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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Execute searches that combine vector embeddings, keyword matching, and structured data filters in a single query. Vespa processes all three search modalities simultaneously and ranks results using unified scoring.
Serve machine learning models (ONNX, XGBoost, TensorFlow) directly within ranking pipelines to score and re-rank search results without external inference services. Models execute on indexed data during query time.
Process and index large batches of documents efficiently, supporting bulk updates, deletions, and insertions with optimized throughput.
Execute complex queries using Vespa's YQL (Vespa Query Language) to specify search logic, filtering, grouping, and result processing in a single declarative statement.
Build recommendation systems by combining collaborative filtering, content-based filtering, and ML models within Vespa's ranking pipeline to generate personalized recommendations.
Execute multi-phase ranking pipelines where initial phases use fast approximate ranking to reduce candidate set, and later phases apply expensive ML models to final candidates.
Filter search results using structured data conditions on fields like dates, numbers, categories, and enums. Combine multiple filter conditions with boolean logic.
Index new documents and updates to existing documents in real-time with immediate searchability. Supports both streaming updates and batch ingestion while maintaining index consistency.
+7 more capabilities
Store and automatically index high-dimensional vector embeddings in a managed, scalable database without manual infrastructure provisioning. The system handles index optimization and partitioning transparently.
Query stored vectors to find semantically similar items by computing distance metrics between query embeddings and indexed vectors. Returns ranked results based on relevance in sub-100ms latency.
Serve as the retrieval component in Retrieval-Augmented Generation pipelines, providing relevant context documents to language models for grounded responses.
Provide a generous free tier (1 pod, 100K vectors) enabling teams to build and test real applications before committing to paid plans.
Execute searches using both dense vector embeddings and sparse keyword-based vectors simultaneously, combining results to improve relevance by capturing both semantic and lexical similarity.
Filter vector search results based on metadata attributes (tags, categories, timestamps, custom fields) before or during similarity search, enabling faceted and conditional retrieval.
Insert, update, or replace multiple vectors and their metadata in a single batch operation, optimizing throughput for bulk data ingestion without individual API calls.
Both Vespa and Pinecone offer these capabilities:
Query stored vectors to find semantically similar items by computing distance metrics between query embeddings and indexed vectors. Returns ranked results based on relevance in sub-100ms latency.
Pinecone scores higher at 71/100 vs Vespa at 47/100.
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Partition vector data within a single index using namespaces, enabling logical separation of data (by user, tenant, or dataset) without creating separate indexes.
+4 more capabilities