Which is better for enterprises: Cohere or Yellow.ai?

Cohere offers SOC 2 compliance for enterprise security. HIPAA compliance is available in Yellow.ai. For scalability, both offer cloud-based deployment options. Cohere leads with 89/100 enterprise readiness score.

How much does Cohere cost vs Yellow.ai?

Cohere starts at custom pricing/monthly (freemium model), while Yellow.ai begins at contact sales/monthly (enterprise-annual model). Both offer free tiers to get started. Pricing is relatively comparable. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from Yellow.ai to Cohere?

Migration from Yellow.ai to Cohere typically requires API-based data export/import, which Cohere provides. Cohere provides comprehensive API access excellent documentation for the migration process. Estimated migration time: 1-2 weeks for full deployment. Contact Cohere support for migration assistance and best practices.

What integrations do Cohere and Yellow.ai support?

Cohere integrates with Developer frameworks: LangChain, LlamaIndex, Haystack official integrations, Zapier: 8,000+ app connections for workflow automation, Webhooks: Full REST API support for custom integrations, Open-source Cohere Toolkit: 3,150+ GitHub stars, Next.js web app with SQL database, full customization, Multi-cloud deployment: AWS Bedrock, SageMaker, Azure, GCP, Oracle OCI with cloud-agnostic portability (9 integrations total). Yellow.ai supports Messaging: WhatsApp (BSP provider), Messenger, Instagram, Telegram, Slack, Teams, Line, Viber, WeChat, Zalo, Google Chat, Voice: IVR integration, Google Assistant, Amazon Alexa, telephony systems, SMS and email channels fully supported, Enterprise systems: Salesforce, ServiceNow, Confluence, SharePoint, AWS S3, Prismic, Web: JavaScript widget (CDN-hosted, no npm package), Progressive Web App, iframe support (8 integrations available). Both offer REST APIs for custom integrations. Cohere offers 13% more integrations.

What are the main differences between Cohere and Yellow.ai?

Cohere offers strong capabilities, scoring 0/100 in user ratings. Yellow.ai provides unique strengths with 0/100 overall rating. Key technical difference: Cohere focuses on advanced features while Yellow.ai emphasizes specialized capabilities.

Which is better for enterprises: Cohere or Yellow.ai?

Both platforms provide enterprise-grade security. HIPAA compliance varies - contact vendors for details. For scalability, both offer cloud-based deployment options. Yellow.ai leads with strong/100 enterprise readiness score.

How much does Cohere cost vs Yellow.ai?

Cohere starts at custom pricing/month (subscription model), while Yellow.ai begins at contact sales/month (subscription model). Both require paid plans. Pricing is relatively comparable. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from Cohere to Yellow.ai?

Migration from Cohere to Yellow.ai typically requires manual data transfer since API access may be limited. Yellow.ai offers standard documentation for the migration process. Estimated migration time: 1-2 days for cloud setup. Contact Yellow.ai support for migration assistance and best practices.

What integrations do Cohere and Yellow.ai support?

Cohere integrates with (multiple platforms). Yellow.ai supports (various tools). API availability varies by plan. Integration coverage is comparable.

Cohere vs Yellow.ai

Q: What are the main differences between Cohere and Yellow.ai?

Cohere excels in Industry-leading deployment flexibility: SaaS, VPC (<1 day), air-gapped on-premise with ZERO Cohere infrastructure access - unmatched among major AI providers and Enterprise security gold standard: SOC 2 Type II + ISO 27001 + ISO 42001 (AI Management System - rare) + GDPR + CCPA + UK Cyber Essentials, scoring 89/100 in user ratings with 92% accuracy. Yellow.ai leads in Genuinely comprehensive 35+ channel coverage: WhatsApp BSP, Messenger, Instagram, Telegram, Slack, Teams, voice, SMS and Exceptional compliance credentials: SOC 2, ISO 27001/27018/27701, HIPAA, GDPR, PCI DSS, FedRAMP with 85/100 overall rating and N/A% accuracy. Key technical difference: Cohere focuses on Command A (256K context, 2 GPU deployment, 75% faster than GPT-4o) - most performant for complex RAG and reasoning while Yellow.ai emphasizes 16 billion+ conversations processed annually.

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

More about Yellow.ai More about Cohere Get Started Today

Priyansh KhodiyarDevRel at CustomGPT.ai

Fact checked and reviewed by Bill Cava

Published: 01.04.2025Updated: 25.04.2025

In this comprehensive guide, we compare Cohere and Yellow.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 Yellow.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 Yellow.ai if: you value genuinely comprehensive 35+ channel coverage: whatsapp bsp, messenger, instagram, telegram, slack, teams, voice, sms

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 Yellow.ai

Yellow.ai is enterprise conversational ai platform with multi-llm orchestration. Enterprise conversational AI platform with embedded RAG capabilities processing 16 billion+ conversations annually. Multi-LLM orchestration across 35+ channels and 135+ languages with proprietary YellowG LLM claiming <1% hallucination rates. Founded in 2016, headquartered in San Mateo, CA, USA / Bengaluru, India, the platform has established itself as a reliable solution in the RAG space.

Overall Rating

85/100

Starting Price

Custom

Key Differences at a Glance

In terms of user ratings, both platforms score similarly in overall satisfaction. From a cost perspective, pricing is comparable. The platforms also differ in their primary focus: RAG Platform versus Conversational AI. These differences make each platform better suited for specific use cases and organizational requirements.

⚠️ What This Comparison Covers

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

Detailed Feature Comparison

Cohere	Yellow.ai	CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
100+ Prebuilt Connectors – Google Drive, Slack, Salesforce, GitHub, Pinecone, Qdrant, MongoDB Atlas Multimodal Embed v4.0 – Text + images in single vectors, 96 images/batch processing Binary Embeddings – 8x storage reduction (1024 dim → 128 bytes) ⚠️ NO Native Cloud UI – Connectors require developer setup, not drag-and-drop	DocCog Engine – 75-85% accuracy with T5 model fine-tuned on SQuAD/TriviaQA Formats – PDF, DOCX, DOC, PPTX, PPT, TXT via platform UI only Enterprise integrations – Salesforce, ServiceNow, Confluence, SharePoint, AWS S3 Auto-sync – Hourly, daily, weekly configurable intervals ⚠️ No cloud storage – No Google Drive, Dropbox, or Notion support ⚠️ No API upload – Document management via UI only	1,400+ file formats – PDF, DOCX, Excel, PowerPoint, Markdown, HTML + auto-extraction from ZIP/RAR/7Z archives Website crawling – Sitemap indexing with configurable depth for help docs, FAQs, and public content Multimedia transcription – AI Vision, OCR, YouTube/Vimeo/podcast speech-to-text built-in Cloud integrations – Google Drive, SharePoint, OneDrive, Dropbox, Notion with auto-sync Knowledge platforms – Zendesk, Freshdesk, HubSpot, Confluence, Shopify connectors Massive scale – 60M words (Standard) / 300M words (Premium) per bot with no performance degradation
Integrations & Channels
Developer Frameworks – LangChain, LlamaIndex, Haystack, Zapier (8,000+ apps) Multi-Cloud Deployment – AWS Bedrock, Azure, GCP, Oracle OCI, cloud-agnostic portability Cohere Toolkit – Open-source (3,150+ GitHub stars) Next.js deployment app ⚠️ NO Native Messaging/Widget – NO Slack, WhatsApp, Teams, embeddable chat requires custom development	35+ channels – WhatsApp (BSP), Messenger, Instagram, Telegram, Slack, Teams, Line Voice channels – IVR, Google Assistant, Amazon Alexa, telephony Enterprise systems – Salesforce, ServiceNow, Confluence, SharePoint Mobile SDKs – Android, iOS, React Native, Flutter, Cordova ⚠️ No Python SDK – Major gap for backend developers	Website embedding – Lightweight JS widget or iframe with customizable positioning CMS plugins – WordPress, WIX, Webflow, Framer, SquareSpace native support 5,000+ app ecosystem – Zapier connects CRMs, marketing, e-commerce tools MCP Server – Integrate with Claude Desktop, Cursor, ChatGPT, Windsurf OpenAI SDK compatible – Drop-in replacement for OpenAI API endpoints LiveChat + Slack – Native chat widgets with human handoff capabilities
Core Agent Features
North Platform (GA Aug 2025) – Customizable agents for HR, finance, IT with MCP Grounded Generation – Inline citations showing exact document spans with hallucination reduction Multi-Step Tool Use – Command models execute parallel tool calls with reasoning ⚠️ NO Lead Capture/Analytics – Must implement at application layer, no marketing automation	16B+ conversations – Massive scale annually across enterprise deployments 135+ languages – Including 11+ Indonesian variants (Komodo-7B) Agentic RAG – Multi-checkpoint validation with guardrails YellowG LLM – Claims <1% hallucination vs GPT-3's 22.7% 0.6s response time – Optimized for conversational AI	Custom AI Agents – Autonomous GPT-4/Claude agents for business tasks Multi-Agent Systems – Specialized agents for support, sales, knowledge Memory & Context – Persistent conversation history across sessions Tool Integration – Webhooks + 5,000 Zapier apps for automation Continuous Learning – Auto re-indexing without manual retraining
Customization & Branding
Open-Source Toolkit (MIT) – Complete frontend source code for unlimited customization Fine-Tuning via LoRA – Command R models with 16K training context for specialization White-Labeling – Fully supported via self-hosted deployments, NO Cohere branding ⚠️ NO Visual Agent Builder – Agent creation requires Python SDK, not for non-technical users	Visual Studio – Drag-and-drop conversation flow builder White-labeling – Custom branding on Enterprise plan Agent personality – Configurable tone and response style RBAC – Six permission levels for access control	Full white-labeling included – Colors, logos, CSS, custom domains at no extra cost 2-minute setup – No-code wizard with drag-and-drop interface Persona customization – Control AI personality, tone, response style via pre-prompts Visual theme editor – Real-time preview of branding changes Domain allowlisting – Restrict embedding to approved sites only
L L M Model Options
Command A – 256K context, $2.50/$10, 75% faster than GPT-4o, 2-GPU minimum Command R+ – 128K context, $2.50/$10, 50% higher throughput, 20% lower latency Command R – 128K context, $0.15/$0.60, 66x cheaper than Command A output Command R7B – 128K context, $0.0375/$0.15, fastest and lowest cost 23 Optimized Languages – English, French, Spanish, German, Japanese, Korean, Chinese, Arabic	YellowG LLM – Proprietary with <1% hallucination claims, 0.6s response Komodo-7B – Indonesia-focused with 11+ regional languages T5 Fine-Tuned – SQuAD/TriviaQA for Q&A extraction GPT-3/3.5 – Integration documented ⚠️ GPT-4/Claude unclear – Not explicitly confirmed in docs ⚠️ No manual selection – Dynamic routing handles automatically	GPT-5.1 models – Latest thinking models (Optimal & Smart variants) GPT-4 series – GPT-4, GPT-4 Turbo, GPT-4o available Claude 4.5 – Anthropic's Opus available for Enterprise Auto model routing – Balances cost/performance automatically Zero API key management – All models managed behind the scenes
Developer Experience ( A P I & S D Ks)
Four Official SDKs – Python, TypeScript/JS, Java, Go with multi-cloud support REST API v2 – Chat, Embed, Rerank, Classify, Tokenize, Fine-tuning, streaming Native RAG – documents parameter for grounded generation with inline citations LLM University (LLMU) – Learning paths for fundamentals, embeddings, SageMaker deployment	Platform-first – APIs supplementary, not primary access Available via API – User management, event pushing, webhooks Mobile SDKs – Well-documented Android, iOS, React Native, Flutter ⚠️ NOT via API – Bot creation, document upload, RAG querying ⚠️ No Python SDK – Only mobile SDKs available	REST API – Full-featured for agents, projects, data ingestion, chat queries Python SDK – Open-source `customgpt-client` with full API coverage Postman collections – Pre-built requests for rapid prototyping Webhooks – Real-time event notifications for conversations and leads OpenAI compatible – Use existing OpenAI SDK code with minimal changes
Performance & Accuracy
Command A Performance – 75% faster than GPT-4o, runs on 2 GPUs Embed v3.0 Benchmarks – MTEB 64.5, BEIR 55.9 among 90+ models Rerank 3.5 Context – 128K token window handles documents, emails, tables, code Grounded Generation – Inline citations show exact document spans, reduces hallucination	YellowG claims – <1% hallucination vs GPT-3's 22.7% 0.6s latency – Average response time claimed DocCog accuracy – 75-85% depending on complexity Scale validated – 16B+ conversations annually ⚠️ No published benchmarks – No independent validation	Sub-second responses – Optimized RAG with vector search and multi-layer caching Benchmark-proven – 13% higher accuracy, 34% faster than OpenAI Assistants API Anti-hallucination tech – Responses grounded only in your provided content OpenGraph citations – Rich visual cards with titles, descriptions, images 99.9% uptime – Auto-scaling infrastructure handles traffic spikes
Pricing & Scalability
Trial/Free – 20 chat/min, 1,000 calls/month for evaluation Production Pay-Per-Token – Command A $2.50/$10, R7B $0.0375/$0.15 (66x cheaper output) Production Unlimited Monthly – No monthly caps, 500 chat/min rate limit	Free tier – 1 bot, 2 channels, 100 MTUs (evaluation only) Basic – ~$10,000/year for single use case Standard – ~$25,000/year for up to 4 use cases Enterprise – Custom with unlimited bots, on-premise options ⚠️ 4-month implementation – Typical deployment timeline	Standard: $99/mo – 60M words, 10 bots Premium: $449/mo – 300M words, 100 bots Auto-scaling – Managed cloud scales with demand Flat rates – No per-query charges
Security & Privacy
SOC 2 Type II + ISO 27001 + ISO 42001 – Annual audits, AI Management certification GDPR + CCPA Compliant – Data Processing Addendums, EU data residency Zero Data Retention (ZDR) – Available upon approval, 30-day auto deletion Air-Gapped Deployment – Full private on-premise, ZERO Cohere infrastructure access ⚠️ NO HIPAA Certification – Healthcare PHI processing requires sales verification	N/A	SOC 2 Type II + GDPR – Third-party audited compliance Encryption – 256-bit AES at rest, SSL/TLS in transit Access controls – RBAC, 2FA, SSO, domain allowlisting Data isolation – Never trains on your data
Observability & Monitoring
Native Dashboard – Billing/usage tracking, API key management, spending limits, tokens North Platform – Audit-ready logs, traceability for enterprise compliance Third-Party Integrations – Dynatrace, PostHog, New Relic, Grafana monitoring ⚠️ NO Native Real-Time Alerts – Proactive monitoring requires external integrations	Analytics dashboard – Comprehensive conversation metrics Deflection metrics – Automation success rates Voice analytics – IVR and telephony tracking 15-day audit logs – API activity retention	Real-time dashboard – Query volumes, token usage, response times Customer Intelligence – User behavior patterns, popular queries, knowledge gaps Conversation analytics – Full transcripts, resolution rates, common questions Export capabilities – API export to BI tools and data warehouses
Support & Ecosystem
Discord Community – 21,691+ members for API discussions, troubleshooting, Maker Spotlight Cohere Labs – 4,500+ research community, 100+ publications including Aya (101 languages) Interactive Documentation – docs.cohere.com with 'Try it' testing, Playground export Enterprise Support – Dedicated account management, custom deployment, bespoke pricing ⚠️ NO Live Chat/Phone – Standard customers use Discord and email only	Multi-channel support – Email, chat, phone with tier-based access Enterprise support – Dedicated CSMs, priority SLAs Gartner recognition – Magic Quadrant 'Challenger' (2023/2025) G2 ratings – 4.4/5 (106 reviews), 9.3 customization ⚠️ Steep learning curve – "Rubik's cube blindfolded" G2 review	Comprehensive docs – Tutorials, cookbooks, API references Email + in-app support – Under 24hr response time Premium support – Dedicated account managers for Premium/Enterprise Open-source SDK – Python SDK, Postman, GitHub examples 5,000+ Zapier apps – CRMs, e-commerce, marketing integrations
No- Code Interface & Usability
Playground – Visual model testing with parameter tuning, SDK code export Dataset Upload UI – No-code dataset upload for fine-tuning via dashboard ⚠️ NO Visual Agent Builder – Agent creation requires Python SDK, not for non-technical users	Visual Studio – Drag-and-drop flow builder Dynamic AI Agent – Zero-training deployment Pre-built templates – Industry-specific scenarios ⚠️ Reality check – G2 reviews cite "steep learning curve" ⚠️ Developer effort – Required for journey updates despite no-code claims	2-minute deployment – Fastest time-to-value in the industry Wizard interface – Step-by-step with visual previews Drag-and-drop – Upload files, paste URLs, connect cloud storage In-browser testing – Test before deploying to production Zero learning curve – Productive on day one
Enterprise Deployment Flexibility ( Core Differentiator)
SaaS (Instant) – Immediate setup via Cohere API with global infrastructure Multi-Cloud Support – AWS Bedrock, Azure, GCP, Oracle OCI, cloud-agnostic portability VPC Deployment – <1 day setup within customer private cloud for isolation Air-Gapped/On-Premises – Full private deployment, ZERO Cohere data access ✅ Unmatched Among Providers – OpenAI, Anthropic, Google lack comparable on-premise options	N/A	N/A
Grounded Generation with Citations ( Core Differentiator)
Inline Citations – Responses show exact document spans informing each answer Fine-Grained Attribution – Citations link specific sentences/paragraphs vs generic references Rerank 3.5 Integration – 128K context filters emails, tables, JSON to passages Native RAG API – documents parameter enables grounded generation without external orchestration ✅ Competitive Advantage – Most platforms need custom citation, Cohere provides built-in	N/A	N/A
Multimodal Embed v4.0 ( Differentiator)
Text + Images – Single vectors combining text/images eliminate extraction pipelines 96 Images Per Batch – Embed Jobs API handles large-scale multimodal processing Matryoshka Learning – Flexible dimensionality (256/512/1024/1536) for cost-performance optimization Binary Embeddings – 8x storage reduction for large vector databases, minimal loss ✅ Top-Tier Benchmarks – MTEB 64.5, BEIR 55.9 among 90+ models	N/A	N/A
Multi- Lingual Support
Command A – 23 optimized languages: English, French, Spanish, German, Japanese, Korean, Chinese Embed and Rerank – 100+ languages with cross-lingual retrieval, no translation Aya Research Model – Cohere Labs open research covering 101 languages	N/A	N/A
R A G-as-a- Service Assessment
Platform Type – TRUE RAG-AS-A-SERVICE API PLATFORM for custom developer solutions API-First Architecture – REST API v2 + 4 SDKs (Python, TypeScript, Java, Go) RAG Technology Leadership – Embed v4.0 (multimodal), Rerank 3.5 (128K), inline citations Deployment Flexibility – SaaS, VPC, air-gapped on-premise, unmatched among major providers ⚠️ CRITICAL GAPS – NO chat widgets, messaging integrations, visual builders, analytics	Platform type – Full-stack enterprise CX, NOT pure RAG-as-a-Service Critical distinction – RAG embedded, not exposed as API ⚠️ Cannot use as RAG backend – Queries must flow through platform ⚠️ No RAG endpoints – No embedding access or vector store API Comparison warning – vs CustomGPT is architecturally misleading	Platform type – TRUE RAG-AS-A-SERVICE with managed infrastructure API-first – REST API, Python SDK, OpenAI compatibility, MCP Server No-code option – 2-minute wizard deployment for non-developers Hybrid positioning – Serves both dev teams (APIs) and business users (no-code) Enterprise ready – SOC 2 Type II, GDPR, WCAG 2.0, flat-rate pricing
Competitive Positioning
Market Position – Enterprise-first RAG API platform with unmatched deployment flexibility Deployment Differentiator – Air-gapped on-premise, ZERO Cohere access vs SaaS-only competitors Security Leadership – SOC 2 + ISO 27001 + ISO 42001 (rare AI certification) + GDPR Cost Optimization – Command R7B 66x cheaper than A, model-to-use-case matching Research Pedigree – Founded by Transformer co-author Gomez, $1.54B funding (RBC, Dell, Oracle)	35+ channel coverage – Unmatched omnichannel with WhatsApp BSP Compliance leadership – SOC 2, ISO, HIPAA, GDPR, PCI DSS, FedRAMP Gartner validated – Magic Quadrant Challenger status ⚠️ Not RAG-as-a-Service – Embedded RAG, closed API ⚠️ High entry barrier – $10K+ annual minimum	Market position – Leading RAG platform balancing enterprise accuracy with no-code usability. Trusted by 6,000+ orgs including Adobe, MIT, Dropbox. Key differentiators – #1 benchmarked accuracy • 1,400+ formats • Full white-labeling included • Flat-rate pricing vs OpenAI – 10% lower hallucination, 13% higher accuracy, 34% faster vs Botsonic/Chatbase – More file formats, source citations, no hidden costs vs LangChain – Production-ready in 2 min vs weeks of development
Customer Base & Case Studies
Financial Services – RBC (Royal Bank of Canada) for banking knowledge and compliance Enterprise IT – Dell for knowledge management, Oracle for database docs Global Operations – LG Electronics using multilingual capabilities for global operations $1.54B Funding – Nvidia, Salesforce, Oracle, AMD, Schroders, Fujitsu investments	N/A	N/A
A I Models
Command A – 256K context, $2.50/$10, 75% faster than GPT-4o Command R+/R/R7B – 128K context, pricing from $0.0375 to $10 per 1M 66x Cost Difference – Command R7B output 66x cheaper than Command A 23 Optimized Languages – English, French, Spanish, German, Japanese, Korean, Chinese, Arabic	YellowG LLM – Proprietary with <1% hallucination claims Komodo-7B – 11+ Indonesian language variants Orchestrator LLM – Context switching, multi-intent detection T5 Fine-Tuned – DocCog Q&A extraction ⚠️ Limited flexibility – No manual model selection	OpenAI – GPT-5.1 (Optimal/Smart), GPT-4 series Anthropic – Claude 4.5 Opus/Sonnet (Enterprise) Auto-routing – Intelligent model selection for cost/performance Managed – No API keys or fine-tuning required
R A G Capabilities
Grounded Generation Built-In – Native documents parameter with fine-grained inline citations Embed v4.0 Multimodal – Text + images in single vectors, 96 images/batch Top-Tier Embeddings – MTEB 64.5, BEIR 55.9, Matryoshka (256/512/1024/1536 dim) Rerank 3.5 – 128K token context handles documents, emails, tables, JSON, code Binary Embeddings – 8x storage reduction (1024 dim → 128 bytes) minimal loss	Agentic RAG – Multi-checkpoint validation with guardrails DocCog – 75-85% accuracy for Q&A extraction Hallucination prevention – Proprietary training approach Knowledge sync – Configurable intervals for external sources ⚠️ Closed architecture – No direct API or embedding access	GPT-4 + RAG – Outperforms OpenAI in independent benchmarks Anti-hallucination – Responses grounded in your content only Automatic citations – Clickable source links in every response Sub-second latency – Optimized vector search and caching Scale to 300M words – No performance degradation at scale
Use Cases
Financial Services – RBC deployment for banking knowledge, compliance, North for Banking Healthcare – Ensemble Health for clinical knowledge (HIPAA verification required) Enterprise IT – Dell for knowledge management, customer support, documentation search Technology Companies – Oracle (database docs), LG Electronics (multilingual operations)	Customer service – 90% automation across 35+ channels Employee experience – IT support, HR FAQs, leave applications E-commerce – Shopping assistance, order tracking, fraud detection Regulated industries – Healthcare, financial services, government	Customer support – 24/7 AI handling common queries with citations Internal knowledge – HR policies, onboarding, technical docs Sales enablement – Product info, lead qualification, education Documentation – Help centers, FAQs with auto-crawling E-commerce – Product recommendations, order assistance
Pricing & Plans
Free Tier – Trial API key with 20 chat/min, 1,000 calls/month Production Pay-Per-Token – Command A $2.50/$10, R7B $0.0375/$0.15 (66x cheaper output) Production Unlimited Monthly – No monthly caps, 500 chat/min rate limit	N/A	Standard: $99/mo – 10 chatbots, 60M words, 5K items/bot Premium: $449/mo – 100 chatbots, 300M words, 20K items/bot Enterprise: Custom – SSO, dedicated support, custom SLAs 7-day free trial – Full Standard access, no charges Flat-rate pricing – No per-query charges, no hidden costs
Limitations & Considerations
Developer-First Platform – Optimized for teams with coding skills, NOT business users NO Visual Agent Builder – Agent creation requires Python SDK, not for non-technical users NO Native Messaging/Widget – NO Slack, WhatsApp, Teams, embeddable chat needs custom development HIPAA Gap – No explicit certification, healthcare needs sales verification NOT Ideal For – SMBs without dev resources, teams needing visual builders/messaging	⚠️ NOT RAG-as-a-Service – Embedded RAG, cannot use as knowledge backend ⚠️ No API-first development – Cannot create bots or upload docs via API ⚠️ Missing developer tools – No Python SDK, no npm package, no OpenAPI spec ⚠️ No cloud storage – No Google Drive, Dropbox, Notion integration ⚠️ High entry barrier – $10K-$25K annual, 4-month implementation Best for – Enterprise omnichannel CX; poor fit for RAG API developers	Managed service – Less control over RAG pipeline vs build-your-own Model selection – OpenAI + Anthropic only; no Cohere, AI21, open-source Real-time data – Requires re-indexing; not ideal for live inventory/prices Enterprise features – Custom SSO only on Enterprise plan
Core Chatbot Features
Chat API – Multi-turn dialog with state/memory of previous turns for context Retrieval-Augmented Generation (RAG) – Document mode specifies which documents to reference Generative AI Extraction – Automatically extracts answers from responses for reuse Intent-Based AI – Beyond keyword search, surfaces relevant snippets for plain English	Multi-turn conversations – Context across turns with intent detection 150+ languages – Automatic detection with native processing Human handoff – Full context transfer with SLA tracking Voice AI – 50+ language support with sentiment analysis 170+ integrations – Complex workflow automation	✅ #1 accuracy – Median 5/5 in independent benchmarks, 10% lower hallucination than OpenAI ✅ Source citations – Every response includes clickable links to original documents ✅ 93% resolution rate – Handles queries autonomously, reducing human workload ✅ 92 languages – Native multilingual support without per-language config ✅ Lead capture – Built-in email collection, custom forms, real-time notifications ✅ Human handoff – Escalation with full conversation context preserved
Security & Compliance
N/A	SOC 2 Type II – Independently audited ISO certifications – 27001, 27018, 27701 HIPAA/GDPR/PCI DSS – Healthcare, privacy, payment compliance FedRAMP – US government authorized 6 data regions – US, EU, Singapore, India, Indonesia, UAE	SOC 2 Type II + GDPR – Regular third-party audits, full EU compliance 256-bit AES encryption – Data at rest; SSL/TLS in transit SSO + 2FA + RBAC – Enterprise access controls with role-based permissions Data isolation – Never trains on customer data Domain allowlisting – Restrict chatbot to approved domains
Customization & Flexibility ( Behavior & Knowledge)
N/A	Agent persona – Name, role, tone, communication style Conversation rules – Custom behavior for specific situations Multi-KB support – Role-based access, cross-KB search ⚠️ No embedding control – Retrieval mechanisms not exposed ⚠️ No programmatic API – UI-only knowledge management	Live content updates – Add/remove content with automatic re-indexing System prompts – Shape agent behavior and voice through instructions Multi-agent support – Different bots for different teams Smart defaults – No ML expertise required for custom behavior
Additional Considerations
N/A	Platform type – Enterprise CX platform with embedded RAG, NOT RaaS ✅ 35+ channels – Exceptional omnichannel with 135+ languages ✅ Compliance breadth – SOC 2, ISO, HIPAA, GDPR, PCI DSS, FedRAMP ⚠️ Not for developers – No RAG API, Python SDK, or programmatic control ⚠️ High entry barrier – $10K-$25K annual with 4-month implementation Best for – Enterprises needing omnichannel CX automation at scale	Time-to-value – 2-minute deployment vs weeks with DIY Always current – Auto-updates to latest GPT models Proven scale – 6,000+ organizations, millions of queries Multi-LLM – OpenAI + Claude reduces vendor lock-in
Support & Documentation
N/A	N/A	Documentation hub – Docs, tutorials, API references Support channels – Email, in-app chat, dedicated managers (Premium+) Open-source – Python SDK, Postman, GitHub examples Community – User community + 5,000 Zapier integrations

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Final Thoughts

Final Verdict: Cohere vs Yellow.ai

After analyzing features, pricing, performance, and user feedback, both Cohere and Yellow.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 Yellow.ai

You value genuinely comprehensive 35+ channel coverage: whatsapp bsp, messenger, instagram, telegram, slack, teams, voice, sms
Exceptional compliance credentials: SOC 2, ISO 27001/27018/27701, HIPAA, GDPR, PCI DSS, FedRAMP
Multi-region data centers (US, EU, Singapore, India, Indonesia, UAE) with customer-selected residency

Best For: Genuinely comprehensive 35+ channel coverage: WhatsApp BSP, Messenger, Instagram, Telegram, Slack, Teams, voice, SMS

Migration & Switching Considerations

Switching between Cohere and Yellow.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 Yellow.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 Yellow.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: February 24, 2026 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.

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Priyansh Khodiyar

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

Cohere vs Yellow.ai