Semantic AI search vs keyword search for docs and support. Real costs, latency, and the four scenarios where each one wins.
TL;DR:
- Traditional search (BM25, keyword) is fast, deterministic, and cheap. AI search (embeddings, generative answers) is slower, costs 10-100x more per query, and answers natural-language questions far better.
- For pure docs sites with strong keyword content, traditional search often wins on cost-per-resolution.
- For support chat, ticket deflection, and ecommerce intent search, AI search is now table stakes.
- The serious teams in 2026 are running hybrid: keyword for recall, vectors for re-ranking, generative for the final answer. Algolia, Zendesk, Inkeep, Kustomer, and MeiliSearch all ship this pattern now.
- AI search is overkill if your KB is under 50 articles, your traffic is under 5,000 searches a month, or your users already use the right keywords.
The "search box on a help center" is one of the most under-thought parts of the support stack. Most teams either accept whatever shipped with their helpdesk or bolt on a search-as-a-service tool and call it a day. The result is the search experience that drives the famous Nielsen finding that roughly half of help-center searches return zero useful results.
AI search promises to fix this. It mostly delivers, but with real trade-offs in latency, cost, and predictability. This post is a clear-eyed comparison of where each approach earns its place in 2026.
Traditional search matches words. AI search matches meaning. A keyword engine asked "my package is stuck" looks for documents containing "package" and "stuck." A semantic engine converts the query into a vector and finds documents about delayed shipments, lost orders, and tracking status even when none of those exact words appear in the query.
That single difference cascades into everything else: cost, latency, infrastructure, predictability, and the kind of content that wins.
The dominant algorithm is BM25, a refinement of TF-IDF that has powered serious search systems for 30 years. It scores documents by how often query terms appear in them, weighted by how rare those terms are across the corpus.
Strengths:
Weaknesses:
Modern AI search uses two layers:
Strengths:
Weaknesses:
| Dimension | Traditional Search (BM25) | AI Search (Hybrid + RAG) |
|---|---|---|
| Best for | Strong keyword content, docs sites, dev portals | Natural-language questions, support chat, mixed-intent queries |
| Latency | 5-50 ms | 1-5 seconds for generated answers, 100-300 ms for retrieval only |
| Cost per 1k queries | $0.05-$0.50 | $0.50-$15 depending on model and answer length |
| Accuracy on natural questions | Poor without query rewriting | Strong out of the box |
| Setup effort | Hours to a day | Days to weeks (chunking, embeddings, eval) |
| Determinism | Fully deterministic | Stochastic by default |
| Works without LLM | Yes | No |
Cost-per-query numbers are rough. Algolia's Grow Plus plan lists keyword search at $0.50 per 1,000 requests and NeuralSearch lifts that to $0.75 per 1,000 retrieval calls. Generative answers (using Claude Sonnet 4.6 or GPT-4o on top) add another $2 to $10 per 1,000 depending on answer length and how much context you pass.
Winner: Hybrid, biased toward keyword.
Developer audiences search using API names, error codes, and specific function signatures. They need exact matches. They also occasionally type natural-language questions like "how do I authenticate without a session cookie."
Run BM25 as the primary retriever and use vectors as a fallback or re-ranker. Inkeep, MeiliSearch hybrid, and Algolia NeuralSearch all do this. Pure semantic search will frustrate developers searching for 403_FORBIDDEN.
Winner: AI search with generative answers.
Shopper queries are messy. "Where is my order," "package not here yet," "can I get a refund I changed my mind" all map to different articles in your help center. The retrieval needs to bridge vocabulary, and the answer is most useful when it pulls the relevant fact out and presents it directly.
This is where Zendesk Generative Search, Chatsy, Intercom Fin, and similar are doing their best work. The economics work because deflected tickets save real money.
Winner: Hybrid, with strong keyword and filters.
Catalog search has a different shape than help-center search. Users filter by category, price, color, and size. They search for brand names and SKUs. Pure semantic search frequently returns "stylistically similar" results that miss exact-attribute matches.
Algolia, Coveo, and similar still win this category by combining strong keyword and faceting with optional vector boosts. Pure RAG is usually a step backward here.
Winner: AI search.
Internal queries are nearly always natural language. "How much PTO do I have left," "who do I talk to about a hardware upgrade," "is there a policy on contractor expenses." Employees rarely type the keywords used in policy doc titles. Glean built a billion-dollar company on this fact.
This is where AI search has the strongest case in 2026: the queries are messy, the corpus is well-bounded, and the cost per query is acceptable because volumes are moderate.
Embed everything. Run a vector search at query time. Optionally re-rank the top results with a cross-encoder or LLM. Generate an answer.
Best for: small, well-curated KBs (under 5,000 articles). Most modern docs sites.
Run both BM25 and vector search in parallel. Merge results using reciprocal rank fusion or a learned re-ranker. Generate from the top merged set.
Best for: anything serious. This is what Algolia, MeiliSearch, OpenSearch, and Zendesk all converged on.
Run BM25 first. If the top result has high confidence (high BM25 score, exact title match, etc.) serve it as a normal search result. Otherwise fall back to RAG.
Best for: budget-sensitive sites with a clear keyword content strategy. Keeps the 80 percent of "easy" queries at keyword-search cost.
This is where most "AI for everything" posts skip ahead. Be honest about when you do not need this.
If your KB is small, a flat index of titles plus full-text BM25 will work fine and will be cheaper, faster, and easier to reason about. AI search shines when there are too many articles for a human to grep mentally. A 30-article KB does not benefit.
Generative search at $0.005 to $0.015 per query is cheap in absolute terms but the engineering complexity of chunking, embedding, eval, and monitoring is real. If you serve 1,000 searches a month and your help center has 80 articles, your time is better spent fixing the articles than swapping search engines.
If every help-center article title is the literal phrase users type into the search box, BM25 already nails it. The benefit of semantic search is closing the vocabulary gap. If there is no gap, there is no benefit.
Embedded search-as-you-type, autocomplete, faceted product search, and live search inside an app cannot tolerate one-second response times. Stick with keyword search and put RAG on the chatbot.
Is Google's AI Overview the same as help-center AI search? Same idea, different scope. Google AI Overviews and Help Center AI search both retrieve and summarize. The difference is corpus size and trust: a help center has tens to thousands of articles you control; Google indexes the open web. The help-center version is dramatically easier to keep accurate.
How do I evaluate whether my help center needs AI search? Run a "zero-results" audit. Pull six months of help-center search logs. Calculate the percentage of queries that returned zero results or zero clicks. If that number is above 25 percent, you have a vocabulary gap that AI search will close. Below 10 percent, you do not have a search problem.
Can I just use a chatbot instead of a help-center search box? For many teams, yes. A well-built AI chatbot is functionally a search-and-answer box with conversation memory. If your support traffic is dominated by self-service and your KB is the source of truth, a chatbot can replace the search box on many help-center pages. Keep the search box for power users who prefer it.
What about cost at high volume? At 1 million help-center searches a month, AI search costs $5,000 to $15,000 just in inference depending on stack choices. Hybrid keyword-first patterns cap that at around $1,000 to $3,000 by serving most queries with cheap retrieval. Above 5 million queries a month, the cost math forces hybrid by default.
The honest answer in 2026 is that "AI vs traditional" is the wrong framing. The right question is which pattern fits which surface in your support stack: keyword for fast autocomplete and exact-match content, semantic for messy natural-language questions, generative for the final answer when a direct response saves real time.
If you want AI search and a chatbot in one place without standing up your own RAG pipeline, start a free Chatsy account. It indexes your help center, runs hybrid retrieval, and generates answers grounded in your content. Or see pricing for plan limits.
How to evaluate a customer support chatbot before launch using a 25-question golden set, four scoring dimensions, and the right tooling stack.
