Lesson 18 / 28

Comparing Engines: A Practical Checklist

Ask the same questions of every candidate.

Questions beat feature tables

Feature lists on vendor sites are similar and change monthly, so compare with questions about your workload. Scale: how many vectors and what dimension now and in two years; does it fit in memory; what does it do beyond RAM (disk indexes, quantisation)? Filtering: are filters evaluated during search or after; are payload indexes supported; how does it behave with a 1% filter? Search types: dense only, or sparse/keyword and hybrid with fusion; multi-vector; reranking hooks? Updates: how quickly does a write become searchable; how are deletes and re-indexing handled? Consistency and durability: transactions, write-ahead log, snapshots, replication? Operations: deployment (Kubernetes, managed), backups, monitoring, upgrades. Security: authentication, role-based access, encryption, tenant isolation, audit. Ecosystem and cost: client libraries, framework integrations, licence, support, total price. Score each candidate against your answers.

A scoring sheet

Fill it in with measured results from your own tests, not from marketing pages.

Criterion (weight)            Candidate A     Candidate B     Candidate C   how measured
recall@10 at p95 < 50 ms (5)   __/5            __/5            __/5          your golden set + load test
filter at 1% selectivity (4)   __/5            __/5            __/5          returns 10 rows? latency?
write-to-searchable delay (3)  __/5            __/5            __/5          insert, poll until found
ops effort (3)                 __/5            __/5            __/5          deploy, backup, upgrade drill
security features (4)          __/5            __/5            __/5          RBAC, encryption, tenant tests
cost at 2-year scale (4)       __/5            __/5            __/5          price sheet + memory estimate

Quick check: Why compare engines with questions about your workload rather than feature tables?

  • Feature lists look alike and change often; behaviour on your data decides
  • Features never matter
  • Vendors forbid comparison
  • Tables are always wrong
Answer

Feature lists look alike and change often; behaviour on your data decides — Test with your vectors, filters and load before choosing.