Azure AI vs SimplyRetrieve

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Azure AI vs SimplyRetrieve 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 Azure AI and SimplyRetrieve 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 Azure AI and SimplyRetrieve!

Here are some unique insights on Azure AI:

Azure AI Search slots neatly into Microsoft’s cloud stack, pairing keyword and semantic search with large language models. Powerful, yes—but spinning up indexes, analyzers, and access rules can feel complex unless you already live in Azure. Keep in mind you’ll often juggle multiple services (like Azure OpenAI) to get full RAG power.

And here's more information 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.

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

Detailed Feature Comparison

Features
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Azure AI
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SimplyRetrieve
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Data Ingestion & Knowledge Sources
  • Lets you pull data from almost anywhere—databases, blob storage, or common file types like PDF, DOCX, and HTML—as shown in the Azure AI Search overview.
  • Uses Azure pipelines and connectors to tap into a wide range of content sources, so you can set up indexing exactly the way you need.
  • Keeps everything in sync through Azure services, ensuring your information stays current without extra effort.
  • 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.
  • 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
  • Provides full-featured SDKs and REST APIs that slot right into Azure’s ecosystem—including Logic Apps and PowerApps (Azure Connectors).
  • Supports easy embedding via web widgets and offers native hooks for Slack, Microsoft Teams, and other channels.
  • Lets you build custom workflows with Azure’s low-code tools or dive deeper with the full API for more control.
  • 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.
  • 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
  • Combines semantic search with LLM generation to serve up context-rich, source-grounded answers.
  • Uses hybrid search (keyword + semantic) and optional semantic ranking to surface the most relevant results.
  • Offers multilingual support and conversation-history management, all from inside the Azure portal.
  • 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.
  • 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
  • Gives you full control over the search interface—tweak CSS, swap logos, or craft welcome messages to fit your brand.
  • Supports domain restrictions and white-labeling through straightforward Azure configuration settings.
  • Lets you fine-tune search behavior with custom analyzers and synonym maps (Azure Index Configuration).
  • Default Gradio interface is pretty plain, with minimal theming.
  • For a branded UI you’ll tweak source code or build your own front end.
  • 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
  • Hooks into Azure OpenAI Service, so you can use models like GPT-4 or GPT-3.5 for generating responses.
  • Makes it easy to pick a model and shape its behavior with prompt templates and customizable system prompts.
  • Gives you the choice of Azure-hosted models or external LLMs accessed via API.
  • 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.
  • 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)
  • Packs robust REST APIs and official SDKs for C#, Python, Java, and JavaScript (Azure SDKs).
  • Backs you up with deep documentation, tutorials, and sample code covering everything from index management to advanced queries.
  • Integrates with Azure AD for secure API access—just provision and configure from the Azure portal to get started.
  • 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.
  • 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
  • Plays nicely with the broader Azure stack, letting you combine search with Logic Apps, Power BI, and more.
  • Supports low-code integration through Azure connectors as well as custom workflows via REST calls.
  • Gives you a single Azure portal to manage index creation, data ingestion, and querying from end to end.
  • 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.
  • 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
  • Designed for enterprise scale—expect millisecond-level responses even under heavy load (Microsoft Mechanics).
  • Employs hybrid search and semantic ranking, plus configurable scoring profiles, to keep relevance high.
  • Runs on Azure’s global infrastructure for consistently low latency and high throughput wherever your users are.
  • 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.
  • 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)
  • Gives granular control over index settings—custom analyzers, tokenizers, and synonym maps let you shape search behavior to your domain.
  • Lets you plug in custom cognitive skills during indexing for specialized processing.
  • Allows prompt customization in Azure OpenAI so you can fine-tune the LLM’s style and tone.
  • 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.
  • 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
  • Uses a pay-as-you-go model—costs depend on tier, partitions, and replicas (Pricing Guide).
  • Includes a free tier for development or small projects, with higher tiers ready for production workloads.
  • Scales on demand—add replicas and partitions as traffic grows, and tap into enterprise discounts when you need them.
  • 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.
  • 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
  • Built on Microsoft Azure’s secure platform, meeting SOC, ISO, GDPR, HIPAA, FedRAMP, and other standards (Azure Compliance).
  • Encrypts data in transit and at rest, with options for customer-managed keys and Private Link for added isolation.
  • Integrates with Azure AD to provide granular role-based access control and secure authentication.
  • 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.
  • 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
  • Offers an Azure portal dashboard where you can track indexes, query performance, and usage at a glance.
  • Ties into Azure Monitor and Application Insights for custom alerts and dashboards (Azure Monitor).
  • Lets you export logs and analytics via API for deeper, custom analysis.
  • 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.
  • 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
  • Backed by Microsoft’s extensive support network, with in-depth docs, Microsoft Learn modules, and active community forums.
  • Offers enterprise support plans featuring SLAs and dedicated channels for mission-critical deployments.
  • Benefits from a large community of Azure developers and partners who regularly share best practices.
  • 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.
  • 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
  • Deep Azure integration lets you craft end-to-end solutions without leaving the platform.
  • Combines fine-grained tuning capabilities with the reliability you’d expect from an enterprise-grade service.
  • Best suited for organizations already invested in Azure, thanks to unified billing and familiar cloud management tools.
  • 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.
  • 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
  • Provides an intuitive Azure portal where you can create indexes, tweak analyzers, and monitor performance.
  • Low-code tools like Logic Apps and PowerApps connectors help non-developers add search features without heavy coding.
  • More advanced setups—complex indexing or fine-grained configuration—may still call for technical expertise versus fully turnkey options.
  • 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 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 Azure AI vs SimplyRetrieve helpful.

Azure AI Search is a solid fit for teams deep in Azure or those needing top-shelf compliance. Newcomers may find the learning curve (and piecemeal billing) steeper than expected, so weigh the trade-offs before diving in.

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.

Stay tuned for more updates!

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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.