• HOME
  • CATEGORIES

    • CATEGORIES

    • Browse All Categories
  • FOR VENDORS

    • FOR VENDORS

    • Log In to Vendor Portal
    • Get Started
  • REVIEWS

    • REVIEWS

    • Write a Review
    • Product Reviews
    • Vendor Directory
    • Product Comparisons
  • GARTNER PEER COMMUNITY™
  • GARTNER.COM
  • Community GuidelinesListing GuidelinesBrowse VendorsRules of EngagementFAQPrivacyTerms of Service
    ©2026 Gartner, Inc. and/or its affiliates.
    All rights reserved.
  • Categories

    • No categories available

      Browse All Categories

      Select a category to view markets

  • For Vendors

    • Log In to Vendor Portal 

    • Get Started 

  • Write a Review

Join / Sign In
  1. Home
  2. /
  3. Vespa
Logo of Vespa

Vespa

byVespa.ai
in
5.0

Overview

Product Information on Vespa

Updated 13th October 2025

What is Vespa?

Vespa is a software used for storing, searching, and processing data at scale, typically in applications that require fast retrieval and ranking of large volumes of structured and unstructured data. The software handles real-time data ingestion and indexing, full-text search, filtering, recommendation, and personalization. Vespa enables advanced ranking and relevance management using machine-learned models, making it suitable for scenarios involving search engines, recommendation systems, and large-scale data-driven applications. The software is designed to manage low-latency queries and support high throughput while supporting the deployment, scaling, and management of data stores and computation across clusters. Vespa addresses business needs around efficient information retrieval, personalization, and analytics for large datasets.

Vespa Pricing

Vespa software uses an open source pricing model, allowing users to access its core features without charge. Additional functionalities or enterprise support may be available under separate commercial agreements, with pricing details typically determined based on specific business needs or usage requirements.

Overall experience with Vespa

Staff ML Systems Engineer
<50M USD, Transportation
FAVORABLE

“Powerful backend for vector and hybrid search with many bells and whistles.”

5.0
Dec 18, 2024
This text serves as a placeholder and does not reflect the user’s review responses or opinions. This text serves as a placeholder and does not reflect the user’s review responses or opinions. This text serves as a placeholder and does not reflect the user’s review responses or opinions.
There are no reviews in this category.
CRITICAL

About Company

Company Details

Updated 6th August 2025
Year Founded
2023
Head office location
Norway
Number of employees
11 - 50
Annual Revenue
<50M USD
Website
https://vespa.ai

Do You Manage Peer Insights at Vespa.ai?

Access Vendor Portal to update and manage your profile.

Key Insights

A Snapshot of What Matters - Based on Validated User Reviews

Top Vespa Alternatives

Logo of Portkey
1. Portkey
4.6
(201 Ratings)
Logo of Elastic Search
2. Elastic Search
4.5
(144 Ratings)
Logo of Algolia
3. Algolia
4.4
(104 Ratings)
View All Alternatives

Peer Discussions

Vespa Reviews and Ratings

5.0

(12 Ratings)

Rating Distribution

5 Star
100%
4 Star
0%
3 Star
0%
2 Star
0%
1 Star
0%
Why ratings and reviews count differ?
  • Staff ML Systems Engineer
    <50M USD
    Transportation
    Review Source

    Powerful backend for vector and hybrid search with many bells and whistles.

    5.0
    Dec 18, 2024
    The Vespa team is extremely responsive and very pleasant to work with. We got immediate access to high-level people in the company and we had a regular weekly meeting with the rep who was extremely well-versed in Vespa and its deployment and typical customer concerns and was often able to debug things by just asking a few questions and most of the time his guesses were spot-on. He als checked-in on critical deployments at times over the weekend. We really felt well cared-for in the whole experience.
  • Staff ML Systems Engineer
    <50M USD
    Transportation
    Review Source

    Powerful backend for vector and hybrid search with many bells and whistles.

    5.0
    Dec 18, 2024
    The Vespa team is extremely responsive and very pleasant to work with. We got immediate access to high-level people in the company and we had a regular weekly meeting with the rep who was extremely well-versed in Vespa and its deployment and typical customer concerns and was often able to debug things by just asking a few questions and most of the time his guesses were spot-on. He als checked-in on critical deployments at times over the weekend. We really felt well cared-for in the whole experience.
  • Read All 12 Reviews

    Get unlimited access to verified peer reviews and insights

    Read unlimited Gartner-vetted product reviews
    View and share valuable product insights
    Download full product profiles
    Review products you use today

Recommended Gartner Insights

  • Market Guide for Generative AI Engineering
Powered by Google TranslateThis service may contain translations provided by Google. Google disclaims all warranties related to the translations, express or implied, including any warranties of accuracy, reliability, and any implied warranties of merchantability, fitness for a particular purpose and noninfringement. Gartner's use of this provider is for operational purposes and does not constitute an endorsement of its products or services.

Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences, and should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.

This site is protected by hCaptcha and its Privacy Policy and Terms of Use apply.


Software reviews and ratings for EMMS, BI, CRM, MDM, analytics, security and other platforms - Peer Insights by Gartner
Community GuidelinesListing GuidelinesBrowse VendorsRules of EngagementFAQsPrivacyTerms of Use

©2026 Gartner, Inc. and/or its affiliates.

All rights reserved.

Reviewer Insights for: Vespa

Vespa Likes & Dislikes

Like

We purchased the Enclave product which was really well-suited for us because it let us run the hosts in our own Google cloud account, and thus didn't require us to transfer any data out which was well-aligned with our security stance. It provided light-touch deployment and observability services that we lacked and helped us bootstrap quickly and with minimal investment. The Vespa search backend itself provided a good match to our requirements of near-real time hybrid search, combining nearest neighbor embedding search with attribute filters, in a distributed and highly scalable way. Our target installation comprised >12TB of memory across 24 hosts and held O(1B) vector embeddings.

Like

We purchased the Enclave product which was really well-suited for us because it let us run the hosts in our own Google cloud account, and thus didn't require us to transfer any data out which was well-aligned with our security stance. It provided light-touch deployment and observability services that we lacked and helped us bootstrap quickly and with minimal investment. The Vespa search backend itself provided a good match to our requirements of near-real time hybrid search, combining nearest neighbor embedding search with attribute filters, in a distributed and highly scalable way. Our target installation comprised >12TB of memory across 24 hosts and held O(1B) vector embeddings.

Like

We purchased the Enclave product which was really well-suited for us because it let us run the hosts in our own Google cloud account, and thus didn't require us to transfer any data out which was well-aligned with our security stance. It provided light-touch deployment and observability services that we lacked and helped us bootstrap quickly and with minimal investment. The Vespa search backend itself provided a good match to our requirements of near-real time hybrid search, combining nearest neighbor embedding search with attribute filters, in a distributed and highly scalable way. Our target installation comprised >12TB of memory across 24 hosts and held O(1B) vector embeddings.