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Vespa.ai VS Google Custom Search Engine

Compare Vespa.ai VS Google Custom Search Engine and see what are their differences

Vespa.ai logo Vespa.ai

Store, search, rank and organize big data

Google Custom Search Engine logo Google Custom Search Engine

Google Custom Search is a platform provided by Google that allows web developers to feature specialized information in web searches, refine and categorize queries and create customized search engines, based on Google Search.
  • Vespa.ai Landing page
    Landing page //
    2023-05-13
  • Google Custom Search Engine Landing page
    Landing page //
    2022-01-31

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.

Google Custom Search Engine features and specs

  • Easy Integration
    Google Custom Search Engine (CSE) allows for seamless integration into your website with just a few lines of code, providing a hassle-free way to add powerful search capabilities.
  • Customizable Appearance
    The appearance of the search engine can be customized to match the aesthetics of your website, ensuring a consistent user experience.
  • Google Search Algorithms
    CSE leverages Google's advanced search algorithms, which means it provides highly relevant search results and reliable performance.
  • Filtering and Restrictions
    You can specify certain sites or pages to be included or excluded from the search results, allowing for tailored control over search parameters.
  • Sponsored Links Integration
    By default, the search results can include relevant ads, providing potential revenue opportunities without additional work.
  • Multiple Sites Search
    CSE allows you to create search engines that span multiple websites or subsets of sites, enhancing the search scope as needed.

Possible disadvantages of Google Custom Search Engine

  • Advertising
    The free version of CSE displays ads alongside the search results, which could be distracting or undesirable for some users and website owners.
  • Limited Customization for Free Version
    The level of search result customization is limited unless you opt for the paid version, which may incur additional costs.
  • Dependency on Google Infrastructure
    Using CSE ties your site's search functionality to Google's infrastructure, which could be a concern for those seeking more independent or diversified solutions.
  • Privacy Concerns
    Using Google services for search means that information about users' search behavior can be collected by Google, potentially raising privacy issues.
  • Data Limitations
    The specificity and depth of data accessibility are limited compared to integrating a custom-built search engine with full database access.
  • Learning Curve
    Configuring and optimizing CSE, especially for more complex requirements, may require some learning and adjustment time from the developer's side.

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Google Custom Search Engine videos

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Category Popularity

0-100% (relative to Vespa.ai and Google Custom Search Engine)
Custom Search Engine
61 61%
39% 39
Search Engine
89 89%
11% 11
Custom Search
0 0%
100% 100
Databases
100 100%
0% 0

User comments

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Social recommendations and mentions

Google Custom Search Engine might be a bit more popular than Vespa.ai. We know about 22 links to it since March 2021 and only 20 links to Vespa.ai. 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.

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 / 11 days 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 / about 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
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Google Custom Search Engine mentions (22)

  • Website Search Hurts My Feelings
    There are actually plenty of non-ES products that are way easier to integrate and tune (and get better results with less effort). - Typesense (https://github.com/typesense/typesense). - Source: Hacker News / over 1 year ago
  • How To De-index Your Docs From Google (And Then Fix It)
    One other important detail — there is an in-site search for the C7 docs. Notably, it is built on a programmable Google search engine. Basically, that means that the search functionality on the site is powered by Google. A search query entered into the C7 docs search box gives basically the same results as entering the query on google.com and filtering by site:docs.camunda.org. Again, "foreshadowing....". - Source: dev.to / over 1 year ago
  • Swirl: An open-source search engine with LLMs and ChatGPT to provide all the answers you need 🌌
    Using the Galaxy UI, knowledge workers can systematically review the best results from all configured services including Apache Solr, ChatGPT, Elastic, OpenSearch, PostgreSQL, Google BigQuery, plus generic HTTP/GET/POST with configurations for premium services like Google's Programmable Search Engine, Miro and Northern Light Research. - Source: dev.to / over 1 year ago
  • Mullvad Leta
    We used this for a project: https://programmablesearchengine.google.com/about/ before we moved to Elastic Search. It has a REST API (and can be used to query the internet also). - Source: Hacker News / almost 2 years ago
  • Tutorial: Create an AI agent that reads Wikipedia for you
    First, you need to create a programmable search engine for Wikipedia. - Source: dev.to / about 2 years ago
View more

What are some alternatives?

When comparing Vespa.ai and Google Custom Search Engine, you can also consider the following products

Meilisearch - Ultra relevant, instant, and typo-tolerant full-text search API

Algolia - Algolia's Search API makes it easy to deliver a great search experience in your apps & websites. Algolia Search provides hosted full-text, numerical, faceted and geolocalized search.

Typesense - Typo tolerant, delightfully simple, open source search 🔍

ElasticSearch - Elasticsearch is an open source, distributed, RESTful search engine.

Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.

Swiftype - The simplest way to add search to your website or application. Sign up for free.