AI-Powered Enterprise Search Software Pricing 2026
Compare pricing for 8 ai-powered enterprise search tools. Find the right software for your budget.
AI-Powered Enterprise Search software pricing ranges from $20 to $183 per user/month in 2026. The typical cost is around $49/user/month across 8 popular tools. Top picks: Vectara (custom pricing), Meilisearch ($20–$20/user/mo), Searchie ($41–$183/user/mo), and 5 more.
All AI-Powered Enterprise Search Tools
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Vectara
Custom pricing
Meilisearch
$20–$20/month
Searchie
$41–$183/month
Elastic Enterprise Search
Custom pricing
Glean
Custom pricing
Coveo
Custom pricing
Dust.tt
$29–$29/month / user
Guru AI Search
Custom pricingNo matches
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AI-Powered Enterprise Search Pricing FAQ
01 What is AI-powered enterprise search?
AI-powered enterprise search lets employees find answers across all internal systems (docs, wikis, tickets, chat, drives) using natural language. Instead of keyword matching, it uses semantic search and LLMs to understand intent, retrieve relevant content, and generate direct answers with citations, respecting each user's access permissions.
02 How much does enterprise AI search cost?
Enterprise AI search is typically priced per user per month or by data volume and connectors, with most vendors quoting custom enterprise pricing rather than public list prices. Costs rise with the number of integrated data sources, seats, and query volume. Expect implementation and connector configuration to be part of the total.
03 How is AI enterprise search different from regular search?
Regular search matches keywords within a single system. AI enterprise search unifies many systems, understands natural-language questions semantically, generates synthesized answers with sources, and enforces per-user permissions so people only see what they're allowed to. This turns scattered company knowledge into a single answer engine.
04 What hidden costs come with enterprise search?
Watch for per-connector fees, implementation and data-mapping services, ongoing maintenance as source systems change, and LLM token costs for generated answers. Permission-aware indexing across many systems adds complexity and cost, and large data volumes increase storage and re-indexing expenses.