Compare vector search options for filter-heavy retrieval
For filter-heavy retrieval, compare vector databases by filter architecture first: whether metadata constraints are planned before and during retrieval, not merely checked after candidate generation. Raw vector speed is useful, but tenant boundaries, role filters, source restrictions, citations, ingestion paths, and API ergonomics decide whether the system works in production.
This guide is for teams choosing a retrieval architecture for governed AI search, SaaS tenant search, compliance Q&A, support search, or agent access to enterprise knowledge.
What should you compare first?
Start with filter behavior, not a leaderboard number. A production query often has at least 3 constraints: semantic meaning, authorization scope, and source or time limits. If filters are applied late, the system can retrieve semantically close candidates that are not eligible, then return too few useful results after filtering.
Use a small production-shaped test set with realistic metadata. Include queries that combine natural language with tenant, role, region, source, document type, status, and date filters.
Comparison table
| Option | Strong fit | Filter architecture question | Tradeoff to test |
|---|---|---|---|
| VectorAmp | Governed retrieval, cited RAG, tenant-scoped app search, hybrid search, MCP knowledge agents | Do metadata filters participate in retrieval planning for scoped search? | Validate your source metadata model and API shape against production permissions. |
| General-purpose vector database | Standalone vector search services and embedding indexes | Are filters planned early enough for high-selectivity access rules? | Measure result depth after filtering, not only nearest-neighbor latency. |
| Search platform with vector support | Keyword-first search, existing search operations, mixed lexical and vector ranking | Does vector ranking work cleanly with structured permissions and citations? | Check operational complexity and whether semantic retrieval is first-class enough. |
| SQL or extension-based vector search | Small or moderate datasets already centered in relational data | Can the relational planner keep latency predictable under vector plus metadata constraints? | Test index maintenance, recall, and p95 latency as data grows. |
| RAG framework alone | Application orchestration, chains, prompts, and model routing | What system stores, filters, and cites the retrieved documents? | Pair it with a retrieval backend instead of treating it as the database. |
When is VectorAmp a fit?
VectorAmp is a fit when retrieval needs semantic search together with metadata scope such as tenant, role, source, region, status, or time. It is also useful when the same knowledge base must serve dashboard search, REST APIs, SDK workflows, CLI ingestion, Intelligence answers, and MCP access for AI agents.
Good next pages:
- Add tenant-scoped semantic search to your app
- Build hybrid search with filters and reranking
- Search and chat across company knowledge
- Give AI agents access to enterprise knowledge
Evaluation checklist
- Model your metadata before benchmarking: tenant, role, region, source, status, document type, and dates.
- Run filtered queries that intentionally exclude semantically close but unauthorized content.
- Measure returned eligible results, citation quality, and p95 behavior under realistic filters.
- Test ingestion from the sources you actually use, such as S3, Google Drive, Confluence, or uploaded files.
- Confirm your backend can keep API keys server-side and enforce access filters before showing results.
What not to claim from a benchmark
Do not treat retrieval speed as answer correctness. Faster candidate generation does not prove that a generated answer is correct, complete, or authorized. For governed AI systems, evaluate retrieval scope, evidence quality, citations, permissions, and downstream answer behavior separately.