Scalability
Vespa.ai can handle large-scale data processing and real-time analytics, making it suitable for enterprises with vast data sets and high performance requirements.
Flexibility
Offers the ability to deploy applications on various infrastructures whether on-premises, in the cloud, or in hybrid environments, which enhances deployment flexibility.
Real-time Data Processing
Designed to facilitate real-time data ingestion and querying, which supports applications that require fast data retrieval and processing.
Open Source
Being open-source allows developers to customize and contribute to the platform, fostering community engagement and innovation.
Advanced Search Capabilities
Provides a strong search engine that supports natural language processing and complex query handling, which enhances user interactions and data retrieval.
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Check the traffic stats of Vespa.ai on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Vespa.ai on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Vespa.ai's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Vespa.ai on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Vespa.ai on Reddit. This can help you find out how popualr the product is and what people think about it.
In cases where a company possesses a strong technological foundation and faces a substantial workload demanding advanced vector search capabilities, its ideal solution lies in adopting a specialized vector database. Prominent options in this domain include Chroma (having raised $20 million), Zilliz (having raised $113 million), Pinecone (having raised $138 million), Qdrant (having raised $9.8 million), Weaviate... - Source: dev.to / about 1 month ago
If you're serious about scaling up, definitely consider Vespa (https://vespa.ai). At serious scale, Vespa will likely knock all the other options out of the park. - Source: Hacker News / about 1 year ago
Yahoo released their geographic data catalogue under open license and it still lives on as https://whosonfirst.org/ Afaik https://en.wikipedia.org/wiki/Apache_ZooKeeper started at Yahoo https://vespa.ai/ was Yahoo's search engine for news and other content product, now spinned off (https://techcrunch.com/2023/10/04/yahoo-spins-out-vespa-its-search-tech-into-an-independent-company/). - Source: Hacker News / over 1 year ago
I think https://vespa.ai/ has the right approach in this space by focusing on being hybrid - vectors alone aren't great for production use cases, it's the combining of vectors+text that lets you use ranking to get meaningful result. (I'm an investor so I'm biased; but it's also the reason why I invested). - Source: Hacker News / over 1 year ago
So what’s the catch? Why is this not everywhere? Because IR is not quite NLP — it hasn’t gone fully mainstream, and a lot of the IR frameworks are, quite frankly, a bit of a pain to work with in-production. Some solid efforts to bridge the gap like Vespa [1] are gathering steam, but it’s not quite there. [1] https://vespa.ai. - Source: Hacker News / over 1 year ago
When it comes to search I cannot disagree more. https://vespa.ai is a purpose built search engine. If you start bolting search onto your database, your relevance will be terrible, you'll be rewriting a lot of table stakes tools/features from scratch, and your technical debt will skyrocket. - Source: Hacker News / almost 2 years ago
Milvus (https://milvus.io) and Vespa (https://vespa.ai) are great choices if you're looking for hardened, scalable, and production-ready vector databases. We (Milvus) also have `milvus-lite` if you'd like something pip installable:. - Source: Hacker News / about 2 years ago
Imo the most advanced vector DB out there is Vespa https://vespa.ai/. Harder to set up than wrappers like Chroma, but very powerful. - Source: Hacker News / about 2 years ago
Vespa(4.3k ⭐) → A fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real time. - Source: dev.to / about 2 years ago
Surprised to see that no one mentioned https://vespa.ai/ the best alternative to vector DB imho. - Source: Hacker News / about 2 years ago
(disclaimer: former Yahoo employee) Yahoo at the time was clearly underestimated from a developer point of view. Everyone just saw them losing ground to Google in web search, but they had a really strong technology behind them that was open sourced. - YUI was a really good frontend framework based on components and events. It was so easy to create complex interfaces compared to others at the time. - Hadoop open... - Source: Hacker News / over 2 years ago
Shameless plug from someone not related to the project. Try https://vespa.ai , fully open-source, very mature hybrid search with dense and approximated vector search. A breeze to deploy and maintain compared to ES and Solr. If I could name a single secret ingredient for my startup, is Vespa. - Source: Hacker News / over 2 years ago
For those interested in search engines, recommend checking out Vespa.ai[1][2] - the engine behind several features at Yahoo. [1] https://vespa.ai/. - Source: Hacker News / almost 3 years ago
Yahoo released Vespa as open source: https://vespa.ai/ It has everything you need at a platform level to build a production recommendation system given that it’s the engine that powered a lot of yahoo product’s search and recommendation capabilities. I have been experimenting with it, the number of capabilities are immense. It’s really an untapped resource. Take a look at the features: https://vespa.ai/features... - Source: Hacker News / about 3 years ago
Good luck! For those who want an engine that already provides this functionality there's always https://vespa.ai. Source: over 3 years ago
For a similar ANN/vector search capabilities, https://vespa.ai/ is a great open-source solution. Elasticsearch may offer some form of ANN too but need to double check. - Source: Hacker News / over 3 years ago
In the search space there’s a large menu of new search engines like http://vespa.ai and many Rust based alternatives. Not particularly new, but the streaming world with dataflow and Flink is pretty cool. - Source: Hacker News / over 3 years ago
After looking at various alternatives, I'm thinking of trying out https://vespa.ai/ [0] [0] https://github.com/vespa-engine/vespa. - Source: Hacker News / almost 4 years ago
I ran into this the other day looking for search systems you may want to check it out. https://vespa.ai. Source: almost 4 years ago
What are the modern tech Stack behind Yahoo? Still have any FreeBSD running? Do Yahoo actually use Vespa [1] for their search engine? [1] https://vespa.ai. - Source: Hacker News / about 4 years ago
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