34 products

Best Enterprise Search 2026

Compare 34 enterprise search and knowledge platforms independently reviewed by knowledge management, IT, and AI leaders. Elastic and Coveo lead traditional enterprise search, while Glean, Sinequa, and Lucidworks have repositioned around AI-grounded answers and retrieval-augmented generation. Algolia dominates site search. Filter by workplace search, site search, RAG, knowledge graph, and on-prem support. Every review is verified. No vendor pays for ranking.

Elastic Enterprise Search
Elastic
From $95/mo
4.5
1,840 reviews
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Coveo Platform
Coveo
Enterprise pricing
4.4
380 reviews
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Algolia
Algolia
From $0/10k requests
4.6
720 reviews
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Glean
Glean
Enterprise pricing
4.7
380 reviews
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Sinequa
Sinequa
Enterprise pricing
4.4
140 reviews
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Lucidworks Fusion
Lucidworks
Enterprise pricing
4.3
180 reviews
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Microsoft Search
Microsoft
Included with M365
4.2
540 reviews
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AWS Kendra
Amazon Web Services
From $810/mo
4.2
220 reviews
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Vespa
Vespa.ai
Open-source
4.5
120 reviews
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Pinecone
Pinecone
From $0
4.6
380 reviews
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Weaviate
Weaviate
From $0
4.5
180 reviews
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Yext
Yext
Enterprise pricing
4.2
640 reviews
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Enterprise search and RAG 2026

The enterprise search market has been transformed by retrieval-augmented generation. Glean has emerged as the leading purpose-built workplace assistant, indexing connected SaaS systems and delivering grounded answers with citations. Traditional vendors Elastic and Coveo have responded with hybrid search combining keyword, vector, and generative answers.

Site search remains a distinct category. Algolia dominates e-commerce site search and developer-led implementations, while Yext leads brand search and answers across owned and earned channels.

Vector databases (Pinecone, Weaviate, Qdrant) have become a parallel layer for AI applications, particularly RAG and agent memory. Pair enterprise search with enterprise AI, ECM, and the full directory. Compare Glean vs Elastic or see Best Platforms for RAG.

Related Categories

Frequently Asked Questions

What is enterprise search?
Enterprise search indexes and retrieves information across internal systems such as SharePoint, Google Drive, Confluence, ticketing systems, and intranets, delivering unified results to employees. Modern enterprise search platforms combine keyword and vector retrieval with permissions awareness and increasingly generative answer synthesis.
How is enterprise search different from a vector database?
Vector databases store and retrieve embeddings for similarity search and serve AI applications. Enterprise search platforms ingest content, manage permissions, present results to end users, and orchestrate ranking models. Many enterprise search platforms now use vector databases internally, but the user-facing scope is different.
What is retrieval-augmented generation (RAG)?
Retrieval-augmented generation grounds large language models in trusted enterprise content by retrieving relevant passages at query time and supplying them to the model as context. RAG reduces hallucinations and enables citations. It has become the default architecture for enterprise AI assistants and knowledge applications.
How long does enterprise search deployment take?
Connector-based platforms such as Glean and Microsoft Search commonly reach pilot in 2 to 6 weeks. Custom Elastic, Coveo, or Lucidworks deployments with bespoke connectors, ranking models, and permissions plumbing typically run 4 to 12 months for full production rollout.
How does TechVendorIndex rank enterprise search platforms?
We weight verified buyer reviews, connector breadth, permissions accuracy, generative answer quality, and total cost of ownership. No vendor pays for placement. Full methodology at /methodology/.
Last updated: May 2026
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How Index.Html fits the Enterprise Search category

Index.Html is one of several options in the Enterprise Search category on TechVendorIndex. The right way to evaluate it is in the context of your specific buyer profile rather than in isolation: who in your organisation will use it day-to-day, what scale of deployment you need, what existing systems it has to integrate with, and which capabilities are non-negotiable for your use case. Index.Html's strengths land best for buyers who match a particular profile; the related pages and comparisons surface the trade-offs against the most common alternatives so a buyer can decide quickly whether to keep it on the shortlist or rule it out.

What to evaluate during a proof-of-concept

Buyers who shortlist Index.Html typically focus their proof-of-concept on three things: depth of functionality in the specific use case that triggered the project, real-world performance and stability under representative load, and the practical experience of integrating with the rest of the existing stack. Vendor-provided demonstration environments rarely surface integration friction, identity-management edge cases, or data-volume scaling limits. A structured pilot against a representative slice of your own data is the single highest-leverage step in the evaluation.

Total cost considerations

The list price for Index.Html is only one element of the three-year total cost of ownership. Buyers also need to estimate implementation services, internal team time, integration platform fees, training and change-management costs, and any adjacent tooling required to make the product useful in the buyer's specific environment. Vendors often offer attractive year-one pricing that does not reflect the true ongoing cost; ask explicitly for a three-year quote with assumptions documented before signing.

When to revisit this decision

Each profile on TechVendorIndex is reviewed at the same cadence as the parent category. Index.Html's position in the Enterprise Search category may shift as competing products release new capabilities, as Index.Html itself releases new versions, or as pricing models change. Buyers who selected Index.Html more than two years ago may want to re-evaluate even if the product is meeting needs today.