Comparison
How Seek.js fits next to static search, vector databases, and AI chat SaaS
Comparison
This page summarizes the competitive framing from the Seek.js README—useful for deciding whether the architecture matches your constraints.
At a glance
| Category | Examples | Search model | Architecture | Typical cost |
|---|---|---|---|---|
| Static search | Pagefind, Stork | Lexical | Local-first | $0 (OSS) |
| Vector databases | Pinecone, Upstash | Vector | Centralized DB | Often hundreds/month at production scale |
| AI chat SaaS | Mendable, Kapa.ai | RAG chat | Centralized API | Usage + storage fees |
| Hosted search | Algolia, Orama Cloud | Neural / hybrid | Centralized SaaS | Tiered monthly |
| Seek.js | — | Hybrid | Disaggregated (build → CDN → browser → edge) | $0 OSS; optional managed tier |
When Seek.js is a strong fit
- You already ship a static or Jamstack site and want “Ask AI” without provisioning a database cluster.
- You want hybrid retrieval (keyword + semantic) with local latency for search results.
- You are comfortable caching an index artifact in the browser and only calling edge LLMs for summaries.
Honest tradeoffs
- Index size: Large sites need quantization, Brotli, and sharding—see the roadmap.
- Generative quality: Edge models are smaller than frontier APIs; citation discipline matters.
- Maturity: Packages are still stabilizing; treat integrations as experimental until versioned releases land.
Return to Getting Started or dive into Architecture for the full pipeline.