Semantic Search vs Keyword Search?
Direct answer
Keyword search (BM25 and friends) matches the literal terms a user typed; semantic search embeds queries and documents as vectors and matches by meaning, so "cheap flights" finds "affordable airfare." Neither wins outright: keyword search is still better for exact identifiers, names, and rare terms, while semantic search wins on natural-language questions and paraphrases. Nearly every production system I build ends up hybrid — both methods fused with reciprocal rank fusion, often with a reranker on top. A keyword baseline costs days on infrastructure you already have; adding a semantic layer typically brings full search projects into the $15K–$90K range depending on scale and quality targets.
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How the two actually differ in practice
Keyword search treats text as bags of terms. Algorithms like BM25 score documents by how often query words appear and how rare those words are across the corpus. It is fast, cheap, deterministic, and explainable — you can always answer "why did this result rank here?" Its weakness is vocabulary mismatch: if the user says "laptop won't turn on" and your doc says "computer fails to boot," pure keyword search finds nothing.
Semantic search runs every document and every query through an embedding model that maps text to a point in vector space, where similar meanings land close together. It handles paraphrase, synonyms, and loosely-worded questions gracefully. Its weaknesses mirror keyword search's strengths: it can miss exact matches on part numbers or names, its failures are hard to explain, and it needs an embedding pipeline and a vector index — real infrastructure that keyword search doesn't require.
Where keyword search still wins
Buyers are often surprised when I talk them out of a pure semantic build. Keyword search remains the better tool when queries are exact lookups: SKUs, error codes, invoice numbers, people's names, legal citations, function names in code. Embedding models can blur precisely the distinctions that matter in those cases — "ERR_4012" and "ERR_4021" embed almost identically but are completely different things.
Keyword systems also filter and facet naturally (category, date range, status), integrate with infrastructure you already run — Postgres full-text search or an existing Elasticsearch cluster — and add no per-query model cost. If your query logs show mostly short, precise, known-item lookups, a well-tuned BM25 setup with good analyzers and synonym lists may be 90% of what you need at 10% of the cost. Look at your actual queries before buying anything: the logs, not the vendor demo, should drive this decision.
Why hybrid is usually the right answer
Real query streams are mixed: some users type exact terms, others type questions, and many queries contain both ("refund policy order #58291"). That is why the systems I ship almost always run both retrievers in parallel — BM25 for lexical precision, vector search for semantic recall — and merge results with reciprocal rank fusion (RRF), a simple, robust technique that needs no tuning to work well. For higher-stakes search, a cross-encoder reranker on the top candidates adds a further quality step.
Hybrid also degrades gracefully. When the embedding model misses, keyword matches still surface; when vocabulary mismatch defeats BM25, vectors carry the load. In evaluations I've run for clients, hybrid consistently beats either method alone on mixed query sets — usually by a comfortable margin. The added complexity is modest since Postgres with pgvector, or any mainstream search engine, can serve both sides of the hybrid from one datastore at startup scale.
What it costs and how to evaluate before spending
A solid keyword baseline on Postgres full-text or an existing Elastic cluster is days of work, not weeks. Adding a semantic layer means an embedding pipeline (with re-embedding on content changes), a vector index, hybrid fusion, and — the part teams skip — evaluation. A focused implementation for one content type typically lands around $15K–$40K; systems with reranking, multilingual content, permission-aware results, or large scale run $50K–$90K.
Before committing, build a golden set: 50–200 real queries from your logs with the results a human judges correct, then measure recall and precision of your current search as a baseline. Any vendor quote that doesn't include an evaluation harness should worry you — without one, "the search feels better" is the only acceptance criterion, and that is how projects drift. The golden set also tells you honestly whether you need semantic search at all.
People also ask
Do I need a dedicated vector database for semantic search?
Usually not at startup scale. Postgres with the pgvector extension comfortably handles collections into the millions of vectors, keeps your search data next to your application data, and supports hybrid keyword-plus-vector queries in one system. Dedicated vector databases earn their keep at much larger scale or with strict latency requirements — adopt one when you hit those limits, not before.
How much better is semantic search than keyword search?
It depends entirely on your queries. Natural-language questions and paraphrase-heavy queries often see large relevance gains; exact lookups (IDs, names, codes) see little or none and can even regress. The honest way to know is to sample real queries from your logs, build a small graded test set, and measure both — which is the first step of any search engagement I take on.
Is semantic search the same thing as RAG?
No, but they're related. Semantic search is a retrieval technique: given a query, return the most relevant documents. RAG (retrieval-augmented generation) uses retrieval — usually hybrid semantic plus keyword — as one component, then feeds the retrieved passages to a language model that writes an answer. Good RAG depends on good retrieval, which is why search quality work usually comes first.