Aeion Search vs Algolia & ElasticSearch
Discover why scaling E-Commerce brands and enterprise applications are abandoning exorbitant third-party API fees and fragile database syncing for a natively integrated, Semantic Vector Search OS.
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Decisive Architectural Advantages
The End of the Search Tax
SaaS giants like Algolia punish your growth. The more successful your website is, the more they charge you for every single keystroke your users type. Aeion OS believes search is a fundamental computing right. The Search module is a core platform feature. You pay $0 a month, whether you execute 1,000 searches or 10,000,000 searches.
Zero-Sync Architecture
Using ElasticSearch or Algolia means you have to maintain a separate copy of your data. If you change a product price in your database, you must write a script to push that update to Algolia over an API. This causes inevitable sync lag and broken search results. Aeion Search uses native PostgreSQL. The search index is the primary database. The millisecond a product is saved, it is globally searchable with perfect accuracy.
True Semantic Understanding
Traditional search engines fail when users don't type the exact keyword. Aeion leverages `pgvector` to store mathematical AI embeddings natively. When a user searches for "a jacket for snowy weather," the engine calculates the Cosine Similarity and returns your "Winter Parka" product, radically boosting E-Commerce conversion rates by searching by meaning, not just syntax.
Hybrid RRF Perfection
Pure vector search can sometimes be too "creative," missing obvious SKU searches. Aeion solves this with native Hybrid Search. The system queries both the Full-Text `ts_rank` engine and the Semantic Vector engine simultaneously, using Reciprocal Rank Fusion (RRF) math to blend the exactness of keywords with the contextual brilliance of AI into one flawless list of results.
Frequently Asked Questions
No. You do not need to learn Lucene query syntax or manage separate Java-based clusters. Aeion uses native PostgreSQL extensions, meaning your standard relational database handles all the heavy lifting effortlessly.
Yes. The Federated Search engine allows you to execute a single query that natively cross-references your CRM contacts, workspace pages, and commerce products simultaneously, delivering categorized, unified results to the Command Palette.
It tolerates typos. The engine breaks a query into short letter fragments (trigrams) for a fast, index-friendly first pass, then scores close matches by edit distance (Levenshtein) scaled to word length so short words aren't over-matched — "seach" still finds "search," "nke" still finds "nike." Exact matches always rank above close ones.
Analytics are built in and free. Every query is logged asynchronously off the request path (with retry and backoff, so telemetry never adds latency to a live search): the normalized text, the mode used, result count, timing, and which user ran it. That powers top-queries and — most usefully — no-results reports that show exactly where users searched and found nothing.
You bring your own key. Embeddings route through the platform AI gateway to whatever provider you've configured — text-embedding-3-small (1536 dims) is the balanced default, text-embedding-3-large (3072 dims) for higher quality. You pay the provider directly at their rate, with zero Aeion markup, rather than a bundled per-embedding fee.
No — there's no separate index to sync. Vectors and full-text tsvectors live in the same PostgreSQL rows as the data itself, so a single transactional `UPDATE` changes the record and its searchability together. The class of "sync drift" bugs that comes with an external index simply can't occur.