I would rate Vespa a six, as it is a powerful tool with great potential in terms of search engine capabilities, but the steep learning curve and initial setup costs are significant downsides. I chose six because of the steep learning curve and the substantial initial costs involved with setting up Vespa. If it were feasible for people with limited budgets, even as low as fifty dollars a month, it would be more appealing. While conducting A/B testing, Vespa seemed to be performing slightly better than Elasticsearch, especially in search relevancy within live production systems, and its performance was decent. Comparing raw Elasticsearch text-based search against Vespa's vector-based and text-based search, we were already recommending Vespa to several peer companies. During A/B testing, looking at conversion rates, search-to-basket ratios, and add-to-basket ratios showed improvement until we shut it down. It took several iterations to get the results, particularly after switching to Embedding Gemma, emphasizing that the quality of embedding used heavily influenced the outcome. Nothing else comes to mind regarding improvements needed for Vespa. I would not suggest Vespa unless you are an enterprise due to the steep learning curve and significant infrastructure costs involved. My overall rating for Vespa is six out of ten.
Integration Related To Ai at RedBlink Technologies
MSP
Top 10
Jun 6, 2026
For anyone who wants to use a vector store, they should do research on their end, and if nothing comes up after discussion and research, I recommend using Vespa because they have good reliability. The main thing is the speed. The retrieval speed is very good. I recommend Vespa for systems to get integrated with. Vespa is very good and it improves our product, and we got more clients. We got very good results and very good relevance. This mainly depends on how you can design the Vespa document schemas. The document schema design determines how your relevance will come and how your retrieval will be done. The feedback for how Vespa responds is good and also fast. We are using Amazon Web Services (AWS) and it is easy because of their well-documented documentation. I give Vespa a rating of nine out of ten.
Up to now, I am still in the building phase. I have not gone commercial with my product, and so I cannot give a relevant answer about that. I am still trying out Vespa to see if it actually meets my business need. I would tell others that the product is actually good if they have some resources on their side because it is resource-intensive. It actually requires someone who knows what they are doing to reap most of the benefits out of Vespa because you do not have to implement most of the features in the code layer; you can just do it at the database layer. I would rate this product an 8 out of 10.
Vespa is a versatile product that enhances search functionality and improves the performance of large-scale applications. Users have reported using Vespa for content recommendation, personalization, and real-time analytics.
It handles high volumes of data and delivers fast and accurate search results. Vespa is also valuable for building intelligent applications, powering e-commerce platforms, and enabling efficient data retrieval and processing.
With exceptional performance,...
I would rate Vespa a six, as it is a powerful tool with great potential in terms of search engine capabilities, but the steep learning curve and initial setup costs are significant downsides. I chose six because of the steep learning curve and the substantial initial costs involved with setting up Vespa. If it were feasible for people with limited budgets, even as low as fifty dollars a month, it would be more appealing. While conducting A/B testing, Vespa seemed to be performing slightly better than Elasticsearch, especially in search relevancy within live production systems, and its performance was decent. Comparing raw Elasticsearch text-based search against Vespa's vector-based and text-based search, we were already recommending Vespa to several peer companies. During A/B testing, looking at conversion rates, search-to-basket ratios, and add-to-basket ratios showed improvement until we shut it down. It took several iterations to get the results, particularly after switching to Embedding Gemma, emphasizing that the quality of embedding used heavily influenced the outcome. Nothing else comes to mind regarding improvements needed for Vespa. I would not suggest Vespa unless you are an enterprise due to the steep learning curve and significant infrastructure costs involved. My overall rating for Vespa is six out of ten.
For anyone who wants to use a vector store, they should do research on their end, and if nothing comes up after discussion and research, I recommend using Vespa because they have good reliability. The main thing is the speed. The retrieval speed is very good. I recommend Vespa for systems to get integrated with. Vespa is very good and it improves our product, and we got more clients. We got very good results and very good relevance. This mainly depends on how you can design the Vespa document schemas. The document schema design determines how your relevance will come and how your retrieval will be done. The feedback for how Vespa responds is good and also fast. We are using Amazon Web Services (AWS) and it is easy because of their well-documented documentation. I give Vespa a rating of nine out of ten.
Up to now, I am still in the building phase. I have not gone commercial with my product, and so I cannot give a relevant answer about that. I am still trying out Vespa to see if it actually meets my business need. I would tell others that the product is actually good if they have some resources on their side because it is resource-intensive. It actually requires someone who knows what they are doing to reap most of the benefits out of Vespa because you do not have to implement most of the features in the code layer; you can just do it at the database layer. I would rate this product an 8 out of 10.