In this comprehensive guide, we compare Cohere and Progress Agentic RAG 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 Progress Agentic RAG, 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 Progress Agentic RAG if: you value proprietary remi v2 model (30x faster inference) addresses hallucination problem with continuous quality monitoring - differentiated capability absent from most competitors
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 Progress Agentic RAG
Progress Agentic RAG is enterprise application development and deployment platform. Enterprise RAG-as-a-Service platform launched Sept 2025 following Progress Software's acquisition of Barcelona-based Nuclia. Combines SOC2/ISO 27001 security with proprietary REMi evaluation model for continuous answer quality monitoring. Built on open-source NucliaDB (710+ GitHub stars) with Python/JavaScript SDKs. Starting at $700/month. Founded in 2019 (Nuclia), acquired 2025, headquartered in Barcelona, Spain (Nuclia) / Bedford, MA, USA (Progress), the platform has established itself as a reliable solution in the RAG space.
Overall Rating
82/100
Starting Price
$700/mo
Key Differences at a Glance
In terms of user ratings, Cohere in overall satisfaction. From a cost perspective, Cohere starts at a lower price point. The platforms also differ in their primary focus: RAG Platform versus Enterprise Software. 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)
CRITICAL: CRITICAL GAP - NO YouTube Transcripts: Requires external transcription service + custom connector development
CRITICAL: NO Native Cloud Storage UI: Connectors available but require development setup vs drag-and-drop sync from no-code platforms
60+ Document Formats: PDF, Word (.docx), Excel, PowerPoint, plain text, email formats with automatic parsing
Multimedia Processing: Automatic speech-to-text (MP3, WAV, AIFF), video transcript extraction (MP4, etc.), OCR for scanned documents/images
Cloud Connectors: SharePoint, Confluence, OneDrive, Google Drive, Amazon S3 with direct integration
Sync Agent Desktop App: 60-minute automatic sync with content hashing to prevent redundant reindexing
Manual Upload Interface: Files, folders, web links, sitemaps, Q&A pairs via dashboard
Fast Deployment: 2-hour initial ingestion, 48-hour full deployment timeline
CRITICAL GAPS: NO Dropbox integration, NO Notion integration, NO explicit YouTube transcript extraction documented
Architecture Focus: Comprehensive knowledge retrieval vs lead conversion focus (unlike Drift)
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
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
Retrieval Agents: Autonomously select optimal retrieval strategies based on query characteristics
CSS Customization: Shadow DOM architecture with cssPath attribute for advanced styling
White-Labeling: Full OEM deployment support via API-first design
MISSING FEATURES: NO lead capture, NO human handoff/escalation workflows, NO proactive alerting (monitoring exists, alerting undocumented)
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
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 3.7, Claude 3.5 Sonnet v2
OpenAI: ChatGPT 4o, 4o mini
Google: Gemini Flash 2.5, Palm2
Meta: Llama 3.2
Microsoft/Azure: Mistral Large 2
Cohere: Command-R suite
Nuclia Private GenAI: 100% data isolation for maximum security
Model Switching: Change providers without architectural changes via Prompt Lab
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
Benchmark Leader: Nuclia with OpenAI embeddings achieved highest scores vs Vectara on Docmatix 1.4k dataset across answer relevance, context relevance, correctness
100M Vectors: Fully ingested and optimized in ~20 minutes with sufficient worker allocation
REMi v2 Speed: 30x faster inference than original Mistral-based implementation (Llama 3.2-3B based)
Four-Index Hybrid Search: Document Index (property filtering), Full Text (keyword/fuzzy), Vector/Chunk (semantic), Knowledge Graph (entity relationships)
Dynamic Sharding: Automatic shard creation as vector counts grow with index node replication for fault tolerance
Fast Deployment: 2-hour initial ingestion, 48-hour full deployment timeline
ACID Compliance: TiKV key-value store (Tier 2) manages resource metadata with transaction guarantees
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)
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
SOC2 Type 2 Certified: Annual audits for enterprise security assurance
ISO 27001 Certified: Annually audited information security management
GDPR Compliant: Built-in PII anonymization automatically detects and removes personal data
Encryption: AES-256 at rest, TLS in transit for comprehensive data protection
AI Risk Classification: Low to minimal AI risk category with policy-as-code guardrails
Human-in-the-Loop: Validation options for critical workflows
Tenant Isolation: Customer data separation ensures multi-tenant security
Audit Logs: Standard across all pricing tiers for compliance tracking
API Key Management: Temporal keys and rotation for security hygiene
CRITICAL: CRITICAL LIMITATION: NO HIPAA certification documented - healthcare organizations processing PHI must contact sales for compliance clarification
Data Governance: Enterprise tier supports complete on-premise deployment for 100% data 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
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
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: TRUE RAG-AS-A-SERVICE PLATFORM - Core mission is retrieval-augmented generation backend with developer-first API access
Core Focus: Semantic search and generative Q&A across knowledge bases with transparent NucliaDB architecture
RAG Backend Design: Fully managed RAG infrastructure with embeddable widgets (NOT closed conversational marketing like Drift/Yellow.ai)
Programmatic Access: Complete REST API + dual SDKs (Python/JavaScript) for full knowledge base management
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
Market Position: Enterprise RAG-as-a-Service with genuine no-code accessibility + developer-first API design (dual-track appeal)
vs. CustomGPT: Similar RAG-as-a-Service category, Progress emphasizes REMi quality monitoring + open-source foundation differentiation
vs. Drift/Yellow.ai: TRUE RAG platform vs conversational marketing/sales engagement platforms (fundamentally different categories)
vs. Lindy.ai: Full API/SDK access vs NO public API (Progress developer-friendly, Lindy no-code only)
Integration Gaps: NO native messaging channels (Slack/WhatsApp/Teams) vs omnichannel competitors - requires custom development
HIPAA Gap: No documented certification creates healthcare trust gap vs compliant competitors (Drift has HIPAA)
Recent Acquisition Risk: June 2025 Progress purchase means platform still maturing under new ownership with potential direction changes
Progress Ecosystem Advantage: Integration with OpenEdge, Sitefinity CMS provides distribution channels unavailable to standalone competitors
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
Fully Managed Cloud: EU (primary) and US data centers with regional API routing (https://{region}.rag.progress.cloud/api/v1/)
Hybrid Deployment: Cloud processing with on-premise NucliaDB storage for data sovereignty requirements
Complete On-Premise: Enterprise tier supports 100% on-premise deployment for maximum data governance
AWS Marketplace: Available November 2025 for streamlined enterprise procurement with existing cloud spend
Discord Community: 21,691+ members indicating active developer ecosystem
Cohere Labs: 4,500+ research community members, 100+ publications including Aya multilingual model (101 languages)
SRS Distribution (Wholesale Building Materials): "Progress Agentic RAG has fundamentally changed how we access and act on information across our organisation. Its ability to deliver fast, accurate, and verifiable insights from our unstructured data has been a game-changer for productivity and decision-making."
BrokerChooser (Financial Services): Replaced keyword search with generative AI, reporting significant conversion increases and better user experience
NAFEMS (Engineering Simulation Association): Knowledge discovery across thousands of technical publications for international membership community
Althaia Hospitals (Spain's Largest Central Catalonia Hospital): Medical protocol search supporting 5,000+ healthcare professionals
Columbia Business School: Academic knowledge discovery and research support
Barry University: Education sector deployment for institutional knowledge management
CCOO (Spain's Largest Trade Union): 1M+ members served with knowledge retrieval platform
Buff Sportswear: Commercial deployment for product and customer knowledge management
Pre-Acquisition Scale: ~20 customers across healthcare, pharmaceutical, education, public administration sectors
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
Anthropic Models: Claude 3.7, Claude 3.5 Sonnet v2 for safety-focused, high-quality generation
OpenAI Models: ChatGPT 4o, 4o mini for industry-leading language capabilities
Google Models: Gemini Flash 2.5, PaLM2 for multimodal and search-optimized tasks
Meta Models: Llama 3.2 for open-source flexibility and customization
Microsoft/Azure: Mistral Large 2 for enterprise deployments with Azure integration
Cohere Models: Command-R suite for retrieval-optimized generation
Nuclia Private GenAI: 100% data isolation mode for maximum security without third-party LLM exposure
Model Switching: Change providers without architectural changes via Prompt Lab for side-by-side testing
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
Agentic RAG Engine: Retrieval agents autonomously select optimal strategies based on query characteristics
Four-Index Hybrid Search: Document (property filtering), Full Text (keyword/fuzzy), Vector/Chunk (semantic), Knowledge Graph (entity relationships)
Multimodal Processing: OCR for scanned documents/images, automatic speech-to-text for audio (MP3, WAV, AIFF), video transcript extraction
60+ Document Formats: PDF, Word, Excel, PowerPoint, plain text, email formats with automatic parsing
Open-Source Foundation: NucliaDB (710+ GitHub stars, AGPLv3) provides transparency into retrieval mechanisms vs black-box platforms
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
Enterprise Knowledge Management: Non-technical teams (marketing, HR, legal, customer support) deploying knowledge bases in minutes
Healthcare & Pharma: Althaia Hospitals medical protocol search for 5,000+ healthcare professionals with HIPAA-grade security needs
Financial Services: BrokerChooser replaced keyword search with generative AI for significant conversion increases
Education: Columbia Business School and Barry University for academic knowledge discovery and institutional knowledge management
Engineering & Research: NAFEMS knowledge discovery across thousands of technical publications for international membership
Trade Organizations: CCOO (Spain's largest union) serving 1M+ members with knowledge retrieval platform
Dynamic Knowledge Management: Continuous updates, gap identification, and automatic documentation generation
Developer RAG Backend: API-first infrastructure for building custom AI applications with Prompt Lab experimentation
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
SOC2 Type 2: Annually audited for enterprise security assurance
ISO 27001: Annually audited information security management certification
GDPR Compliant: Built-in PII anonymization automatically detects and removes personal data
Encryption: AES-256 at rest, TLS in transit for comprehensive data protection
AI Risk Classification: Low to minimal AI risk category with policy-as-code guardrails
Human-in-the-Loop: Validation options for critical workflows requiring human oversight
Tenant Isolation: Customer data separation ensures multi-tenant security with isolated Knowledge Boxes
Audit Logs: Standard across all pricing tiers for compliance tracking and security monitoring
API Key Management: Temporal keys and rotation for security hygiene
CRITICAL LIMITATION: NO HIPAA certification documented - healthcare organizations processing PHI must contact sales for compliance clarification
Data Governance: Enterprise tier supports complete on-premise deployment for 100% data control and sovereignty
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
Fly Tier: $700/month - 10GB/15K resources, 750MB max file, 1 Knowledge Box, cloud only, 10K tokens/month included
Scaling Consideration: Token-based consumption pricing requires careful usage forecasting for budget predictability beyond included tier
Best Value For: Organizations wanting to control costs through usage optimization vs fixed seat-based or per-project pricing models
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
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 HIPAA Certification Documented: Healthcare organizations processing PHI must contact sales - major compliance gap vs competitors with documented HIPAA
NO Native Messaging Channels: No Slack, WhatsApp, Telegram, or Microsoft Teams integrations - requires custom API-based development
Documentation Fragmentation: Dual portals (docs.rag.progress.cloud + docs.nuclia.dev) during Progress acquisition transition may cause confusion
Recent Acquisition Risk: June 2025 Progress purchase means platform still maturing under new ownership with potential direction changes
Scalability Concerns: Multiple problems limit scalability - hard to scale nodes up/down, write operations affect concurrent search performance
NO Dropbox Integration: Missing Dropbox connector vs competitors - limits cloud storage sync options
NO Notion Integration: Missing Notion connector - gap for knowledge management workflows
NO YouTube Transcript Extraction: Not explicitly documented vs competitors with video indexing features
Missing Features: NO lead capture, NO human handoff/escalation workflows, NO proactive alerting (monitoring exists, alerting undocumented)
Learning Curve: 30+ RAG parameters and Prompt Lab may feel technical for non-developer teams despite no-code dashboard
Best For: Development teams and technical users - powerful for experts but may overwhelm business users wanting simple deployment
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
Recent Acquisition (June 2025): Progress Software acquired Nuclia for $50M - platform transitioning under new ownership with potential strategic direction changes
Genuine No-Code + Developer Appeal: Dual-track value proposition - non-technical teams use dashboard, developers leverage API/SDKs for custom builds
REMi Quality Differentiator: Proprietary continuous evaluation model (30x faster in v2) addresses hallucination problem absent from most RAG competitors
Open-Source Trust Factor: NucliaDB (710+ GitHub stars, AGPLv3) provides code transparency vs black-box platforms - security audits possible
Multimodal Strength: OCR for images, speech-to-text for audio/video creates comprehensive searchable corpus beyond text-only competitors
Enterprise RAG Focus: Platform optimized for knowledge retrieval and semantic search - not conversational marketing/sales engagement like Drift/Yellow.ai
Progress Ecosystem Integration: OpenEdge database connector, Sitefinity CMS integration provides distribution channels unavailable to standalone platforms
Documentation Fragmentation: Dual portals (docs.rag.progress.cloud + legacy docs.nuclia.dev) during transition may cause developer confusion
Competitive Pricing Entry: $700/month Fly tier undercuts enterprise RAG alternatives while providing genuine capabilities vs limited free tiers
Best For: Organizations wanting model flexibility (7 providers), multimodal indexing, open-source transparency, and developer API access without managing infrastructure
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
AI Search & Generative Answers: Semantic search and Q&A across knowledge bases with trusted, source-linked answers
Multi-Turn Conversations: Context-aware dialogue with conversation history maintained for follow-up questions
Source Citations: Every answer includes citations linking to source documents for verification and transparency
Auto-Summarization: Automatic summarization of long documents for quick understanding
Entity Recognition: AI classification and entity extraction enriching corpus for better Q&A
Answer-Only Mode: Widget configuration for concise answers vs detailed responses based on use case
Multilingual Support: Nuclia multilingual embedding model handles multiple languages out-of-box
MISSING FEATURES: NO lead capture, NO human handoff/escalation workflows, NO chat history export for users
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.
R E Mi Evaluation Model ( Core Differentiator)
N/A
Proprietary Investment: Significant R&D differentiator addressing hallucination problem - absent from most competitors
REMi v2 (Current): Llama-REMi v1 based on Llama 3.2-3B with 30x faster inference vs original Mistral implementation
Continuous Quality Monitoring: Evaluates EVERY interaction across four dimensions (0-5 scale)
Answer Relevance: Measures how directly response addresses the query
Context Relevance: Assesses quality of retrieved passages relative to question
Groundedness: Evaluates degree to which answers derive from source context (hallucination detection)
Answer Correctness: Alignment with ground truth when available (optional dimension)
Benchmark Validation: Nuclia with OpenAI embeddings achieved highest scores vs Vectara on Docmatix 1.4k dataset across answer relevance, context relevance, correctness
Real-Time Visibility: Dashboard health displays with 7-day rolling averages and performance graphs (24h to 30d)
Competitive Advantage: Most RAG platforms lack continuous quality evaluation - Progress makes this core differentiator
N/A
Open- Source Nuclia D B Foundation
N/A
GitHub Presence: 710+ stars, AGPLv3 license provides full transparency into core retrieval mechanisms
Technology Stack: Python and Rust implementation for performance and reliability
Managed Infrastructure: Progress removes operational burden while maintaining technical transparency
Four Index Types: Document Index (property filtering), Full Text (keyword/fuzzy search), Chunk/Vector (semantic similarity), Knowledge Graph (entity relationships)
Dynamic Sharding: Automatic shard creation as vectors grow with index node replication for fault tolerance
After analyzing features, pricing, performance, and user feedback, both Cohere and Progress Agentic RAG 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 Progress Agentic RAG
You value proprietary remi v2 model (30x faster inference) addresses hallucination problem with continuous quality monitoring - differentiated capability absent from most competitors
Open-source NucliaDB transparency (710+ GitHub stars) with managed infrastructure removes operational burden while maintaining technical visibility
Genuine no-code accessibility: business users (marketing, HR, legal, support) can deploy functional RAG pipelines in minutes via visual dashboard
Best For: Proprietary REMi v2 model (30x faster inference) addresses hallucination problem with continuous quality monitoring - differentiated capability absent from most competitors
Migration & Switching Considerations
Switching between Cohere and Progress Agentic RAG 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 Progress Agentic RAG begins at $700/month. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.
Our Recommendation Process
Start with a free trial - Both platforms offer trial periods to test with your actual data
Define success metrics - Response accuracy, latency, user satisfaction, cost per query
Test with real use cases - Don't rely on generic demos; use your production data
Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
Check vendor stability - Review roadmap transparency, update frequency, and support quality
For most organizations, the decision between Cohere and Progress Agentic RAG 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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