Vertex AI vs Yellow.ai

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

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Priyansh Khodiyar's avatar
Priyansh KhodiyarDevRel at CustomGPT.ai

Fact checked and reviewed by Bill Cava

Published: 01.04.2025Updated: 25.04.2025

In this comprehensive guide, we compare Vertex AI 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 Vertex AI 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 Vertex AI if: you value industry-leading 2m token context window with gemini models
  • Choose Yellow.ai if: you value genuinely comprehensive 35+ channel coverage: whatsapp bsp, messenger, instagram, telegram, slack, teams, voice, sms

About Vertex AI

Vertex AI Landing Page Screenshot

Vertex AI is google's unified ml platform with gemini models and automl. Vertex AI is Google Cloud's comprehensive machine learning platform that unifies data engineering, data science, and ML engineering workflows. It offers state-of-the-art Gemini models with industry-leading context windows up to 2 million tokens, AutoML capabilities, and enterprise-grade infrastructure for building, deploying, and scaling AI applications. Founded in 2008, headquartered in Mountain View, CA, the platform has established itself as a reliable solution in the RAG space.

Overall Rating
88/100
Starting Price
Custom

About Yellow.ai

Yellow.ai Landing Page Screenshot

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: AI Chatbot 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

logo of vertexai
Vertex AI
logo of yellow
Yellow.ai
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CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
  • Multi-format support – Structured/unstructured data from Google Cloud Storage (PDF, HTML, CSV) (Vertex AI Search)
  • Google web-crawling – Automatically ingests relevant public website content into indexes (Towards AI)
  • ✅ Continuous ingestion – Auto-indexing keeps knowledge base current without manual updates
  • BigQuery integration – Direct connection to structured data sources for real-time analytics integration
  • 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
  • REST APIs & SDKs – Python, Java, JavaScript libraries for web/mobile/enterprise apps (API Docs)
  • GCP ecosystem – Native BigQuery, Dataflow, Cloud Functions integration with unified billing (GCP Connectors)
  • Low-code connectors – Logic Apps and PowerApps for non-developer integrations
  • Flexible deployment – Custom front-ends, embedded widgets, or standalone conversational agents
  • 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 Chatbot Features
  • RAG-powered answers – Vertex AI Search + Conversation grounds responses in indexed data (RAG Engine)
  • Google LLMs – PaLM 2 and Gemini models for context-aware reasoning
  • Multi-turn context – Maintains conversation coherence across dialogue sessions
  • ✅ Session memory – Stores interactions for personalized agent responses and continuity
  • 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
Customization & Branding
  • UI customization – Cloud console settings for themes, logos, domain restrictions (Console)
  • Design system integration – Tie into existing brand guidelines for consistent styling
  • ⚠️ Limited no-code – Customization requires technical expertise, not full drag-and-drop builder
  • 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
  • Google models – PaLM 2, Gemini family; external LLM API support (Models)
  • Flexible selection – Choose models balancing cost, speed, and quality per use case
  • Prompt templates – Customize tone, format, citation rules through prompt engineering
  • ⚠️ Limited diversity – No native Claude, GPT-4, or Llama support vs multi-model platforms
  • 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)
  • REST APIs & SDKs – Python, Java, JavaScript libraries with comprehensive documentation (SDK Docs)
  • Sample notebooks – Quick-start guides, Jupyter notebooks, and GitHub examples for rapid integration
  • IAM security – Google Cloud IAM for secure API calls and access control
  • ✅ CLI tooling – Command-line interface for local development and automation workflows
  • 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
  • ✅ Millisecond responses – Global infrastructure delivers sub-second query performance worldwide (RAG Engine)
  • Hybrid search – Combines semantic vectors with keyword (BM25) matching for accuracy
  • Advanced reranking – Multi-stage pipeline reduces hallucinations and ensures factual consistency
  • Consistency scoring – Returns factual-consistency score with every answer for reliability assessment
  • 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
Customization & Flexibility ( Behavior & Knowledge)
  • Fine-grained indexing – Control chunk sizes, metadata tags, retrieval parameters (Search APIs)
  • Generation controls – Adjust temperature, max tokens, prompt templates for domain-specific responses
  • Custom skills – Integrate custom cognitive processing or open-source models for specialized requirements
  • ✅ Semantic weighting – Balance semantic and keyword search per query type for optimal retrieval
  • 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
Pricing & Scalability
  • Pay-as-you-go – Charges for storage, queries, model compute; $300 free tier (Pricing)
  • ✅ Autoscaling – Global infrastructure automatically adjusts resources, prevents overprovisioning
  • Enterprise discounts – Volume discounts and committed use for GCP enterprise agreements
  • ⚠️ Cost monitoring needed – Requires careful tracking to prevent unexpected costs at scale
  • 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
  • ✅ Enterprise encryption – TLS 1.3 in transit, AES-256 at rest, fine-grained IAM (Compliance)
  • SOC/ISO/HIPAA/GDPR – Comprehensive certifications with customer-managed encryption keys (CMEK)
  • Private Link – Private network connectivity for on-premise to GCP network isolation
  • Audit logs – Cloud Audit Logs track all API calls and configuration changes
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
  • ✅ Operations Suite – Real-time monitoring, logging, alerting via Google Cloud (Monitoring)
  • Performance dashboards – Query latency, index health, resource usage metrics with custom analytics APIs
  • Log exports – Export logs and metrics for compliance or deep-dive analysis needs
  • Trace integration – Cloud Trace provides comprehensive agent behavior and performance tracking
  • 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
  • Enterprise support – 24/7 support tiers with SLAs and dedicated account managers (Support)
  • Community & training – Forums, sample projects, certification paths, hands-on labs
  • ✅ Partner ecosystem – Robust network of consulting, implementation, and managed service partners
  • Regular updates – Continuous R&D investment in RAG and generative AI capabilities
  • 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
Additional Considerations
  • Factual scoring – Hybrid search returns consistency scores with every answer for reliability
  • Deployment flexibility – Public cloud, VPC, or on-premise for data-residency compliance
  • ✅ Continuous innovation – Google's ongoing R&D investment in RAG and generative AI
  • 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
No- Code Interface & Usability
  • Cloud console – Manage indexes and search settings via browser interface
  • ⚠️ No drag-and-drop builder – Agent Builder (2024) added visual interface, but limited vs specialized platforms
  • Low-code connectors – PowerApps, Logic Apps simplify basic integrations for non-developers
  • ⚠️ Technical expertise required – Deeper customization needs GCP and developer skills
  • 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
Competitive Positioning
  • Market position: Enterprise-grade Google Cloud AI platform combining Vertex AI Search with Conversation for production-ready RAG, deeply integrated with GCP ecosystem
  • Target customers: Organizations already invested in Google Cloud infrastructure, enterprises requiring PaLM 2/Gemini models with Google's search capabilities, and companies needing global scalability with multi-region deployment and GCP service integration
  • Key competitors: Azure AI Search, AWS Bedrock, OpenAI Enterprise, Coveo, and custom RAG implementations
  • Competitive advantages: Native Google PaLM 2/Gemini models with external LLM support, Google's web-crawling infrastructure for public content ingestion, integrated GCP services (BigQuery, Dataflow, Cloud Functions), hybrid search with advanced reranking, SOC/ISO/HIPAA/GDPR compliance with customer-managed keys, global infrastructure for millisecond responses worldwide, and Google Cloud Operations Suite for comprehensive monitoring
  • Pricing advantage: Pay-as-you-go with free tier for development; competitive for GCP customers leveraging existing enterprise agreements and volume discounts; autoscaling prevents overprovisioning; best value for organizations with GCP infrastructure wanting unified billing and managed services
  • Use case fit: Best for organizations already using GCP infrastructure (BigQuery, Cloud Functions), enterprises needing Google's proprietary models (PaLM 2, Gemini) with web-crawling capabilities, and companies requiring global scalability with multi-region deployment and tight integration with GCP analytics and data pipelines
  • 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
A I Models
  • PaLM 2 & Gemini family – Gemini 2.5 Pro/Flash, 2.0 Flash optimized for enterprise workloads
  • Gemini 2.5 Pro – $1.25-$2.50/M input, $10-$15/M output for advanced multimodal reasoning
  • Gemini 2.5 Flash – $0.30/M input, $2.50/M output for cost-effective high-speed inference
  • Gemini 2.0 Flash – $0.15/M input, $0.60/M output for ultra-low-cost deployment
  • External LLM support – Call external APIs if preferring non-Google models
  • ⚠️ Limited diversity – No native Claude, GPT-4, Llama vs multi-model platforms
  • 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
  • ✅ Hybrid search – Semantic vectors + keyword (BM25) matching for strong retrieval accuracy
  • Advanced reranking – Multi-stage pipeline reduces hallucinations, ensures factual consistency scores
  • Google web-crawling – Automatically ingests public website content into indexes
  • Fine-grained control – Chunk sizes, metadata tags, semantic/lexical weighting per query type
  • Multi-format support – BigQuery structured data and unstructured docs (PDF, HTML, CSV)
  • Custom skills – Integrate custom processing or open-source models for specialized requirements
  • 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
  • ✅ GCP-native orgs – Unified AI with BigQuery, Cloud Functions, Dataflow, unified billing
  • Global deployments – Multi-region with millisecond responses worldwide via Google's infrastructure
  • Multimodal AI – Gemini processes text, images, videos, code for rich content analysis
  • Workspace integration – Gmail, Docs, Sheets for content-heavy workflows in Workspace ecosystem
  • Regulated industries – Healthcare, finance, government with SOC/ISO/HIPAA/GDPR compliance and CMEK
  • 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
Security & Compliance
  • ✅ Enterprise encryption – TLS 1.3 in transit, AES-256 at rest, fine-grained IAM
  • SOC 2/3, ISO 27001/17/18 – Comprehensive security controls and international standards compliance
  • HIPAA & GDPR – Healthcare BAAs for PHI, EU data residency options
  • CMEK & Private Link – Customer-managed keys, private on-premise to GCP connectivity
  • Audit logs & VPC – Cloud Audit Logs track all changes; VPC/on-prem deployment options
  • 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
Pricing & Plans
  • Pay-as-you-go – Storage, queries, model compute; $300 free tier for experiments
  • Gemini 2.5 Pro – $1.25-$2.50/M input, $10-$15/M output for advanced reasoning
  • Gemini 2.5 Flash – $0.30/M input, $2.50/M output for cost-effective inference
  • Gemini 2.0 Flash – $0.15/M input, $0.60/M output for ultra-low-cost scale
  • ✅ Unified billing – Single GCP bill for all services; enterprise volume discounts available
  • ⚠️ Cost monitoring needed – Requires tracking to prevent unexpected costs at scale
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
Support & Documentation
  • Enterprise support tiers – Basic to Premium with SLAs, 24/7 support, 15-min P1 response
  • ✅ Comprehensive docs – Detailed guides at cloud.google.com/vertex-ai/docs covering APIs, SDKs, tutorials
  • Sample projects – Pre-built examples, Jupyter notebooks, GitHub quick-starts for rapid integration
  • Training & certification – Hands-on labs, certification paths for Vertex AI and ML
  • Partner ecosystem – Robust network offering consulting, implementation, managed services
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
Limitations & Considerations
  • ⚠️ GCP ecosystem dependency – Strongest value for GCP users; less compelling for AWS/Azure-native orgs
  • ⚠️ No full no-code builder – Agent Builder (2024) added GUI but limited vs specialized platforms
  • ⚠️ Google models only – PaLM 2/Gemini only; no native Claude, GPT-4, Llama support
  • ⚠️ Technical expertise required – Customization needs developer skills, not for non-technical teams
  • ⚠️ Vendor lock-in – Deep GCP integration creates switching costs to alternative providers
  • ⚠️ Overkill for simple cases – Enterprise capabilities unnecessary for basic FAQ bots
  • ⚠️ 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 Agent Features
  • Agent Engine – Autonomous agents with short/long-term memory for session management and personalization
  • Agent Builder (2024) – Visual drag-and-drop, LlamaIndex/LangChain integrations, RAG with real-time retrieval
  • ✅ Multi-turn context – Sessions store interactions for coherent dialogue and context persistence
  • Human handoff – Interaction summaries, citations, conversation history for AI-to-human transitions
  • Agent orchestration – Cross-system context, dynamic capability discovery, automated back-end interactions
  • ⚠️ No native lead capture – Focuses on enterprise AI, not marketing automation features
  • 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
R A G-as-a- Service Assessment
  • ✅ TRUE RAG-as-a-Service – Fully managed orchestration with developer-first APIs for production-ready implementations
  • RAG Engine (GA 2024) – Streamlines vector search, chunking, embedding, retrieval automatically
  • API-first design – Comprehensive APIs with VPC-SC security, CMEK support for rapid prototyping
  • Customization depth – Various parsing, chunking, embedding, vector storage options with open-source integration
  • Enterprise readiness – SOC/ISO/HIPAA/GDPR, CMEK, Private Link, audit logs, Operations Suite monitoring
  • ⚠️ GCP lock-in – Strongest for GCP customers; less compelling for AWS/Azure vs platform-agnostic options
  • 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

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Final Thoughts

Final Verdict: Vertex AI vs Yellow.ai

After analyzing features, pricing, performance, and user feedback, both Vertex AI and Yellow.ai are capable platforms that serve different market segments and use cases effectively.

When to Choose Vertex AI

  • You value industry-leading 2m token context window with gemini models
  • Comprehensive ML platform covering entire AI lifecycle
  • Deep integration with Google Cloud ecosystem

Best For: Industry-leading 2M token context window with Gemini models

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 Vertex AI 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

Vertex AI 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

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

For most organizations, the decision between Vertex AI 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's avatar

Priyansh Khodiyar

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

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