What Experts Say About Vespa
Analysts and users alike recognize Vespa’s capabilities in real-time AI search, recommendation, and personalization. Explore their perspectives on Vespa’s role in powering production-scale systems for generative AI.
Independent Analysts and users alike identify Vespa as a platform built for the demands of modern AI. Known for its low-latency performance, flexible data modeling across text, vectors, tensors, structured data, and consistently excellent support, Vespa powers production-grade search, recommendation, and generative AI (RAG). Explore these perspectives and full reports below.
“For organizations building modern search, recommendation, or RAG-enabled systems where real-time AI performance is paramount, Vespa warrants serious consideration and should be on the evaluation short list.”
Whit Walters, Field CTO,
GigaOm
“Vespa has developed solutions designed to deliver on the enormous potential generative AI has in orderto power retailers to greater engagement and, ultimately, more revenue.”
Mark Beccue, Principal Analyst,
Enterprise Stratgy Group.
“Vespa.ai was classified as an Outperformer because of its proficiency in storing, retrieving, and processing complex data structures at scale and in real time.”
Andrew Brust, Analyst,
GigaOm.
Vespa is a Leader and Outperformer in the GigaOm Radar for Vector Databases.
This research note explores the emergence of versatile AI databases that support multi-model applications. Practitioners, data/AI leaders, and business leaders should read this report to understand this new platform option for supporting modern AI/ML initiatives.
As organizations integrate corporate data with Natural Language Models (NLMs), Retrieval-Augmented Generation (RAG) is essential for enhancing AI accuracy and relevance, especially for complex queries and unstructured data. RAG allows businesses to unlock insights while maintaining control over data access, privacy, and compliance. When choosing RAG solutions, organizations should consider scalability, performance, integration ease, security features like encryption, and cost efficiency to ensure the system meets their data needs and budget. To help organizations navigate their choice in RAG adoption, BARC has prepared the research note: Why and How Retrieval-Augmented Generation Improves GenAI Outcomes.
Vespa.ai is the world’s first AI Search Platform, unifying vector, keyword, and structured retrieval with machine-learned ranking and real-time inference. Trusted by leaders like Perplexity, Spotify, and Yahoo, Vespa delivers the speed, scale, and accuracy required for deep research, agentic AI, and customer-facing generative applications.
By building on Vespa’s platform, Perplexity delivers accurate, near-real-time responses to more than 15 million monthly users and handles more than 100 million queries each week.
“RavenPack has trusted Vespa.ai open source for over five years–no other RAG platform performs at the scale we need to support our users. Following rapid business expansion, we transitioned to Vespa Cloud. This simplifies our infrastructure and gives us access to expert guidance from Vespa engineers on billion-scale vector deployment.”
