Technical differences that affect your architecture
Solr supports vector search, hybrid scoring, and learned reranking. The differences lie in how ranking stages, model execution, updates, and data distribution are implemented. Use the arrows to explore each of the four key areas:
- Ranking and model execution
- Feature computation and integration
- Update cost and search visibility
- Data distribution and scaling
Ranking and model execution
Solr supports initial retrieval followed by reranking, including model execution through its learning-to-rank module. More complex model architectures may require additional integrations or custom components.
Vespa provides programmable first- and second-phase ranking on content nodes, followed by optional global-phase ranking over merged candidates. ONNX, XGBoost, and LightGBM support lets engineers place models in the ranking pipeline based on computational cost and candidate budget.
Feature computation and integration
Solr can combine lexical relevance, vector similarity, and business signals through queries, functions, and learning-to-rank features. Requirements beyond those mechanisms may introduce custom plugins or separate services.
Vespa’s ranking expressions and tensor operations let engineers define features, combine query and document signals, and execute models within the search application. This can reduce data movement and the number of components required for custom ranking.
Update cost and search visibility
Solr supports atomic field updates, but most require document reindexing. In-place updates avoid this for a restricted set of eligible fields. Newly indexed content becomes searchable when a searcher is opened, commonly through a soft commit.
Vespa supports partial updates without reindexing the entire document and makes changes searchable without periodic soft commits. This matters when embeddings, availability, prices, or other ranking signals change frequently.
Data distribution and scaling
SolrCloud provides shard splitting, replica placement, and replica movement. Teams manage shard topology and decide when to split shards or redistribute replicas as capacity requirements change.
Vespa automatically distributes and redistributes data as content nodes are added or removed. This reduces manual partition management, although capacity planning, resource sizing and workload tuning remain necessary.
