Vespa.ai Surpasses Elasticsearch in Latest Performance Benchmark

Vespa.ai Achieves Notable Performance Improvements

Vespa.ai has made significant strides in the realm of AI applications, recently releasing benchmark results that showcase outstanding performance when compared to Elasticsearch. This comparison is particularly crucial for developers aiming to deploy large-scale, real-time applications that require efficient data handling and response times. The study evaluates functionality across various query strategies used in e-commerce search applications, emphasizing Twitching operational costs and hardware capabilities.

Benchmark Study Overview

The benchmark involved comprehensive testing using a dataset comprising 1 million products, where different operational queries were executed to assess the efficiency of Vespa and Elasticsearch. Notably, the tests focused on write operations such as document ingestion and updates, alongside multiple querying strategies, including lexical matching and vector similarity. The results revealed a clear advantage for Vespa.ai in terms of speed and performance.

Insights from Vinted.com

A key experience during the benchmark was shared by Vinted.com, a second-hand marketplace that faced challenges with rising operational expenses and increasing hardware demands while using Elasticsearch. In their search for a more effective solution that integrated both vector and traditional search capabilities, Vinted migrated to Vespa in 2023 after conducting their independent evaluation. This migration highlighted the practical benefits and adaptability of Vespa's platform.

Key Findings of the Vespa Benchmark

The recent benchmark analysis led to several important discoveries regarding the performance and scalability of Vespa.ai:

• Performance Across Query Types
  • Hybrid Queries: Vespa achieved a remarkable 8.5 times higher throughput per CPU core compared to Elasticsearch.
  • Vector Searches: Vespa exhibited up to 12.9 times higher throughput per CPU core.
  • Lexical Searches: Additionally, Vespa showed 6.5 times better throughput per CPU core.
• Updates Efficiency
  • Steady-State Efficiency: For in-place updates, Vespa demonstrated 4 times greater efficiency, effectively handling queries and updates post-initial bootstrapping.
  • Bootstrap Phase: While Elasticsearch performed well during initial document ingestion, Vespa increasingly excelled in long-term operations.
• Infrastructure Cost Savings
  • Thanks to greater query throughput and enhanced processing efficiency, Vespa can reduce infrastructure costs by up to five times.

Leadership Insights

Jon Bratseth, CEO and Founder of Vespa.ai, stated, "As businesses increasingly demand rapid search results alongside streamlined continuous updates, it becomes crucial to select a solution that not only performs at scale but also remains economically viable. Our benchmark clearly indicates that Vespa excels not just in delivering faster query responses, but also in efficient resource utilization, leading to tangible cost savings for infrastructure."

Understanding the Benchmark Methodology

In the study, all query configurations were designed to ensure equivalent results, promoting a fair evaluation between the two platforms. The parameters measured included dataset sizes and system versions, such as Vespa 8.427.7 and Elasticsearch 8.15.2, allowing for full reproducibility of the results.

About Vespa.ai

Vespa.ai is revolutionizing how real-time AI applications are developed and deployed. With its distributed architecture, Vespa manages data, inference, and application logic for projects demanding high concurrent query rates. Its offerings encompass essential components like a vector database, hybrid search, and support for natural language processing, machine learning, and cutting-edge language models, making it a versatile choice for today’s AI-driven demands. Users can access Vespa through managed services or as open-source solutions.

Frequently Asked Questions

What is the main focus of the Vespa.ai benchmark study?

The benchmark study focuses on comparing the performance, scalability, and efficiency of Vespa.ai against Elasticsearch using an e-commerce search application dataset.

What were the key findings from the benchmark?

The benchmark revealed that Vespa outperformed Elasticsearch with significant improvements in query throughput, updates efficiency, and lower infrastructure costs.

How did Vinted.com benefit from switching to Vespa?

Vinted.com benefited by reducing operational costs and meeting their performance needs more effectively after migrating from Elasticsearch to Vespa.

What are the core functionalities of Vespa.ai?

Core functionalities include support for hybrid search, vector databases, machine learning, and natural language processing, catering to AI applications' diverse needs.

How can businesses implement Vespa for their applications?

Businesses can implement Vespa.ai services through managed offerings or leverage the open-source version for custom application development.

About The Author

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