Semantic vs Keyword Search: When to Use Which

Keyword search, also called lexical or full-text search, remains the backbone of most search systems. It works by matching exact terms or variations in your query against an inverted index of documents. Systems like Elasticsearch and Apache Solr have perfected this approach over decades.

The core strength is speed and predictability. When a user searches for 'iPhone 15 Pro Max', keyword search excels at finding exact product matches. It handles Boolean operators (AND, OR, NOT), wildcards, and fuzzy matching for typos with millisecond response times.

• Blazing fast: 1-50ms response times for most queries
• Highly predictable: same query always returns same results
• Mature ecosystem: battle-tested tools and frameworks
• Low resource requirements: runs efficiently on modest hardware
• Excellent for exact matches: product codes, names, identifiers

The limitation becomes apparent with natural language queries. Search for 'affordable smartphones with good cameras' and keyword search struggles. It might miss 'budget phones with excellent photography' because the words don't match exactly, even though the intent is identical.

Semantic search uses machine learning models to understand the meaning behind queries, not just the words. Modern implementations rely on embeddings and vector databases to represent text as high-dimensional vectors that capture semantic relationships.

When you search for 'dog-friendly vacation spots', semantic search understands this relates to 'pet-accommodating hotels', 'canine-welcome destinations', and 'family trips with pets'—even if those exact words never appear in your documents. This is possible because the underlying transformer models learned these relationships from massive text corpora.

• Intent understanding: grasps what users actually want
• Synonym handling: connects related terms automatically
• Context awareness: considers surrounding words and phrases
• Multilingual capabilities: works across language boundaries
• Conceptual matching: finds relevant content even with different wording

The trade-off is complexity and cost. Vector search systems require specialized infrastructure, GPU acceleration for embedding generation, and significantly more storage. A simple text field becomes a 768-dimension vector requiring 3KB+ of storage per document.

Implementing keyword search is straightforward. Most developers can set up Elasticsearch or PostgreSQL full-text search in a few days. The concepts are intuitive: create an index, define analyzers for tokenization, and write queries using familiar Boolean logic.

{
  "query": {
    "bool": {
      "must": [
        { "match": { "title": "semantic search" } },
        { "range": { "date": { "gte": "2024-01-01" } } }
      ]
    }
  }
}

Semantic search requires multiple components working together: embedding models to convert text to vectors, vector databases like Pinecone or Weaviate to store and search embeddings, and often RAG (Retrieval-Augmented Generation) systems to enhance results with language models.

# Semantic search pipeline
from sentence_transformers import SentenceTransformer
import pinecone

# 1. Generate embeddings
model = SentenceTransformer('all-MiniLM-L6-v2')
query_embedding = model.encode("semantic search")

# 2. Search vector database
index = pinecone.Index("documents")
results = index.query(
    vector=query_embedding.tolist(),
    top_k=10,
    include_metadata=True
)

The complexity extends to model selection, embedding dimension optimization, index maintenance, and handling model updates. Teams typically need 2-6 months to build production-ready semantic search, compared to 1-4 weeks for keyword search.

Keyword search is economical to run. A typical Elasticsearch cluster handling 10M documents might cost $50-200/month on AWS, with most of the expense in storage and basic compute. The infrastructure requirements are predictable and scale linearly.

Semantic search costs are dominated by GPU compute for embedding generation and vector storage overhead. The same 10M documents become 7.5GB+ of vector data (compared to ~1GB for text indexing), requiring specialized vector databases and often GPU instances for real-time embedding.

• Embedding generation: $100-500/month for GPU instances
• Vector storage: 5-10x storage costs compared to text indexes
• Specialized databases: Pinecone, Weaviate licensing costs
• Model serving: Additional inference infrastructure
• Development time: 3-5x longer implementation cycles

However, the ROI calculation changes when considering user satisfaction and conversion rates. E-commerce sites report 15-25% improvements in search-driven revenue after implementing semantic search, often justifying the additional infrastructure costs.

Major platforms demonstrate different approaches based on their specific needs. GitHub uses keyword search for code repositories because developers search for exact function names, file paths, and specific syntax. The precision of keyword matching aligns perfectly with how developers think and search.

Conversely, Netflix employs semantic search for content discovery. When users search for 'funny space movies', they don't want exact text matches—they want comedic science fiction films. Netflix's recommendation system uses embeddings to understand genre relationships, mood, and viewing context.

E-commerce giants like Amazon use hybrid search approaches. Product searches for 'iPhone charger' use keyword matching for exact product identification, while searches like 'gifts for tech enthusiasts' leverage semantic understanding to surface relevant categories and products.