Vespa Cloud Subscription
Vespa.aiReviews from AWS customer
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Powerful backend for vector and hybrid search with many bells and whistles.
What do you like best about the product?
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 (at our pricing with Google), 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.
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.
What do you dislike about the product?
Vespa, in a scalable deployment, presents a fairly complex architecture with a lot of tuning knobs and bells and whistles. It took several months to get familiar with them. The Vespa consultant was very instrumental in this. Feeding Vespa from BigQuery was harder than expected.
Native extensions can only be written in Java which, without a native Java toolchain at our company, proved too challenging to pursue. The documentation is vast but could be better organized and have more contextual examples in places.
Native extensions can only be written in Java which, without a native Java toolchain at our company, proved too challenging to pursue. The documentation is vast but could be better organized and have more contextual examples in places.
What problems is the product solving and how is that benefiting you?
We used the Vespa search backend for hybrid search, consisting of nearest neighbor search of indexed embeddings vectors and attribute filters. This powered a natural-language image search product for our internal users.
My go-to-tool for my research on my e-commerce data
What do you like best about the product?
I like the open-source and free 300 dollar cloud credits for hosting the live applications.
What do you dislike about the product?
I feel there should be more documentation work is in pending and needed as I am still exploring the AI and vector database part.
Anyway I am happy to contribute for open source as a contributor.
Anyway I am happy to contribute for open source as a contributor.
What problems is the product solving and how is that benefiting you?
I worked for my e-commerce client to highlight the product which are giving more sales by ranking and recommendations for efficiency in stock.
Connect data to AI capabilities
What do you like best about the product?
I can create recommendation applications and deploy real-time machine learning inference using this stack. Such a level of functionality is what we need for our large scale search applications.
What do you dislike about the product?
Vespa initialization and subsequent functioning, in fact, require a significant level of system configuration. It may be a little obscure sometimes and for troubleshooting issues one has to really appreciate the underlying environment.
What problems is the product solving and how is that benefiting you?
Vespa solves the problem of managing and processing large amounts of data and its integration with Artificial Intelligence for Web applications. It enables me to build outstanding search capabilities and I use real-time data processing.
Managing vectors and lexical search
What do you like best about the product?
In the case of my recommendation engine, Vespa replaces other systems, thus minimizing issues related to architecture and deployment. Enabling me enhance the search by including the AI models in the process for more unique outcomes.
What do you dislike about the product?
Using ready-made machine learning models in Vespa may present some difficulty to developers. This particular aspect seems to be quite under-documented and the options provided look slightly unnatural.
What problems is the product solving and how is that benefiting you?
Vespa has been useful in composing my huge recommender system. The features of the unified search platform and real time AI inference help me provide customers with powerful relevant information.
Best Gen AI software to build your own infrastructure
What do you like best about the product?
The most helpful thing is the open source big data engine, heps to process and serve large scale data in real time with very low latency time.Its content recommendations are very useful for the modern day real-time analysis. Also, it is more flexible and scalable with advanced query techniques which makes it more easy to use.
What do you dislike about the product?
Integrating vespa with existing systems and workflows can be challenging, particulary if systems were based on different technologies. Documentation and customer support for an open source is not at the top notch when compared to the real time products. since it is highly specialised it may overkill for simpler applications w or less demanding requirements.
What problems is the product solving and how is that benefiting you?
Vespa helps in solving real time updates by using as a search engine which gives lot of recommendations based on our search results. it has the scalability and flexibility to process large volume of data in real time analyses and in turn produces intelligent responses based on the latest data.
Vepsa decreased costs, latency, and management for billions of searches per month
What do you like best about the product?
For our use case in advertising, Vespa leaves Apache Lucene-based products in the dust:
- High indexing throughput while searching
- Very, very technical team
- Best of the best technical support and guidance
- Multiple times, discussions were had and the next day the idea was implemented
- High indexing throughput while searching
- Very, very technical team
- Best of the best technical support and guidance
- Multiple times, discussions were had and the next day the idea was implemented
What do you dislike about the product?
- Search is still costly
- Improving ANN capabilities with ideas like DiskANN
- Simplify schema configuration and testing
- Lean in on more cloud native technologies
- Improving ANN capabilities with ideas like DiskANN
- Simplify schema configuration and testing
- Lean in on more cloud native technologies
What problems is the product solving and how is that benefiting you?
We do web-scale advertising. This means we process billions of queries a month concurrently with hundreds of million of feed requests. Vespa Cloud and their team provided us great technical guidance, saving us hundreds of thousands of dollars by optimizing and implementing fixes for our deployment. Although the road to utilizing Vespa took a long, hard journey, we are in a much better place then our previous solution with a Lucene-based product.
We moved our inhouse recommendations system to Vespa
What do you like best about the product?
Vespa provides a comprehensive set of features you would look for in a search engine, particularly in more ranking capabilites (e.g. leveraging ML models) and performance than what Elasticsearch offers out of the box. They're also constantly making advancements in new capabilities that they offer a nice hybrid between vector databases and a conventional search engine. Particularly for our business problem at OkCupid of recommending potential matches to millions of other users based on a myriad of factors and ranking algorithms, Vespa was a great fit to not only meet those use cases, but improve our team's development and iteration workflows in our recs system.
The Vespa team is also very active on Slack: https://vespatalk.slack.com/ssb/redirect and genuinely collaborative. In my case, we worked together with an engineer from their team who helped raise improvement changes into the engine to help us meet our use cases.
The Vespa team is also very active on Slack: https://vespatalk.slack.com/ssb/redirect and genuinely collaborative. In my case, we worked together with an engineer from their team who helped raise improvement changes into the engine to help us meet our use cases.
What do you dislike about the product?
One of the challenges in the past was around documentation and general community knowledge and expertise. Their documentation has since gone through a substantial revamp
What problems is the product solving and how is that benefiting you?
Vespa provides capabilities around a vector database as well as typical search engine capabilities so that we can consider other filters than just only constraining on similar vectors, etc. Additionally Vespa provides a strong set of ranking capabilities out of the box via ONNX, Tensorflow, LightGBM, etc. models
The best search infrastructure
What do you like best about the product?
Powerful Search Capabilities: Vespa.ai's search engine delivers lightning-fast and highly relevant results, even for complex queries over vast datasets. Their advanced linguistics capabilities ensure accurate understanding of query intent.
Scalable Architecture: I never have to worry about scaling with the Vespa cloud offering
Rich Filtering and Ranking: Vespa provides extensive capabilities for filtering, ranking, and blending results based on multiple criteria and machine learning models. We leverage their HNSW and BM25 rankings
Machine Learning Integration: Their tight integration with advanced machine learning frameworks like TensorFlow and PyTorch allows easy deployment of custom ML models for ranking, recommendations, and other use cases.
Top Tier Customer Support: The Vespa team has been exceedingly responsive to my questions regarding how to implement certain features.
Scalable Architecture: I never have to worry about scaling with the Vespa cloud offering
Rich Filtering and Ranking: Vespa provides extensive capabilities for filtering, ranking, and blending results based on multiple criteria and machine learning models. We leverage their HNSW and BM25 rankings
Machine Learning Integration: Their tight integration with advanced machine learning frameworks like TensorFlow and PyTorch allows easy deployment of custom ML models for ranking, recommendations, and other use cases.
Top Tier Customer Support: The Vespa team has been exceedingly responsive to my questions regarding how to implement certain features.
What do you dislike about the product?
There can be a steep learning curve when onboarding to the product, though it is well worth the investment of time
What problems is the product solving and how is that benefiting you?
Finding relevant information for my end users
Most complete open source vector/hybrid/text search engine
What do you like best about the product?
Proven scalability with planet-scale deployments. Used internally at Yahoo.
Self-hosted with docker and Kubernetes, or cloud hosted with autoscaling and automated updates.
Deployment from configuration, with API or CLI.
Vector search with self-hosted and remote embedding models.
Hybrid search.
Very powerful ranking language.
Multi-stage: retrieval, ranking, reranking.
Great support on GIthub.
Self-hosted with docker and Kubernetes, or cloud hosted with autoscaling and automated updates.
Deployment from configuration, with API or CLI.
Vector search with self-hosted and remote embedding models.
Hybrid search.
Very powerful ranking language.
Multi-stage: retrieval, ranking, reranking.
Great support on GIthub.
What do you dislike about the product?
The internal architecture is flexible but complex to master.
Documentation used to be confusing, but is getting better.
Documentation used to be confusing, but is getting better.
What problems is the product solving and how is that benefiting you?
Solving the most difficult part of any search engine: ranking.
Vespa.ai ranking is flexible and scalable (big data).
Vespa.ai ranking is flexible and scalable (big data).
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