GigaOm Radar for Vector Databases

The latest GigaOm Radar for Vector Databases evaluates 17 leading platforms that power modern AI retrieval systems, including semantic search, retrieval-augmented generation (RAG), and multimodal applications.

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↗ Vespa Vector Database ↗ AI Search Platform

Overview

The latest GigaOm Radar for Vector Databases evaluates leading platforms powering modern AI retrieval systems, including semantic search, retrieval-augmented generation (RAG), multimodal search, and recommendation applications.

Vespa was positioned as a Leader and Outperformer in the Innovation / Platform Play quadrant.

Read the full report to explore the market landscape, evaluation criteria, and detailed vendor analysis.

In this Report

  • Evaluation of 17 leading vector database platforms
  • Analysis of architecture, scalability, and operational capabilities
  • Assessment of support for RAG, semantic search, and multimodal AI applications
  • Comparative analysis of vendor innovation, capabilities, and platform maturity
  • Guidance for architects and technical decision-makers evaluating AI retrieval infrastructure
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Key Themes

The report identifies key trends that are reshaping the vector database and AI retrieval landscape. These provide useful context for organizations evaluating how retrieval systems are evolving beyond traditional vector search.

  • How Leading Vector Databases Compare

    Architectural approaches, strengths, and trade-offs across leading vector database platforms.

  • What Matters Beyond Vector Search

    How ranking, filtering, hybrid retrieval, and operational scalability increasingly influence platform selection.

  • AI Retrieval Requirements Are Evolving

    How organizations are supporting RAG, recommendation, multimodal retrieval, and agentic AI workloads.

Read the Full Report

Read the complete GigaOm Radar for Vector Databases, including vendor assessments, market analysis, and evaluation criteria.

Read the report

Other Resources

Building Scalable RAG for Market Intelligence & Data Providers

Learn how Vespa delivers accurate, high-performance retrieval for GenAI agents at web scale.

The RAG Blueprint

Accelerate your path to production with a best-practice template that prioritizes retrieval quality, inference speed, and operational scale.

Delivering RAG for Perplexity

With Vespa RAG, Perplexity delivers accurate, near-real-time responses to more than 15 million monthly users and handles more than 100 million queries each week.