Vespa Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

- Search is still costly

- Improving ANN capabilities with ideas like DiskANN

- Simplify schema configuration and testing

- Lean in on more cloud native technologies Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

There can be a steep learning curve when onboarding to the product, though it is well worth the investment of time Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The internal architecture is flexible but complex to master.

Documentation used to be confusing, but is getting better. Review collected by and hosted on G2.com.

I like the open-source and free 300 dollar cloud credits for hosting the live applications. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.