SimplyRetrieve vs Vertex AI

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SimplyRetrieve vs Vertex AI Comparison
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 SimplyRetrieve 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

Welcome to the comparison between SimplyRetrieve and Vertex AI!

Here are some unique insights on SimplyRetrieve:

SimplyRetrieve is an open-source RAG stack you run on your own hardware. It keeps data in-house and pairs with open-source LLMs, giving developers full visibility into the pipeline.

Expect hands-on setup—GPU drivers, Python deps, scripts—before you’re up and running.

And here's more information on Vertex AI:

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Enjoy reading and exploring the differences between SimplyRetrieve and Vertex AI.

Detailed Feature Comparison

Features
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SimplyRetrieve
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Vertex AI
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Data Ingestion & Knowledge Sources
  • Uses a hands-on, file-based flow: drop PDFs, text, DOCX, PPTX, HTML, etc. into a folder and run a script to embed them.
  • A new GUI Knowledge-Base editor lets you add docs on the fly, but there’s no web crawler or auto-refresh yet.
  • Pulls in both structured and unstructured data straight from Google Cloud Storage, handling files like PDF, HTML, and CSV (Vertex AI Search Overview).
  • Taps into Google’s own web-crawling muscle to fold relevant public website content into your index with minimal fuss (Towards AI Vertex AI Search).
  • Keeps everything current with continuous ingestion and auto-indexing, so your knowledge base never falls out of date.
  • Lets you ingest more than 1,400 file formats—PDF, DOCX, TXT, Markdown, HTML, and many more—via simple drag-and-drop or API.
  • Crawls entire sites through sitemaps and URLs, automatically indexing public help-desk articles, FAQs, and docs.
  • Turns multimedia into text on the fly: YouTube videos, podcasts, and other media are auto-transcribed with built-in OCR and speech-to-text. View Transcription Guide
  • Connects to Google Drive, SharePoint, Notion, Confluence, HubSpot, and more through API connectors or Zapier. See Zapier Connectors
  • Supports both manual uploads and auto-sync retraining, so your knowledge base always stays up to date.
Integrations & Channels
  • Ships with a local Gradio GUI and Python scripts for queries—no out-of-the-box Slack or site widget.
  • Want other channels? Write a small wrapper that forwards messages to your local chatbot.
  • Ships solid REST APIs and client libraries for weaving Vertex AI into web apps, mobile apps, or enterprise portals (Google Cloud Vertex AI API Docs).
  • Plays nicely with other Google Cloud staples—BigQuery, Dataflow, and more—and even supports low-code connectors via Logic Apps and PowerApps (Google Cloud Connectors).
  • Lets you deploy conversational agents wherever you need them, whether that’s a bespoke front-end or an embedded widget.
  • Embeds easily—a lightweight script or iframe drops the chat widget into any website or mobile app.
  • Offers ready-made hooks for Slack, Microsoft Teams, WhatsApp, Telegram, and Facebook Messenger. Explore API Integrations
  • Connects with 5,000+ apps via Zapier and webhooks to automate your workflows.
  • Supports secure deployments with domain allowlisting and a ChatGPT Plugin for private use cases.
Core Chatbot Features
  • Runs a retrieval-augmented chatbot on open-source LLMs, streaming tokens live in the Gradio UI.
  • Primarily single-turn Q&A; long-term memory is limited in this release.
  • Includes a “Retrieval Tuning Module” so you can see—and tweak—how answers are built from the data.
  • Pairs Vertex AI Search with Vertex AI Conversation to craft answers grounded in your indexed data (Google Developers Blog Vertex AI RAG).
  • Draws on Google’s PaLM 2 or Gemini models for rich, context-aware responses.
  • Handles multi-turn dialogue and keeps track of context so chats stay coherent.
  • Powers retrieval-augmented Q&A with GPT-4 and GPT-3.5 Turbo, keeping answers anchored to your own content.
  • Reduces hallucinations by grounding replies in your data and adding source citations for transparency. Benchmark Details
  • Handles multi-turn, context-aware chats with persistent history and solid conversation management.
  • Speaks 90+ languages, making global rollouts straightforward.
  • Includes extras like lead capture (email collection) and smooth handoff to a human when needed.
Customization & Branding
  • Default Gradio interface is pretty plain, with minimal theming.
  • For a branded UI you’ll tweak source code or build your own front end.
  • Lets you tweak UI elements in the Cloud console so your chatbot matches your brand style.
  • Includes settings for custom themes, logos, and domain restrictions when you embed search or chat (Google Cloud Console).
  • Makes it easy to keep branding consistent by tying into your existing design system.
  • Fully white-labels the widget—colors, logos, icons, CSS, everything can match your brand. White-label Options
  • Provides a no-code dashboard to set welcome messages, bot names, and visual themes.
  • Lets you shape the AI’s persona and tone using pre-prompts and system instructions.
  • Uses domain allowlisting to ensure the chatbot appears only on approved sites.
L L M Model Options
  • Defaults to WizardVicuna-13B, but you can swap in any Hugging Face model if you have the GPUs.
  • Full control over model choice, though smaller open models won’t match GPT-4 for depth.
  • Connects to Google’s own generative models—PaLM 2, Gemini—and can call external LLMs via API if you prefer (Google Cloud Vertex AI Models).
  • Lets you pick models based on your balance of cost, speed, and quality.
  • Supports prompt-template tweaks so you can steer tone, format, and citation rules.
  • Taps into top models—OpenAI’s GPT-4, GPT-3.5 Turbo, and even Anthropic’s Claude for enterprise needs.
  • Automatically balances cost and performance by picking the right model for each request. Model Selection Details
  • Uses proprietary prompt engineering and retrieval tweaks to return high-quality, citation-backed answers.
  • Handles all model management behind the scenes—no extra API keys or fine-tuning steps for you.
Developer Experience ( A P I & S D Ks)
  • Interaction happens via Python scripts—there’s no formal REST API or SDK.
  • Integrations usually call those scripts as subprocesses or add your own wrapper.
  • Offers full REST APIs plus client libraries for Python, Java, JavaScript, and more (Google Cloud Vertex AI SDK).
  • Backs you up with rich docs, sample notebooks, and quick-start guides.
  • Uses Google Cloud IAM for secure API calls and supports CLI tooling for local dev work.
  • Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat. API Documentation
  • Offers open-source SDKs—like the Python customgpt-client—plus Postman collections to speed integration. Open-Source SDK
  • Backs you up with cookbooks, code samples, and step-by-step guides for every skill level.
Integration & Workflow
  • Run it locally: prep a GPU box, drop data, run prepare.py to embed, then chat.py for the Gradio UI.
  • Updating content means re-running scripts or using the new Knowledge tab; scaling is a manual process.
  • Snaps into other GCP services—BigQuery, Dataflow, Cloud Functions—for end-to-end workflows (Google Cloud Architecture).
  • Follows a modular, API-driven design so you can mix search and chat components the way you want.
  • Automates tasks via connectors or custom code to tie into CRMs, ticketing tools, and beyond.
  • Gets you live fast with a low-code dashboard: create a project, add sources, and auto-index content in minutes.
  • Fits existing systems via API calls, webhooks, and Zapier—handy for automating CRM updates, email triggers, and more. Auto-sync Feature
  • Slides into CI/CD pipelines so your knowledge base updates continuously without manual effort.
Performance & Accuracy
  • Open-source models run slower than managed clouds—expect a few to 10 + seconds per reply on a single GPU.
  • Accuracy is fine when the right doc is found, but smaller models can struggle on complex, multi-hop queries.
  • Serves answers in milliseconds thanks to Google’s global infrastructure (Google Cloud Vertex AI RAG).
  • Combines semantic and keyword search for strong retrieval accuracy.
  • Adds advanced reranking to cut hallucinations and keep facts straight.
  • Delivers sub-second replies with an optimized pipeline—efficient vector search, smart chunking, and caching.
  • Independent tests rate median answer accuracy at 5/5—outpacing many alternatives. Benchmark Results
  • Always cites sources so users can verify facts on the spot.
  • Maintains speed and accuracy even for massive knowledge bases with tens of millions of words.
Customization & Flexibility ( Behavior & Knowledge)
  • Lets you tweak everything—KnowledgeBase weight, retrieval params, system prompts—for deep control.
  • Encourages devs to swap embedding models or hack the pipeline code as needed.
  • Gives fine-grained control over indexing—set chunk sizes, metadata tags, and more to shape retrieval (Google Cloud Vertex AI Search).
  • Lets you adjust generation knobs (temperature, max tokens) and craft prompt templates for domain-specific flair.
  • Can slot in custom cognitive skills or open-source models when you need specialized processing.
  • Lets you add, remove, or tweak content on the fly—automatic re-indexing keeps everything current.
  • Shapes agent behavior through system prompts and sample Q&A, ensuring a consistent voice and focus. Learn How to Update Sources
  • Supports multiple agents per account, so different teams can have their own bots.
  • Balances hands-on control with smart defaults—no deep ML expertise required to get tailored behavior.
Pricing & Scalability
  • Free, MIT-licensed open source—no fees, but you supply the GPUs or cloud servers.
  • Scaling means spinning up more hardware and managing it yourself.
  • Uses pay-as-you-go pricing—charges for storage, query volume, and model compute—with a free tier to experiment (Google Cloud Pricing).
  • Scales effortlessly on Google’s global backbone, with autoscaling baked in.
  • Add partitions or replicas as traffic grows to keep performance rock-solid.
  • Runs on straightforward subscriptions: Standard (~$99/mo), Premium (~$449/mo), and customizable Enterprise plans.
  • Gives generous limits—Standard covers up to 60 million words per bot, Premium up to 300 million—all at flat monthly rates. View Pricing
  • Handles scaling for you: the managed cloud infra auto-scales with demand, keeping things fast and available.
Security & Privacy
  • Entirely local: all docs and chat data stay on your own machine—great for sensitive use cases.
  • No built-in auth or enterprise security—lock things down in your own deployment setup.
  • Builds on Google Cloud’s security stack—encryption in transit and at rest, plus fine-grained IAM (Google Cloud Compliance).
  • Holds a long list of certifications (SOC, ISO, HIPAA, GDPR) and supports customer-managed encryption keys.
  • Offers options like Private Link and detailed audit logs to satisfy strict enterprise requirements.
  • Protects data in transit with SSL/TLS and at rest with 256-bit AES encryption.
  • Holds SOC 2 Type II certification and complies with GDPR, so your data stays isolated and private. Security Certifications
  • Offers fine-grained access controls—RBAC, two-factor auth, and SSO integration—so only the right people get in.
Observability & Monitoring
  • An “Analysis” tab shows which docs were pulled and how the query was built; logs print to the console.
  • No fancy dashboard—add your own logging or monitoring if you need broader stats.
  • Hooks into Google Cloud Operations Suite for real-time monitoring, logging, and alerting (Google Cloud Monitoring).
  • Includes dashboards for query latency, index health, and resource usage, plus APIs for custom analytics.
  • Lets you export logs and metrics to meet compliance or deep-dive analysis needs.
  • Comes with a real-time analytics dashboard tracking query volumes, token usage, and indexing status.
  • Lets you export logs and metrics via API to plug into third-party monitoring or BI tools. Analytics API
  • Provides detailed insights for troubleshooting and ongoing optimization.
Support & Ecosystem
  • Open-source on GitHub; support is community-driven via issues and lightweight docs.
  • Smaller ecosystem: you’re free to fork or extend, but there’s no paid SLA or enterprise help desk.
  • Backed by Google’s enterprise support programs and detailed docs across the Cloud platform (Google Cloud Support).
  • Provides community forums, sample projects, and training via Google Cloud’s dev channels.
  • Benefits from a robust ecosystem of partners and ready-made integrations inside GCP.
  • Supplies rich docs, tutorials, cookbooks, and FAQs to get you started fast. Developer Docs
  • Offers quick email and in-app chat support—Premium and Enterprise plans add dedicated managers and faster SLAs. Enterprise Solutions
  • Benefits from an active user community plus integrations through Zapier and GitHub resources.
Additional Considerations
  • Great for offline / on-prem labs where data never leaves the server—perfect for tinkering.
  • Takes more hands-on upkeep and won’t match proprietary giants in sheer capability out of the box.
  • Packs hybrid search and reranking that return a factual-consistency score with every answer.
  • Supports public cloud, VPC, or on-prem deployments if you have strict data-residency rules.
  • Gets regular updates as Google pours R&D into RAG and generative AI capabilities.
  • Slashes engineering overhead with an all-in-one RAG platform—no in-house ML team required.
  • Gets you to value quickly: launch a functional AI assistant in minutes.
  • Stays current with ongoing GPT and retrieval improvements, so you’re always on the latest tech.
  • Balances top-tier accuracy with ease of use, perfect for customer-facing or internal knowledge projects.
No- Code Interface & Usability
  • Basic Gradio UI is developer-focused; non-tech users might find the settings overwhelming.
  • No slick, no-code admin—if you need polish or branding, you’ll build your own front end.
  • Offers a Cloud console to manage indexes and search settings, though there’s no full drag-and-drop chatbot builder yet.
  • Low-code connectors (PowerApps, Logic Apps) make basic integrations straightforward for non-devs.
  • The overall experience is solid, but deeper customization still calls for some technical know-how.
  • Offers a wizard-style web dashboard so non-devs can upload content, brand the widget, and monitor performance.
  • Supports drag-and-drop uploads, visual theme editing, and in-browser chatbot testing. User Experience Review
  • Uses role-based access so business users and devs can collaborate smoothly.

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Final Thoughts

We hope you found this comparison of SimplyRetrieve vs Vertex AI helpful.

If local control and privacy outweigh convenience, SimplyRetrieve is a solid DIY route. Just be ready for the ongoing maintenance that comes with a self-hosted system.

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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.