OpenAI vs Vertex 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 OpenAI and Vertex 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 OpenAI and Vertex 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 OpenAI if: you value industry-leading model performance
  • Choose Vertex AI if: you value industry-leading 2m token context window with gemini models

About OpenAI

OpenAI Landing Page Screenshot

OpenAI is leading ai research company and api provider. OpenAI provides state-of-the-art language models and AI capabilities through APIs, including GPT-4, assistants with retrieval capabilities, and various AI tools for developers and enterprises. Founded in 2015, headquartered in San Francisco, CA, the platform has established itself as a reliable solution in the RAG space.

Overall Rating
90/100
Starting Price
Custom

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

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 Platform versus AI Chatbot. These differences make each platform better suited for specific use cases and organizational requirements.

⚠️ What This Comparison Covers

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

Detailed Feature Comparison

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OpenAI
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Vertex AI
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CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
  • ✅ Embeddings API – text-embedding models generate vectors for semantic search workflows
  • ⚠️ DIY Pipeline – No ready-made ingestion; build chunking, indexing, refreshing yourself
  • Azure File Search – Beta preview tool accepts uploads for semantic search
  • Manual Architecture – Embed docs → vector DB → retrieve chunks at query time
  • 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
  • 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
  • ⚠️ No First-Party Channels – Build Slack bots, widgets, integrations yourself or use third-party
  • ✅ API Flexibility – Run GPT anywhere; channel-agnostic engine for custom implementations
  • Community Tools – Zapier, community Slack bots exist but aren't official OpenAI
  • Manual Wiring – Everything is code-based; no out-of-the-box UI or connectors
  • 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
  • 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
  • ✅ Multi-Turn Chat – GPT-4/3.5 handle conversations; you resend history for context
  • ⚠️ No Agent Memory – OpenAI doesn't store conversational state; you manage it
  • Function Calling – Model triggers your functions (search endpoints); you wire retrieval
  • ChatGPT Web UI – Separate from API; not brand-customizable for private data
  • 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
  • ✅ #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
  • ⚠️ No Turnkey UI – Build branded front-end yourself; no theming layer provided
  • System Messages – Set tone/style via prompts; white-label chat requires development
  • ChatGPT Custom Instructions – Apply only inside ChatGPT app, not embedded widgets
  • Developer Project – All branding, UI customization is your responsibility
  • 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
  • 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
  • ✅ GPT-4 Family – GPT-4 (8k/32k), GPT-4 Turbo (128k), GPT-4o top-tier performance
  • ✅ GPT-3.5 Family – GPT-3.5 Turbo (4k/16k) cost-effective for high-volume use
  • ⚠️ OpenAI-Only – Cannot swap to Claude, Gemini; locked to OpenAI ecosystem
  • Manual Routing – Developer chooses model per request; no automatic selection
  • ✅ Frequent Upgrades – Regular releases with larger context windows and better benchmarks
  • 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
  • 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)
  • ✅ Excellent Docs – Official Python/Node.js SDKs; comprehensive API reference and guides
  • Function Calling – Simplifies prompting; you build RAG pipeline (indexing, retrieval, assembly)
  • Framework Support – Works with LangChain/LlamaIndex (third-party tools, not OpenAI products)
  • ⚠️ No Reference Architecture – Vast community examples but no official RAG blueprint
  • 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
  • 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
  • ✅ GPT-4 Top-Tier – Leading performance for language tasks; requires RAG for domain accuracy
  • ⚠️ Hallucination Risk – Can hallucinate on private/recent data without retrieval implementation
  • Well-Built RAG Delivers – High accuracy achievable with proper indexing, chunking, prompt design
  • Latency Considerations – Larger models (128k context) add latency; scales well under load
  • ✅ 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
  • 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-Tuning Available – GPT-3.5 fine-tuning for style; knowledge injection via RAG code
  • ⚠️ Content Freshness – Re-embed, re-fine-tune, or pass context each call; developer overhead
  • Tool Calling Power – Powerful moderation/tools but requires thoughtful design; no unified UI
  • Maximum Flexibility – Extremely flexible for general AI; lacks built-in document management
  • 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
  • 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 – $0.0015/1K tokens GPT-3.5; ~$0.03-0.06/1K GPT-4 token pricing
  • ⚠️ Scale Costs – Great low usage; bills spike at scale with rate limits
  • No Flat Rate – Consumption-based only; cover external hosting (vector DB) separately
  • Enterprise Contracts – Higher concurrency, compliance features, dedicated capacity via sales
  • 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
  • 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
  • ✅ API Data Privacy – Not used for training; 30-day retention for abuse checks
  • ✅ Encryption Standard – TLS in transit, at rest encryption; ChatGPT Enterprise adds SOC 2/SSO
  • ⚠️ Developer Responsibility – You secure user inputs, logs, auth, HIPAA/GDPR compliance
  • No User Portal – Build auth/access control in your own front-end
  • ✅ 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
  • 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
  • ⚠️ Basic Dashboard – Tracks monthly token spend, rate limits; no conversation analytics
  • DIY Logging – Log Q&A traffic yourself; no specialized RAG metrics
  • Status Page – Uptime monitoring, error codes, rate-limit headers available
  • Community Solutions – Datadog/Splunk setups shared; you build monitoring pipeline
  • ✅ 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
  • 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
  • ✅ Massive Community – Thorough docs, code samples; direct support requires Enterprise
  • Third-Party Frameworks – Slack bots, LangChain, LlamaIndex building blocks abound
  • Broad AI Focus – Text, speech, images; RAG is one of many use cases
  • Enterprise Premium Support – Success managers, SLAs, compliance environment for Enterprise customers
  • 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
  • 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
  • ✅ Maximum Freedom – Best for bespoke AI solutions beyond RAG (code gen, creative writing)
  • ✅ Regular Upgrades – Frequent model releases with bigger context windows keep tech current
  • ⚠️ Coding Required – Near-infinite customization comes with setup complexity; developer-friendly only
  • Cost Management – Token pricing cost-effective at small scale; maintaining RAG adds ongoing effort
  • 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
  • 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
  • ⚠️ Not No-Code – Requires coding embeddings, retrieval, chat UI; no-code OpenAI options minimal
  • ChatGPT Web App – User-friendly but not embeddable with your data/branding by default
  • Third-Party Tools – Zapier/Bubble offer partial integrations; not official OpenAI solutions
  • Developer-Focused – Extremely capable for coders; less for non-technical teams wanting self-serve
  • 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
  • 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 – Leading AI model provider; top GPT models as custom AI building blocks
  • Target Customers – Dev teams building bespoke solutions; enterprises needing flexibility beyond RAG
  • Key Competitors – Anthropic Claude API, Google Gemini, Azure AI, AWS Bedrock, RAG platforms
  • ✅ Competitive Advantages – Top GPT-4 performance, frequent upgrades, excellent docs, massive ecosystem, Enterprise SOC 2/SSO
  • ✅ Pricing Advantage – Pay-as-you-go highly cost-effective at small scale; best value low-volume use
  • Use Case Fit – Ideal for custom AI requiring flexibility; less suitable for turnkey RAG without dev resources
  • 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
  • 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
  • ✅ GPT-4 Family – GPT-4 (8k/32k), GPT-4 Turbo (128k), GPT-4o - top language understanding/generation
  • ✅ GPT-3.5 Family – GPT-3.5 Turbo (4k/16k) cost-effective with good performance
  • ✅ Frequent Upgrades – Regular releases with improved capabilities, larger context windows
  • ⚠️ OpenAI-Only – Cannot swap to Claude, Gemini; locked to OpenAI models
  • ✅ Fine-Tuning – GPT-3.5 fine-tuning for domain-specific customization with training data
  • 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
  • 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
  • ⚠️ NO Built-In RAG – LLM models only; build entire RAG pipeline yourself
  • ✅ Embeddings API – text-embedding-ada-002 and newer for vector embeddings/semantic search
  • DIY Architecture – Embed docs → external vector DB → retrieve → inject into prompt
  • Azure Assistants Preview – Beta File Search tool; minimal, preview-stage only
  • Framework Integration – Works with LangChain/LlamaIndex (third-party, not OpenAI products)
  • ⚠️ Developer Responsibility – Chunking, indexing, retrieval optimization all require custom code
  • ✅ 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
  • 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
  • ✅ Custom AI Applications – Bespoke solutions requiring maximum flexibility beyond pre-packaged platforms
  • ✅ Code Generation – GitHub Copilot-style tools, IDE integrations, automated review
  • ✅ Creative Writing – Content generation, marketing copy, storytelling at scale
  • ✅ Data Analysis – Natural language queries over structured data, report generation
  • Customer Service – Custom chatbots integrated with business systems and knowledge bases
  • ⚠️ NOT IDEAL FOR – Non-technical teams wanting turnkey RAG chatbot without coding
  • ✅ 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 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
  • ✅ API Data Privacy – Not used for training; 30-day retention for abuse checks only
  • ✅ ChatGPT Enterprise – SOC 2 Type II, SSO, stronger privacy, enterprise-grade security
  • ✅ Encryption – TLS in transit, at rest encryption with enterprise standards
  • ✅ GDPR/HIPAA – DPA for GDPR; BAA for HIPAA; regional data residency available
  • ✅ Zero-Retention Option – Enterprise/API customers can opt for no data retention
  • ⚠️ Developer Responsibility – User auth, input validation, logging entirely on you
  • ✅ 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 + 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 – $0.0015/1K tokens GPT-3.5; ~$0.03-0.06/1K GPT-4 token pricing
  • ✅ No Platform Fees – Pure consumption pricing; no subscriptions, monthly minimums
  • Rate Limits by Tier – Usage tiers auto-increase limits as spending grows
  • ⚠️ Cost at Scale – Bills spike without optimization; high-volume needs token management
  • External Costs – RAG incurs vector DB (Pinecone, Weaviate) and hosting costs
  • ✅ Best Value For – Low-volume use or teams with existing infrastructure
  • 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
  • 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
  • ✅ Excellent Documentation – Comprehensive guides, API reference, code samples at platform.openai.com
  • ✅ Official SDKs – Well-maintained Python, Node.js libraries with examples
  • ✅ Massive Community – Extensive tutorials, LangChain/LlamaIndex integrations, ecosystem resources
  • ⚠️ Limited Direct Support – Community forums for standard users; Enterprise gets premium support
  • OpenAI Cookbook – Practical examples and recipes for common use cases including RAG
  • 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
  • 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
  • ⚠️ NO Built-In RAG – Entire retrieval infrastructure must be built by developers
  • ⚠️ Developer-Only – Requires coding expertise; no no-code interface for non-technical teams
  • ⚠️ Rate Limits – Usage tiers start restrictive (Tier 1: 500 RPM GPT-4)
  • ⚠️ Model Lock-In – Cannot use Claude, Gemini; tied to OpenAI ecosystem
  • ⚠️ NO Chat UI – ChatGPT web interface not embeddable or customizable for business
  • ⚠️ Cost at Scale – Token pricing can spike without optimization; needs cost management
  • ⚠️ 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
  • 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
  • ✅ Assistants API (v2) – Built-in conversation history, persistent threads, tool access management
  • ✅ Function Calling – Models invoke external functions/tools; describe structure, receive calls with arguments
  • ✅ Parallel Tool Execution – Access Code Interpreter, File Search, custom functions simultaneously
  • Responses API (2024) – New primitive with web search, file search, computer use
  • ✅ Structured Outputs – strict: true guarantees arguments match JSON Schema for reliable parsing
  • ⚠️ Agent Limitations – Less control vs LangChain for complex workflows; simpler assistant paradigm
  • 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
  • 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
  • ⚠️ NOT RAG-AS-A-SERVICE – Provides LLM models/APIs, not managed RAG infrastructure
  • DIY RAG Architecture – Embed docs → external vector DB → retrieve → inject into prompt
  • File Search (Beta) – Azure preview includes minimal semantic search; not production RAG
  • ⚠️ No Managed Infrastructure – Unlike CustomGPT/Vectara, leaves chunking, indexing, retrieval to developers
  • Framework vs Service – Compare to LLM APIs (Claude, Gemini), not managed RAG platforms
  • External Costs – RAG needs vector DBs (Pinecone $70+/month), hosting, embeddings API
  • ✅ 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 – 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: OpenAI vs Vertex AI

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

When to Choose OpenAI

  • You value industry-leading model performance
  • Comprehensive API features
  • Regular model updates

Best For: Industry-leading model performance

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

Migration & Switching Considerations

Switching between OpenAI and Vertex 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

OpenAI starts at custom pricing, while Vertex 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 OpenAI and Vertex 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 16, 2026 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.

See Also

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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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