget vs RAG API platforms (CustomGPT.ai, Pinecone Assistant)
Use Case Fit: Exceptional for business users automating workflows without developers; poor fit for developers requiring programmatic RAG capabilities
Comparison Warning: Direct feature comparison with RAG-as-a-Service platforms is misleading - different product categories, target audiences, and value propositions
Market position: Leading all-in-one RAG platform balancing enterprise-grade accuracy with developer-friendly APIs and no-code usability for rapid deployment
Target customers: Mid-market to enterprise organizations needing production-ready AI assistants, development teams wanting robust APIs without building RAG infrastructure, and businesses requiring 1,400+ file format support with auto-transcription (YouTube, podcasts)
Key competitors: OpenAI Assistants API, Botsonic, Chatbase.co, Azure AI, and custom RAG implementations using LangChain
Competitive advantages: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, SOC 2 Type II + GDPR compliance, full white-labeling included, OpenAI API endpoint compatibility, hosted MCP Server support (Claude, Cursor, ChatGPT), generous data limits (60M words Standard, 300M Premium), and flat monthly pricing without per-query charges
Pricing advantage: Transparent flat-rate pricing at $99/month (Standard) and $449/month (Premium) with generous included limits; no hidden costs for API access, branding removal, or basic features; best value for teams needing both no-code dashboard and developer APIs in one platform
Use case fit: Ideal for businesses needing both rapid no-code deployment and robust API capabilities, organizations handling diverse content types (1,400+ formats, multimedia transcription), teams requiring white-label chatbots with source citations for customer-facing or internal knowledge projects, and companies wanting all-in-one RAG without managing ML infrastructure
Deployment & Infrastructure
SaaS Cloud: Instant setup via Cohere API with global infrastructure and automatic scaling
AWS Bedrock: Managed deployment on AWS with integrated billing and infrastructure
AWS SageMaker: Custom model deployment with full AWS ecosystem integration
Microsoft Azure: Azure-native deployment with regional data residency options
Google Cloud Platform (GCP): GCP-managed deployment with Google infrastructure
Oracle OCI: Oracle Cloud Infrastructure deployment for Oracle ecosystem customers
VPC Deployment: <1 day setup within customer virtual private cloud for network isolation
On-Premises/Air-Gapped: Full private deployment behind customer firewall with ZERO Cohere infrastructure access
Cloud-Agnostic Portability: Switch providers without code changes - consistent API across all deployment options
Regional Data Residency: Enterprise customers choose data center locations for compliance (EU, US, APAC)
Complete Data Sovereignty: Private deployments ensure Cohere has NO access to customer data, queries, or infrastructure
N/A
N/A
Customer Base & Case Studies
RBC (Royal Bank of Canada): Banking deployment for financial services knowledge retrieval and compliance
Dell: Enterprise IT knowledge management and customer support optimization
Oracle: Database and enterprise software documentation search and retrieval
LG Electronics: Multinational corporation using multilingual capabilities for global operations
Ensemble Health Partners: First healthcare deployment for clinical knowledge retrieval (HIPAA verification required)
Jasper: Content creation platform leveraging Cohere for AI-powered writing
LivePerson: Conversational AI integration for customer engagement
Enterprise Focus: Major global corporations in regulated industries (finance, healthcare, technology, manufacturing)
Discord Community: 21,691+ members indicating active developer ecosystem
Cohere Labs: 4,500+ research community members, 100+ publications including Aya multilingual model (101 languages)
N/A
N/A
A I Models
Command A: 256K context, $2.50 in/$10.00 out per 1M tokens - most performant for complex RAG and agents, 75% faster than GPT-4o, 2-GPU deployment minimum
Command A Reasoning (August 2025): First enterprise reasoning LLM with 256K context for multi-step problem solving and advanced agentic workflows
Command R: 128K context, $0.15 in/$0.60 out - cost-conscious simple RAG applications (66x cheaper than Command A for output tokens)
Command R7B: 128K context, $0.0375 in/$0.15 out - fastest, lowest cost for chatbots and simple tasks with minimal latency
Model Retraining: Command model retrained weekly to stay current with latest data and improve performance continuously
23 Optimized Languages: Command A supports English, French, Spanish, German, Japanese, Korean, Chinese, Arabic, and more with native language understanding
Fine-Tuning Support: LoRA for Command R models with up to 16,384 tokens training context for domain-specific adaptation
LIMITATION: NO automatic model routing - developers must implement own logic for query complexity-based selection or use LangChain/third-party orchestration
Default Model - Claude Sonnet 4.5: Primary LLM 'almost no one overrides' according to Anthropic case study - excels at navigating ambiguity in large context windows
Anthropic Claude Family: Sonnet 4.5 (default, best performance), Sonnet 3.7 (balanced), Haiku 3.5 (fast, cost-effective) with 200K token context windows
Claude Sonnet 4.5 Rationale: Selected for 'navigating ambiguity in large context windows' and handling 'deeply nested data structures requiring nuanced reasoning'
Business Impact: Lindy achieved 10x customer growth after implementing Claude as default LLM - significant competitive advantage
Model Switching: Each workflow action requires explicit model selection - no automatic routing based on query complexity or cost optimization
No Dynamic Model Routing: Cannot intelligently switch between models based on task requirements - manual configuration only vs AI-powered model selection
Limited Model Experimentation: No A/B testing capabilities or automatic model performance comparison across different LLMs
Primary models: GPT-5.1 and 4 series from OpenAI, and Anthropic's Claude 4.5 (opus and sonnet) for enterprise needs
Automatic model selection: Balances cost and performance by automatically selecting the appropriate model for each request
Model Selection Details
Proprietary optimizations: Custom prompt engineering and retrieval enhancements for high-quality, citation-backed answers
Managed infrastructure: All model management handled behind the scenes - no API keys or fine-tuning required from users
Anti-hallucination technology: Advanced mechanisms ensure chatbot only answers based on provided content, improving trust and factual accuracy
R A G Capabilities
Grounded Generation Built-In: Native documents parameter in Chat API for RAG without external orchestration, with fine-grained inline citations showing exact document spans
Embed v4.0 Multimodal: Text + images in single vectors (PNG, JPEG, WebP, GIF), 96 images per batch via Embed Jobs API, eliminates complex extraction pipelines
Binary Embeddings: 8x storage reduction (1024 dimensions → 128 bytes) with minimal accuracy loss for large-scale vector database deployments
Rerank 3.5: 128K token context window handles long documents, emails, tables, JSON, code for production RAG with filtering to most relevant passages
100+ Prebuilt Connectors: Google Drive, Slack, Notion, Salesforce, GitHub, Pinecone, Qdrant, MongoDB Atlas, Milvus (open-source on GitHub)
Automatic Retraining: Compass connectors fetch documents at query time - source changes reflect immediately without reindexing
North vs Competitors: Internal benchmarks claim superiority over Microsoft Copilot and Google Vertex AI on RAG accuracy
Hallucination Acknowledgment: Documentation candidly notes "RAG does not guarantee accuracy... RAG greatly reduces the risk but doesn't necessarily eliminate it altogether"
LIMITATION: NO YouTube transcript support requires external transcription service + custom connector development
Search Fuzziness: 100 = pure semantic search (no file limit), lower values add keyword matching but limit to first 1,500 files - trade-off between precision and scale
Default Retrieval: 4 search results returned per query (adjustable up to 10 maximum) for context-aware responses
Document Processing: PDF, DOCX, XLSX, CSV, TXT, HTML with 20MB per-file size limit and automatic text extraction
Audio & Video: Full audio file support with automatic transcription, YouTube transcript extraction via dedicated action
Website Crawling: Single page or full-site crawling with automatic link following and sitemap discovery
Cloud Integration: Google Drive (shared drives), OneDrive, Dropbox, Notion, SharePoint, Intercom, Freshdesk with automatic 24-hour sync
Manual Refresh: 'Resync Knowledge Base' actions for immediate updates when 24-hour sync insufficient
Vector Database: NOT disclosed - no documentation mentions Pinecone, Chroma, Qdrant, or proprietary implementation
Embedding Models: Undocumented - no information about which embedding models power semantic search or customization options
Chunking Strategy: Not configurable - automatic text segmentation with undisclosed chunk size and overlap parameters
Hallucination Reduction: 'Agents on rails' philosophy constrains LLM behavior through predefined workflow steps - architectural constraints vs retrieval optimization
Learning Integration: Human feedback corrections embedded in vector storage for future retrieval improvement
CRITICAL LIMITATION - Black Box Implementation: RAG treated as opaque system - no transparency into vector similarity scores, embedding quality, retrieval mechanisms
CRITICAL LIMITATION - No Published Benchmarks: No RAG accuracy metrics, retrieval precision/recall scores, or latency measurements available
CRITICAL LIMITATION - No Developer Control: Cannot customize embedding models, similarity thresholds, reranking, or retrieval parameters
Enterprise Concern: Opacity may concern organizations requiring transparency into AI decision-making for compliance auditing or regulatory requirements
Core architecture: GPT-4 combined with Retrieval-Augmented Generation (RAG) technology, outperforming OpenAI in RAG benchmarks
RAG Performance
Anti-hallucination technology: Advanced mechanisms reduce hallucinations and ensure responses are grounded in provided content
Benchmark Details
Automatic citations: Each response includes clickable citations pointing to original source documents for transparency and verification
Optimized pipeline: Efficient vector search, smart chunking, and caching for sub-second reply times
Scalability: Maintains speed and accuracy for massive knowledge bases with tens of millions of words
Context-aware conversations: Multi-turn conversations with persistent history and comprehensive conversation management
Source verification: Always cites sources so users can verify facts on the spot
Use Cases
Financial Services: RBC (Royal Bank of Canada) deployment for banking knowledge retrieval, compliance documentation, and North for Banking secure generative AI platform (January 2025)
Healthcare: Ensemble Health Partners for clinical knowledge retrieval, medical documentation search (HIPAA verification required for PHI processing)
Enterprise IT: Dell for enterprise IT knowledge management, customer support optimization, and internal documentation search
Technology Companies: Oracle (database/software documentation), LG Electronics (multinational operations with multilingual needs)
Content Creation: Jasper content platform leveraging Cohere for AI-powered writing and content generation
Conversational AI: LivePerson integration for customer engagement and support automation
Industries Served: Finance, healthcare, life sciences, insurance, supply chain, logistics, legal, hospitality, manufacturing, energy, public sector
Team Sizes: Enterprise-focused platform designed for large organizations with complex content ecosystems requiring comprehensive RAG infrastructure
North Platform (GA August 2025): Customizable AI agents for HR, finance, IT, customer support with MCP (Model Context Protocol) extensibility
Primary Use Case: No-code workflow automation for operations teams, sales teams, marketing teams requiring AI-powered task execution without developers
Sales Automation: Lead qualification with real-time scoring, email/phone validation, firmographic enrichment, CRM syncing (Salesforce, HubSpot, Pipedrive)
Customer Support: Email triage, ticket routing, FAQ responses, escalation workflows with human handoff and context transfer
Healthcare: Patient appointment scheduling, medical record processing (HIPAA-compliant), insurance verification, billing automation
Legal: Document review, contract analysis, case research, deadline tracking with confidentiality controls
Voice Agents (Gaia): Phone call automation with 30+ language support, call transcription in 50+ languages, call transfer to humans
Team Sizes: Individual contributors to enterprise teams (1-500+ users) - scales from solopreneurs to Fortune 500 companies
Industries: Technology, professional services, healthcare, legal, financial services, e-commerce, real estate - any industry with repetitive workflows
Implementation Speed: 30 seconds with Agent Builder ('vibe coding') vs 15-60 minutes with Zapier/Make - fastest setup in automation category
NOT Ideal For: Developers needing programmatic RAG APIs, custom retrieval pipeline tuning, embedding model experimentation, transparent RAG implementation details, organizations requiring EU data residency
Customer support automation: AI assistants handling common queries, reducing support ticket volume, providing 24/7 instant responses with source citations
Internal knowledge management: Employee self-service for HR policies, technical documentation, onboarding materials, company procedures across 1,400+ file formats
Sales enablement: Product information chatbots, lead qualification, customer education with white-labeled widgets on websites and apps
Documentation assistance: Technical docs, help centers, FAQs with automatic website crawling and sitemap indexing
Educational platforms: Course materials, research assistance, student support with multimedia content (YouTube transcriptions, podcasts)
Healthcare information: Patient education, medical knowledge bases (SOC 2 Type II compliant for sensitive data)
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 with reports available under NDA via Trust Center demonstrating robust security controls
ISO 27001 Certified: Information Security Management System compliance for international security standards
ISO 42001 Certified: AI Management System - industry-leading standard for AI governance and responsible AI practices
GDPR Compliant: Data Processing Addendums available, EU data residency options for compliance with European privacy regulations
CCPA Compliant: California Consumer Privacy Act requirements met for US data privacy compliance
UK Cyber Essentials: Government-backed cybersecurity certification for UK market requirements
Zero Data Retention (ZDR): Available upon approval - enterprise customers opt out of training via dashboard
30-Day Automatic Deletion: Logged prompts and generations deleted after 30 days automatically for data minimization
Third-Party Content Protection: Google Drive and other connected app content NEVER used for model training automatically
Encryption: TLS in transit, AES-256 at rest for comprehensive data protection
Air-Gapped Deployment: Full private on-premise deployment behind customer firewall with ZERO Cohere access to infrastructure or data
VPC Deployment: <1 day setup within customer virtual private cloud for network isolation and security
Document-Level Security: Enterprise controls for granular access permissions on sensitive knowledge
CRITICAL LIMITATION: NO explicit HIPAA certification - healthcare organizations processing PHI must verify compliance with sales team; no documented BAA availability like competitors
SOC 2 Type II Certified: Independently audited by Johanson Group validating security controls for data protection, availability, processing integrity
HIPAA Compliant: Business Associate Agreement (BAA) available for healthcare organizations handling Protected Health Information (PHI)
GDPR Compliant: EU General Data Protection Regulation compliance with data processing agreements, right to deletion, consent management
PIPEDA Compliant: Canadian Personal Information Protection and Electronic Documents Act for Canadian customer data
CCPA Compliant: California Consumer Privacy Act compliance for California residents with data access/deletion rights
No AI Training on Customer Data: Explicitly stated in privacy policy - customer data NEVER used for AI model training or improvement
Encryption Standards: AES-256 at rest, TLS 1.2+ in transit for comprehensive data protection across all storage and transmission
Infrastructure: Google Cloud Platform hosting with multi-zone redundancy for 99.9%+ uptime and disaster recovery
Daily Backups: Encrypted backups with secure key management and point-in-time recovery capabilities
Access Controls: RBAC (Role-Based Access Control), MFA (Multi-Factor Authentication), audit logs tracking agent activity and data access
Enterprise SSO: Single Sign-On via existing identity providers (Okta, Azure AD, Google Workspace) for centralized authentication
SCIM Provisioning: Automated user lifecycle management with automatic provisioning/deprovisioning for enterprise security
Admin Controls: Lock configurations, set credit allocation limits per user/team, monitor usage for cost control and security
Audit Logs: Track agent activity, data access, configuration changes on Business/Enterprise plans for compliance and security monitoring
Log Retention: 1 day (Free - severely limits troubleshooting), 7-30 days (Pro/Business), 30+ days (Enterprise with custom retention)
LIMITATION - No ISO 27001: Information Security Management System certification not documented - may limit enterprise procurement
LIMITATION - US Data Residency Only: No explicit EU data residency option documented - enterprise inquiries required for region-specific deployments
LIMITATION - Free Tier Log Retention: 1 day severely constrains security incident investigation and compliance auditing vs 30+ day industry standard
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
Free Tier: Trial API key with rate limits - 20 chat requests/min, 1,000 calls/month total for evaluation; access to all endpoints, ticket support, Cohere Discord community
Production Tier: Pay-per-token usage - Command A $2.50 in/$10.00 out, Command R+ $2.50 in/$10.00 out, Command R $0.15 in/$0.60 out, Command R7B $0.0375 in/$0.15 out per 1M tokens
66x Cost Difference: Command R7B output tokens 66x cheaper than Command A - enables matching model to use case complexity for cost optimization
Embed v4.0 Pricing: $0.12 per 1M tokens (text), $0.47 per 1M tokens (images) for multimodal embeddings
Rerank 3.5 Pricing: $2.00 per 1,000 queries for production RAG reranking and relevance filtering
Enterprise Custom Pricing: North platform, Compass, dedicated instances, private deployments, custom model development require sales engagement
NO Fixed Subscription Tiers: Pay-as-you-go token-based pricing for standard API usage - predictable costs based on volume
Production Unlimited Monthly: No monthly usage caps once on production tier - only per-minute rate limits (500 chat/min)
Binary Embeddings Savings: 8x storage reduction translates to significant infrastructure cost savings for large-scale deployments
Free Plan - $0/month: 400 credits, 1M character knowledge base, 100+ integrations, basic automations, 1-day log retention for evaluation
Pro Plan - $49.99/month: 5,000 credits, 20M character knowledge base, phone calls, full integrations, Lindy branding on embed, 7-day logs
Business Plan - $199.99-$299.99/month: 20,000-30,000 credits, 50M character knowledge base, custom branding, 30+ languages, unlimited calls, 30-day logs
Enterprise Plan - Custom Pricing: Unlimited credits/users, custom knowledge base limits, SSO, SCIM provisioning, dedicated support, custom SLAs, custom training
Additional Team Members: $19.99/member/month on Pro/Business plans for expanding user access and collaboration
Phone Calls: $0.19/minute using GPT-4o for voice interactions - additional cost on top of plan credits
Custom Automation Building: $500 one-time fee for professional automation development by Lindy team
Credit Add-Ons: $19-$1,199/month for 10,000-1,000,000 credits for high-volume usage beyond plan limits
Credit Consumption Variability: Varies by model choice (Claude Sonnet 4.5 vs Haiku 3.5), workflow complexity, premium actions - unpredictable costs
Billing Cycle: Monthly subscription with automatic renewal, credit rollover not documented (likely use-it-or-lose-it monthly)
Payment Methods: Credit card, Enterprise invoicing with wire transfer options for annual contracts
Comparison: vs Zapier ($19.99-$69/month), Make ($9-$29/month), n8n (self-hosted free) - Lindy premium pricing justified by AI capabilities
PRIMARY USER COMPLAINT - Unpredictable Costs: Credit depletion speed consistently frustrating in reviews - 'credits consumed quickly and unpredictably'
CRITICAL LIMITATION - Pricing Transparency: Credit system creates forecasting difficulty vs fixed per-seat or usage-based pricing - budget planning challenging
LIMITATION - Character Limits: 50M character cap on Business tier may limit large enterprise deployments vs unlimited competitors
Standard Plan: $99/month or $89/month annual - 10 custom chatbots, 5,000 items per chatbot, 60 million words per bot, basic helpdesk support, standard security
View Pricing
Premium Plan: $499/month or $449/month annual - 100 custom chatbots, 20,000 items per chatbot, 300 million words per bot, advanced support, enhanced security, additional customization
Enterprise Plan: Custom pricing - Comprehensive AI solutions, highest security and compliance, dedicated account managers, custom SSO, token authentication, priority support with faster SLAs
Enterprise Solutions
7-Day Free Trial: Full access to Standard features without charges - available to all users
Annual billing discount: Save 10% by paying upfront annually ($89/mo Standard, $449/mo Premium)
Flat monthly rates: No per-query charges, no hidden costs for API access or white-labeling (included in all plans)
Managed infrastructure: Auto-scaling cloud infrastructure included - no additional hosting or scaling fees
Support & Documentation
Interactive Documentation: docs.cohere.com with 'Try it' API testing, code examples in all SDKs, Playground 'View Code' export for production deployment
Discord Community: 21,691+ members with API discussions, troubleshooting, 'Maker Spotlight' developer sessions for peer support
Cohere Labs: 4,500+ research community members, 100+ publications including Aya multilingual model (101 languages) demonstrating research leadership
LLM University (LLMU): Structured learning paths for LLM fundamentals, embeddings, AWS SageMaker deployment with hands-on tutorials
Cookbook Library: Practical working examples for agents, RAG, semantic search, summarization with production-ready code
Trust Center: SOC 2 Type II reports (requires NDA), penetration test reports, Data Processing Addendums for enterprise compliance
Enterprise Support: Dedicated account management, custom deployment support, bespoke pricing negotiations for large customers
Rate Limit Increases: Available by contacting support team for production scale requirements exceeding standard 500 chat/min
Cohere Toolkit (3,150+ Stars): Open-source Next.js foundation (MIT license) with community contributions and active development
LIMITATION: NO live chat or phone support for standard API customers - support via Discord and email only without real-time channels
Email Support: support@lindy.ai (general), security@lindy.ai (security issues), privacy@lindy.ai (privacy concerns) with tier-based response times
Slack Community: Peer support network for knowledge sharing among Lindy users and automation best practices
Community Forum: community.lindy.ai for discussions, troubleshooting, feature requests with active user participation
Documentation: Lindy Academy with step-by-step tutorials for business users, video walkthroughs, use case examples
Onboarding: Self-service for Free/Pro, guided onboarding for Business, white-glove implementation for Enterprise with custom training
User-Focused Resources: Strong for business user adoption with non-technical language, visual guides, practical examples
CRITICAL GAP - No Developer Documentation: No API reference, code samples, technical architecture documentation, OpenAPI specs
CRITICAL GAP - No Phone Support: Email and community only for Free/Pro/Business tiers - phone access restricted to Enterprise only
LIMITATION - Support Quality Inconsistency: User reviews note 'inconsistent responsiveness on lower tiers' - common Trustpilot criticism
LIMITATION - Slow Response Times: Some users report 'writing to support twice with no response' - support quality concerns for non-enterprise customers
Documentation hub: Rich docs, tutorials, cookbooks, FAQs, API references for rapid onboarding
Developer Docs
Email and in-app support: Quick support via email and in-app chat for all users
Premium support: Premium and Enterprise plans include dedicated account managers and faster SLAs
Code samples: Cookbooks, step-by-step guides, and examples for every skill level
API Documentation
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
Developer-First Platform: Optimized for teams with coding skills building custom RAG applications, NOT business users seeking no-code solutions
NO Visual Agent Builder: Agent creation requires code via Python SDK - not accessible to non-technical users without development resources
NO Pre-Built Templates: Cookbooks provide code examples but require development expertise - NO drag-and-drop templates or visual workflows
NO Native Messaging Integrations: NO Slack chatbot widget, WhatsApp, Telegram, Microsoft Teams integrations for conversational deployment (North Platform connects as DATA SOURCE only)
NO Embeddable Chat Widget: Requires custom development using SDKs or deploying Cohere Toolkit - no iframe/JavaScript widget out-of-box
NO Built-In Analytics Dashboards: Conversation metrics, user engagement, success rates must be implemented at application layer
Limited RBAC: Owner (full access) and User (shared keys/models) roles only - NO granular permissions or custom roles for team management
HIPAA Gap: No explicit certification with documented BAA availability - healthcare requires sales verification for PHI processing compliance
NO Native Real-Time Alerts: Proactive monitoring and automated alerting require external integrations (Dynatrace, PostHog, New Relic, Grafana)
NO Public REST API: Cannot manage agents, create workflows, or query knowledge base programmatically - blocks developer integration
NO Official SDKs: No Python, JavaScript, Ruby, Go, or any language SDK for programmatic access - workflow automation only
NO CLI Tools: No command-line interface for automation or scripting - dashboard-only management
NO Developer Console: No API sandbox, testing environment, or technical documentation for developers
Black Box RAG Implementation: Vector database, embedding models, similarity thresholds completely undisclosed - no transparency
No RAG Benchmarks: No published accuracy metrics, retrieval precision/recall, or latency measurements for evaluation
Search Fuzziness Constraint: Lower fuzziness values limit searches to first 1,500 files - meaningful limitation for large deployments
Character Storage Limits: 50M character maximum on Business tier - may constrain large enterprise knowledge bases vs unlimited competitors
Unpredictable Credit Consumption: Most common user complaint - 'credits depleted quickly and unpredictably' makes budgeting difficult
US-Only Data Residency: No documented EU data residency option - blocks customers with strict data localization requirements (GDPR, Digital Sovereignty)
No ISO 27001 Certification: Only SOC 2 Type II documented - ISO 27001 absence may limit enterprise procurement in regulated industries
1-Day Free Tier Log Retention: Severely limits troubleshooting and security incident investigation vs 30+ day industry standard
Learning Curve for Complex Workflows: Despite 'vibe coding' simplicity, sophisticated multi-agent systems and delegation rules require workflow design expertise
Support Quality Inconsistency: Mixed reviews noting slow/unresponsive support for non-enterprise tiers - support quality varies significantly by plan
No Manual Model Performance Comparison: Cannot A/B test different LLMs or compare model performance - manual experimentation required
Credit-Based Pricing Opacity: Difficult to forecast costs vs fixed per-seat or per-query pricing - budget planning challenging
NOT Ideal For: Developers needing RAG APIs, teams requiring transparent RAG implementation, EU data residency requirements, organizations needing predictable pricing, technical teams wanting embedding/retrieval control
Platform Category Mismatch: Fundamentally a workflow automation platform (competes with Zapier/Make) NOT a RAG-as-a-Service platform - architectural comparison to CustomGPT.ai is misleading
Managed service approach: Less control over underlying RAG pipeline configuration compared to build-your-own solutions like LangChain
Vendor lock-in: Proprietary platform - migration to alternative RAG solutions requires rebuilding knowledge bases
Model selection: Limited to OpenAI (GPT-5.1 and 4 series) and Anthropic (Claude, opus and sonnet 4.5) - no support for other LLM providers (Cohere, AI21, open-source models)
Pricing at scale: Flat-rate pricing may become expensive for very high-volume use cases (millions of queries/month) compared to pay-per-use models
Customization limits: While highly configurable, some advanced RAG techniques (custom reranking, hybrid search strategies) may not be exposed
Language support: Supports 90+ languages but performance may vary for less common languages or specialized domains
Real-time data: Knowledge bases require re-indexing for updates - not ideal for real-time data requirements (stock prices, live inventory)
Enterprise features: Some advanced features (custom SSO, token authentication) only available on Enterprise plan with custom pricing
Additional Considerations
Enterprise Focus & Customization: Collaborates directly with clients to create solutions addressing specific needs with extensive customization capabilities
Data Privacy Leadership: Complete control over where data is processed and stored - crucial for enterprises with sensitive or regulated data
Deployment Flexibility Advantage: Bring models to customer data vs forcing data to models - massive advantage for data governance and compliance
Private Deployment Capability: Fine-tune on proprietary data without data ever leaving your control - build unique competitive advantage while mitigating risk
Cloud-Agnostic Strategy: Deploy on AWS Bedrock, Azure, GCP, Oracle OCI - switch providers without code changes for vendor-agnostic AI future
Cost Efficiency: RAG-optimized Command R/R+ models allow building scalable, factual applications without breaking bank on compute costs
Performance-Per-Dollar Focus: Move projects from prototype to production more viably with focus on cost efficiency and scalability
Integration Simplicity: NLP platform allows businesses to integrate capabilities with tools like chatbots while simplifying process for developers
Regulatory Compliance Enabler: Air-gapped deployment enables finance, government, defense use cases requiring complete infrastructure control
Data Sovereignty Guarantee: Private deployments ensure Cohere has ZERO access to customer data, queries, or infrastructure for maximum privacy
Unmatched Among Major Providers: OpenAI, Anthropic, Google lack comparable air-gapped on-premise deployment options
Best Use Cases: Operations teams automating repetitive workflows without developer resources - lead qualification, email triage, meeting scheduling, CRM updates, customer support routing excel
Primary Strength: Zero-training deployment with Agent Builder ('vibe coding') creates sophisticated automations in 30 seconds vs 15-60 minutes with Zapier/Make for equivalent workflows
Unique Capabilities: Autopilot (Computer Use) enables automations impossible through traditional integrations - can interact with any web-based application without published APIs through AI-powered browser control
Multi-Agent Societies: Multiple specialized Lindies collaborate on complex tasks through delegation rules - Sales (SDR → AE → CS), Support (Triage → Technical → Escalation), Research with specialized investigators
Credit-Based Pricing Reality: Most common user complaint is unpredictable costs - 'credits consumed quickly and unpredictably' makes budget forecasting difficult vs fixed per-seat or usage-based pricing in competitors
Enterprise Limitations: Character limits (50M cap on Business tier) may constrain large deployments, US-only data residency blocks EU customers with strict localization requirements, no ISO 27001 certification may limit procurement
Developer Friction: Deliberately prioritizes no-code accessibility over developer tooling - NO public REST API, NO SDKs, NO CLI tools, NO programmatic RAG control makes it unsuitable for API-first use cases
Support Inconsistency: User reviews note 'inconsistent responsiveness on lower tiers' and 'writing to support twice with no response' - support quality varies significantly by plan tier
Platform Comparison Warning: Fundamentally different architecture from RAG-as-a-Service platforms - comparing Lindy to CustomGPT is misleading as they serve different product categories (workflow automation vs knowledge retrieval)
Slashes engineering overhead with an all-in-one RAG platform—no in-house ML team required.
Gets you to value quickly: launch a functional AI assistant in minutes.
Stays current with ongoing GPT and retrieval improvements, so you’re always on the latest tech.
Balances top-tier accuracy with ease of use, perfect for customer-facing or internal knowledge projects.
Core Chatbot Features
Chat API: Multi-turn dialog capability with state/memory of previous turns to maintain conversation context
Retrieval-Augmented Generation (RAG): "Document mode" allows developers to specify which documents chatbot references when answering user prompts
Information Source Control: Constrain chatbot to enterprise data or expand to scan entire world wide web via Chat API configuration
Customer Support Solutions: Latest large language models extract knowledge ensuring customers get accurate answers all the time
Generative AI Extraction: Automatically extracts answers from agent responses (after human approval) and replies whenever same question asked again
Intent-Based AI: Cutting-edge intent-based AI goes beyond keyword search surfacing relevant snippets for plain English queries
Cohere Toolkit Integration: Open-source (3,150+ GitHub stars, MIT license) Next.js web app for rapid chatbot deployment with full customization
North Platform Integration: Chat capabilities integrated with North for Banking (January 2025) - secure generative AI platform for financial services
Multi-Turn Conversations: Chatbot API handles conversations through multi-turn dialog requiring state of all previous turns
Command Model Foundation: Built on proprietary Command LLM enabling third-party developers to build chat applications
Advanced Language Understanding: Natural language processing enabling nuanced understanding beyond simple keyword matching
Limitation - Requires Development: Building chatbot requires code using Chat API and SDKs - NOT no-code chatbot builder like SMB platforms
Chatbot vs Agent Philosophy: Lindy differentiates through autonomous agent operation rather than traditional chatbot conversation - emphasizes task execution over conversational interaction
Multi-Lingual Voice Agents (Gaia): 30+ language support for voice agents, transcription covers 50+ languages, text agents operate in 85+ languages with automatic detection - no manual language configuration required
Lead Capture Excellence: Real-time qualification with email/phone validation, firmographic enrichment, UTM attribution tracking, automatic CRM syncing - claims up to 70% higher conversion vs traditional forms
Human Handoff Logic: Configurable escalation conditions with phone agents able to transfer calls directly to human team members with full conversation context and history preservation
Conversation Memory System: Tracks conversation history within and across sessions through memory feature - context persists through workflow execution vs vector similarity search in traditional RAG systems
Analytics & Performance Tracking: Qualification rates, response times, completion rates, handling times monitored comprehensively with weekly automated email summaries of task usage and agent performance
Agent Evals Feature: Dedicated benchmarking system for measuring agent performance against quality standards and preventing regression over time with automated quality monitoring
Workflow-Centric Design: Emphasizes autonomous task execution over conversational chatbot patterns - structured workflows with 'agents on rails' philosophy constraining LLM behavior through predefined steps
Hallucination Prevention: Architectural constraints vs retrieval optimization - 'poor man's RLHF' with human confirmation before action execution prevents costly mistakes
Learning Integration: Corrections from user feedback embedded in vector storage for future retrieval improvement - system learns from mistakes through Memory Snippets saving preferences like scheduling constraints
Reduces hallucinations by grounding replies in your data and adding source citations for transparency.
Benchmark Details
Handles multi-turn, context-aware chats with persistent history and solid conversation management.
Speaks 90+ languages, making global rollouts straightforward.
Includes extras like lead capture (email collection) and smooth handoff to a human when needed.
Customization & Flexibility
N/A
Knowledge Updates: Automatic refresh every 24 hours for all connected cloud sources
Manual Resync: 'Resync Knowledge Base' actions available for immediate updates when needed
Cloud Source Syncing: Google Drive, OneDrive, Dropbox, Notion, SharePoint, Intercom, Freshdesk automatically stay current
Settings Context: Agent-level configuration persists across all task runs for consistent behavior
Per-Run Context: Dynamic customization per execution allows adaptive agent responses
Memory Snippets: Learning preferences saved across sessions (e.g., scheduling constraints, communication style preferences)
Workflow Customization: Visual builder allows business users to modify agent logic without coding
Agent Personality: Configurable tone, expertise areas, and communication style through prompt configuration
No Embedding Control: Cannot customize embedding models, vector similarity thresholds, or retrieval parameters
Limited Developer Flexibility: Black-box RAG implementation prevents optimization of retrieval pipeline or tuning of vector search
N/A
Autopilot & Computer Use
N/A
Unique Capability: AI agents operate cloud-based virtual computers for any website/application interaction
No API Required: Enables automations impossible through traditional integrations - can interact with platforms without published APIs
Computer Vision: Agents 'see' and interact with UIs just like humans - click buttons, fill forms, navigate applications
Workflow Expansion: Breaks beyond 5,000+ integration catalog to access literally any web-based application
Use Cases: Legacy system automation, platforms without APIs, visual task completion, web scraping with context
After analyzing features, pricing, performance, and user feedback, both Cohere and Lindy.ai are capable platforms that serve different market segments and use cases effectively.
When to Choose Cohere
You value industry-leading deployment flexibility: saas, vpc (<1 day), air-gapped on-premise with zero cohere infrastructure access - unmatched among major ai providers
Enterprise security gold standard: SOC 2 Type II + ISO 27001 + ISO 42001 (AI Management System - rare) + GDPR + CCPA + UK Cyber Essentials
Grounded generation with inline citations showing exact document spans - built-in hallucination reduction vs competitors requiring custom implementation
Best For: Industry-leading deployment flexibility: SaaS, VPC (<1 day), air-gapped on-premise with ZERO Cohere infrastructure access - unmatched among major AI providers
When to Choose Lindy.ai
You value exceptional no-code usability: 4.9/5 g2 rating, 30-second setup vs 15-60 min with zapier/make
Massive integration ecosystem: 5,000+ apps via Pipedream Connect with 2,500+ pre-built actions
Claude Sonnet 4.5 default drives 10x customer growth - best-in-class language understanding
Best For: Exceptional no-code usability: 4.9/5 G2 rating, 30-second setup vs 15-60 min with Zapier/Make
Migration & Switching Considerations
Switching between Cohere and Lindy.ai requires careful planning. Consider data export capabilities, API compatibility, and integration complexity. Both platforms offer migration support, but expect 2-4 weeks for complete transition including testing and team training.
Pricing Comparison Summary
Cohere starts at custom pricing, while Lindy.ai begins at custom pricing. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.
Our Recommendation Process
Start with a free trial - Both platforms offer trial periods to test with your actual data
Define success metrics - Response accuracy, latency, user satisfaction, cost per query
Test with real use cases - Don't rely on generic demos; use your production data
Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
Check vendor stability - Review roadmap transparency, update frequency, and support quality
For most organizations, the decision between Cohere and Lindy.ai comes down to specific requirements rather than overall superiority. Evaluate both platforms with your actual data during trial periods, focusing on accuracy, latency, ease of integration, and total cost of ownership.
📚 Next Steps
Ready to make your decision? We recommend starting with a hands-on evaluation of both platforms using your specific use case and data.
• Review: Check the detailed feature comparison table above
• Test: Sign up for free trials and test with real queries
• Calculate: Estimate your monthly costs based on expected usage
• Decide: Choose the platform that best aligns with your requirements
Last updated: December 11, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
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DevRel at CustomGPT.ai. Passionate about AI and its applications. Here to help you navigate the world of AI tools and make informed decisions for your business.
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