EVA DB VS Vespa.ai

Compare EVA DB VS Vespa.ai and see what are their differences

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EVA DB

EVA AI-Relational Database System | SQL meets Deep Learning

Vespa.ai

Store, search, rank and organize big data

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2023-04-17

EVA is an open-source AI-relational database with first-class support for deep learning models. It aims to support AI-powered database applications that operate on both structured (tables) and unstructured data (videos, text, podcasts, PDFs, etc.) with deep learning models.

Landing page //
2023-05-13

EVA DB

Website: evadb.readthedocs.io
$ Details: -
Release Date: 2023 March

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Vespa.ai

Website: vespa.ai
$ Details
Release Date: -

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EVA DB features and specs

No features have been listed yet.

Vespa.ai features and specs

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.

Possible disadvantages of Vespa.ai

Complexity
The platform might have a steep learning curve for beginners due to its advanced features and wide range of capabilities, which can increase the onboarding time.
Resource Intensive
Operating and maintaining the system can be resource-intensive, requiring significant computational resources, which might not be viable for small businesses.
Limited Community Support
Although open-source, the community around Vespa.ai is not as large as some other platforms, potentially leading to slower times in community-driven support and updates.
Niche Use Cases
It is specifically tailored for applications that need large-scale data processing and fast search capabilities, which might be more than necessary for simpler projects.
Complex Configuration
Configuring Vespa.ai can be complex and time-consuming, requiring in-depth understanding and expertise, which can delay implementation.

Category Popularity

0-100% (relative to EVA DB and Vespa.ai)

EVA DB

Vespa.ai

Databases

13 13%

Databases

87% 87

Custom Search Engine

6 6%

Custom Search Engine

94% 94

Search Engine

11 11%

Search Engine

89% 89

AI Platform

100 100%

AI Platform

0% 0

User comments

Share your experience with using EVA DB and Vespa.ai. For example, how are they different and which one is better?

Social recommendations and mentions

Based on our record, Vespa.ai seems to be a lot more popular than EVA DB. While we know about 20 links to Vespa.ai, we've tracked only 1 mention of EVA DB. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

EVA DB mentions (1)

Using EvaDB to build AI-enhanced apps
EvaDB plugs AI into traditional SQL databases, so as a first step, we’ll need to install a database. For this article, we’ll use SQLite because it's fast enough for our tests and does not require a proper database server running somewhere. You may choose a different database, if you prefer. - Source: dev.to / over 1 year ago

Vespa.ai mentions (20)

Why You Shouldn’t Invest In Vector Databases?
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
Code Search Is Hard
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
Simple Precision Time Protocol at Meta
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
Are we at peak vector database?
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
Show HN: RAGatouille, a simple lib to use&train top retrieval models in RAG apps
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