What are the main differences between Deepset and Vertex AI?

Deepset and Vertex AI differ in features, pricing, and target audience. Deepset excels in specific use cases while Vertex AI offers different strengths. Our detailed comparison covers all key differences to help you choose.

Which is better: Deepset or Vertex AI?

The better choice between Deepset and Vertex AI depends on your specific needs. Consider factors like budget, required features, team size, and technical requirements. Our comparison tool helps you evaluate both options side-by-side.

How much does Deepset cost compared to Vertex AI?

Pricing varies between Deepset and Vertex AI. Both offer different pricing tiers and models. Check our detailed pricing comparison to see which fits your budget and requirements better.

Can I switch from Deepset to Vertex AI easily?

Migration between Deepset and Vertex AI depends on your current setup and data. Many tools offer migration assistance or import features. Review our migration guide section for specific steps and considerations.

What integrations do Deepset and Vertex AI support?

Both Deepset and Vertex AI offer various integrations with popular tools and platforms. Our comparison includes a detailed integration matrix to help you ensure compatibility with your existing tech stack.

Deepset vs Vertex AI

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

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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 Deepset 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 Deepset and Vertex AI!

Here are some unique insights on Deepset:

Deepset lets you stitch together RAG pipelines piece by piece: link data sources, choose models, tweak retrieval steps. Developers love the freedom, but casual users may find the learning curve steep.

And here's more information on Vertex AI:

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

Detailed Feature Comparison

Features	Deepset	Vertex AI	CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources	Gives developers a flexible framework to wire up connectors and process nearly any file type or data source with libraries like Unstructured. Lets you push content into vector stores such as OpenSearch, Pinecone, Weaviate, or Snowflake—pick the backend that fits best. Learn more Setup is hands-on, but the payoff is deep, domain-specific customization of your ingestion pipelines.	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	API-first approach—drop the RAG system into your own app through REST endpoints or the Haystack SDK. Shareable pipeline prototypes are great for demos, but production channels (Slack bots, web chat, etc.) need a bit of custom code. See prototype feature	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	Builds RAG agents as modular pipelines—retriever + reader, plus optional rerankers or multi-step logic. Multi-turn chat? Source attributions? Fine-grained retrieval tweaks? All possible with the right config. Pipeline overview Advanced users can layer in tool use and external API calls for richer agent behavior.	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	No drag-and-drop theming here—you’ll craft your own front end if you need branded UI. That also means full freedom to shape the visuals and conversational tone any way you like. Custom components	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	Model-agnostic: plug in GPT-4, Llama 2, Claude, Cohere, and more—whatever works for you. Switch models or embeddings through the “Connections” UI with just a few clicks. View supported models	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)	Comprehensive REST API plus the open-source Haystack SDK for building, running, and querying pipelines. Deepset Studio’s visual editor lets you drag-and-drop components, then export YAML for version control. Studio overview	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	Embed deeply into enterprise stacks—custom connectors, bespoke endpoints, the works. Schedule ETL jobs and route data conditionally right from the pipeline config. Deployment API	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	Tune for max accuracy with multi-step retrieval, hybrid search, and custom rerankers. Mix and match components to hit your latency targets—even at large scale. Benchmark insights	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)	Build anything: multi-hop retrieval, custom logic, bespoke prompts—your pipeline, your rules. Create multiple datastores, add role-based filters, or pipe in external APIs as extra tools. Component templates	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	Start free in Deepset Studio, then move to usage-based Enterprise plans as you scale. Deploy in cloud, hybrid, or on-prem setups to handle huge corpora and heavy traffic. Pricing overview	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	SOC 2 Type II, ISO 27001, GDPR, HIPAA—you’re covered for enterprise compliance. Choose cloud, VPC, or on-prem to keep data exactly where you need it. Security compliance	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	Deepset Studio dashboard shows latency, error rates, resource use—everything you’d expect. Detailed logs integrate with Prometheus, Splunk, and more for deep observability. Monitoring features	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	Lean on the Haystack open-source community (Discord, GitHub) or paid enterprise support. Community insights Wide ecosystem of vector DBs, model providers, and ML tools means plenty of plug-ins and extensions.	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	Perfect for teams that need heavily customized, domain-specific RAG solutions. Full control and future portability—but expect a steeper learning curve and more dev effort. More details	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	Deepset Studio offers low-code drag-and-drop, yet it’s still aimed at developers and ML engineers. Non-tech users may need help, and production UIs will be custom-built.	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 Deepset vs Vertex AI helpful.

If your team enjoys building from components and wants total control, Deepset is a strong choice. Otherwise, a simpler, managed platform might save time.

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Priyansh Khodiyar

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