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The right enterprise search platform is not the one with the longest feature list. It is the one that retrieves your important information with the correct permissions, acceptable freshness, useful relevance, manageable total cost, and measurable business results. Start by choosing the product category—native-suite search, packaged workplace search, search infrastructure, customer or ecommerce search, or document intelligence—then prove the shortlist against your own data.
First decide what kind of enterprise search you need
“Enterprise search” describes several different jobs. A platform designed for employee knowledge discovery is not automatically suitable for a public product catalog or a regulated document archive.
| Use case | What the system must do | Likely product category |
|---|---|---|
| Workplace search | Let employees find permitted information across collaboration and business systems. | Microsoft Search, Glean, or another packaged workplace-search product |
| Intranet search | Search owned pages, policies, document libraries, and internal websites. | Native-suite search, packaged search, or a custom search service |
| Customer or support search | Retrieve help articles, tickets, product documentation, and approved answers. | Coveo, Algolia, Elastic, Azure AI Search, Google Cloud, or a specialized service |
| Ecommerce and product discovery | Handle catalog search, filters, merchandising, personalization, recommendations, and conversion analytics. | Algolia, Coveo, Elastic, Azure AI Search, or Google Cloud |
| Search infrastructure | Provide APIs, indexes, ranking, vector retrieval, facets, and analytics for a custom experience. | Elastic, Azure AI Search, Algolia, OpenSearch, or Apache Solr |
| Document intelligence | Extract text and fields, retrieve passages, classify content, and answer questions over complex documents. | IBM Watson Discovery or a comparable document-intelligence platform |
Elastic describes enterprise search as covering both internal organizational search and customer-facing websites, ecommerce, knowledge bases, and customer service: elastic.co/what-is/enterprise-search. IBM similarly frames enterprise search as retrieval from disparate sources, increasingly connected to retrieval-augmented generation (RAG) and agentic AI: ibm.com/think/topics/enterprise-search.
Seven questions to answer before comparing vendors
1. Who searches, and what outcome is required?
Specify whether the user is an employee, customer, support agent, seller, analyst, or software application. Decide whether the expected result is a link, record, passage, generated answer, recommendation, or action. Define what should happen when no reliable result exists.
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2. Which sources must be connected?
Create an inventory that may include SharePoint, OneDrive, Google Drive, Gmail, Slack, Microsoft Teams, Confluence, Jira, Salesforce, ServiceNow, Box, Dropbox, GitHub, databases, warehouses, network shares, websites, PDFs, scanned files, presentations, spreadsheets, images, and video transcripts.
For every required source, verify:
- native connector availability and whether it is generally available or preview;
- incremental synchronization, deletion propagation, and retry behavior;
- permission and group mapping, including guests and contractors;
- custom-field support and API rate-limit handling;
- private-network, on-premises, or sovereign-region support;
- who maintains the connector and how failures are monitored.
Elastic lists supported paths for sources including SharePoint, ServiceNow, MongoDB, Google Cloud, Google Drive, Salesforce, GitHub, Slack, Jira, Box, OneDrive, S3, databases, and custom connectors: elastic.co/enterprise-search/workplace-search. Glean advertises more than 275 app connectors, but that is a vendor claim; validate the specific connector behavior your project needs at glean.com/enterprise-search.
3. What are the security boundaries?
Permission enforcement is a launch-blocking requirement, not a checkbox. Test document, folder, site, row, and field-level access; inherited permissions; external sharing; group changes; revocation; and links or citations that a user cannot open.
Ask vendors: Can a user ever receive a result, snippet, generated answer, citation, autocomplete suggestion, or inferred fact from content they are not authorized to access? Test authorization at ingestion and query time, and determine how the system behaves while permissions are changing.
Microsoft says authenticated users see only content they can access in its trusted cloud. Guests can search SharePoint content in sites to which they were invited, but do not receive organization-wide search results: learn.microsoft.com/en-us/microsoftsearch/faqs. Microsoft’s Azure SharePoint indexer documentation also makes customers responsible for preserving permissions, network controls, and pipeline audits: learn.microsoft.com/en-us/azure/search/search-how-to-index-sharepoint-online.
4. How fresh must results be?
Ask for initial indexing time, incremental-sync frequency, event-driven versus scheduled ingestion, deletion latency, permission-change latency, API-failure handling, replay procedures, and alerting. A system that is accurate but a day old may fail for incident response, sales, HR, legal, or customer support.
5. What does “AI search” mean in the proposal?
Separate semantic retrieval, vector indexing, hybrid retrieval, query rewriting, summarization, generated answers, citations, follow-up questions, conversational memory, multimodal retrieval, personalization, and agent actions. Confirm model choices, bring-your-own-model support, prompt controls, safety features, and evaluation tools.
Generative answers add failure modes: hallucination, unsupported synthesis, omission of contradictory evidence, stale information, citation mismatch, prompt injection in indexed documents, and accidental disclosure through summaries. Require visible source passages, citations, abstention when evidence is insufficient, and testing against a curated question set.
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6. What deployment and governance constraints apply?
Document required regions, encryption, retention, audit logs, private networking, hybrid or on-premises deployment, regulatory contracts, and model-location requirements. Do not infer compliance from a general security page; verify the exact service, region, certification, and contractual terms.
7. How much engineering and operations can you own?
A packaged product can reduce connector and relevance work but limit schema and ranking control. An infrastructure platform offers more control but makes your team responsible for ingestion, permissions, relevance, observability, scaling, and the user experience.
Capabilities that matter in practice
Connectors and ingestion
Connector count is a weak comparison. A connector may be read-only, omit custom fields, lose ACLs, fail to remove deleted content, or support only a cloud edition. Confirm behavior at production volume, not just whether an icon appears on a marketing page.
Retrieval and ranking
Evaluate exact-name lookup, acronyms, misspellings, synonyms, natural-language questions, long queries, ambiguous terms, recent documents, duplicates, conflicting versions, multilingual content, tables, and scanned PDFs. Compare lexical, semantic, vector, and hybrid retrieval; learning-to-rank; field boosts; freshness and authority signals; people and entity recognition; and result diversification.
Elastic documents hybrid and semantic search, filtering, faceting, typeahead, customizable templates, and document-level security among its workplace-search capabilities: elastic.co/enterprise-search/workplace-search.
AI grounding and safety
Score retrieval and generation separately. A system may retrieve the right documents yet produce an incomplete answer. Require citation correctness, source passage visibility, answer abstention, contradictory-source handling, prompt-injection defenses, and controls over which documents can ground an answer.
Administration and analytics
Look for query logs, zero-result and reformulation reports, relevance tuning, audit trails, A/B testing, content-owner workflows, connector health, and role-based administration. Click-through rate alone is not a quality metric: users may click a poor result because every alternative is worse.
Developer control and portability
Assess APIs and SDKs, query language, custom analyzers, synonyms, typo tolerance, facets, embeddings, webhooks, batch ingestion, infrastructure-as-code, test environments, regional deployment, private links, index export, and migration of ranking rules. Check whether raw content, metadata, embeddings, and taxonomies can be recovered if you change suppliers.
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Native-suite search
For a Microsoft 365-centric organization, Microsoft Search is the first baseline. Basic Microsoft Search is included in Microsoft 365 without a separate search charge, although Microsoft notes licensing quotas and purchasable additional quota for some Microsoft 365 Copilot connector capabilities: learn.microsoft.com/en-us/microsoftsearch/faqs. It is a strong fit for SharePoint, OneDrive, and Microsoft identity, but it is not the same product as Azure AI Search and may be less suitable as a neutral, highly customized layer across heterogeneous systems.
Google Cloud Agent Search (formerly associated with Vertex AI Search) is a managed option for Google Cloud teams building search and generative-answer applications over structured, unstructured, or website data. It is not automatically a prebuilt employee-search rollout.
Packaged workplace search
Products such as Glean provide a ready-made employee experience across many SaaS systems, with vendor-managed relevance and a broad connector ecosystem. They can shorten deployment, but pricing is generally sales-led and buyers must validate connector freshness, data residency, permissions, exportability, and customization. A packaged workplace product may be excessive for one website or catalog.
Developer search infrastructure
Elastic is a strong fit for engineering-led organizations that need lexical, semantic, vector, hybrid, filtering, and faceting controls, with hosted, serverless, or self-managed deployment. Its flexibility also means more responsibility for connector permissions, relevance tuning, and operations. Elastic distinguishes these deployment models and publishes a 99.95% monthly uptime SLA for Platinum and Enterprise cloud tiers; confirm the applicable tier and contract at elastic.co/pricing.
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Customer, ecommerce, and product search
Algolia is optimized for application and catalog search, with query rules, synonyms, analytics, personalization, recommendations, and public usage tiers. It is not a ready-made internal workplace-search experience; validate document ingestion and ACL requirements if you intend to use it for unstructured enterprise content.
Coveo, Elastic, Azure AI Search, and Google Cloud are other candidates for customer-facing search, but the weighting should emphasize latency, uptime, merchandising, catalog scale, personalization, and conversion analytics rather than employee identity integration.
Document intelligence
IBM Watson Discovery is better compared with document-understanding and passage-retrieval services than with universal employee search. It is suited to OCR, extraction, content mining, and document-heavy question answering, with hybrid deployment options through IBM Cloud Pak for Data. Confirm plan, region, and feature availability.
A weighted scorecard for a shortlist
Use these starting weights, then change them to match the use case:
| Criterion | Suggested weight | Evidence to verify |
|---|---|---|
| Retrieval quality on real queries | 20% | Benchmark precision, recall, relevance tuning, semantic and hybrid behavior |
| Security and permission enforcement | 20% | ACL fidelity, revocation, guest access, answer-level security |
| Connector coverage and freshness | 15% | Required sources, incremental sync, deletion and permission latency |
| AI answer quality and grounding | 10% | Citations, abstention, prompt-injection controls, evaluation tools |
| Deployment and data governance | 10% | SaaS, private cloud, hybrid, on-premises, regions, encryption, compliance |
| Implementation effort | 10% | Connector work, migration, APIs, services, staffing |
| Total cost of ownership | 10% | License, queries, records, storage, embeddings, compute, implementation |
| Analytics and administration | 5% | Query logs, zero-result reports, testing, audit logs, admin controls |
For public ecommerce, increase the weights for latency, uptime, merchandising, personalization, and conversion analytics. For internal AI knowledge systems, increase the weights for permission fidelity, grounding, freshness, parsing, auditability, and model controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How enterprise search pricing really works
Model the complete cost rather than comparing subscription headlines:
Total cost = license or platform fee
+ indexed-record or document charges
+ query charges
+ storage
+ vector-embedding costs
+ model-generation costs
+ connector or crawler costs
+ network and private-link costs
+ implementation and relevance tuning
+ content cleanup and security work
+ ongoing administrationKey drivers include document and record count, extracted text volume, query and peak-concurrency volume, connector count, synchronization frequency, embedding refreshes, generated-answer volume, tenant and index count, SLA tier, data residency, professional services, and internal engineering time.
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|---|---|---|
| Google Cloud Agent Search | $1.50 per 1,000 standard queries; $4 per 1,000 enterprise queries with core generative answers; advanced generative answers add $4 per 1,000 queries. | US-dollar list-price signals; indexing and storage also apply, and actual configuration may differ. Source |
| IBM Watson Discovery | Plus starts at $500 per month for up to 10,000 documents and 10,000 queries per month. | IBM also lists a 30-day no-cost trial; country, taxes, duties, and availability can change the price. Source |
| Algolia | Build free tier; Grow includes 10,000 search requests and 100,000 records; Grow Plus lists $1.75 per additional 1,000 requests and $0.40 per additional 1,000 records. | Elevate is volume-based with an annual contract; usage and AI features can add cost. Source |
| Azure AI Search | Tier-based Azure pricing. | Microsoft says displayed prices are estimates and vary by agreement, purchase date, currency, and other factors. Source |
| Elastic | Hosted, serverless, and self-managed options with trials and configuration-based pricing. | No single universal enterprise-search price is shown; calculate the selected deployment and workload. Source |
A proof-of-concept plan that produces evidence
- Define success. Set targets for successful searches, precision, zero-result rate, freshness, latency, answer grounding, and security incidents.
- Build a representative corpus. Include current and outdated files, duplicates, misleading filenames, PDFs, scans, spreadsheets, slides, acronyms, conflicting policies, restricted and deleted documents, multilingual content, and structured records.
- Map identities. Create test accounts for employees, executives, contractors, guests, multi-group users, and recently deprovisioned users.
- Create benchmark queries. Test exact titles, natural-language questions, acronyms, misspellings, synonyms, people, dates, “latest policy,” structured filters, no-answer questions, restricted best answers, multi-document questions, tables, and contradictions.
- Configure retrieval. Compare lexical, semantic, vector, and hybrid modes; tune synonyms, fields, freshness, authority, and filters.
- Test security. Check titles, snippets, highlights, generated answers, citations, follow-ups, autocomplete, and administrator-visible analytics for unauthorized leakage.
- Test lifecycle events. Measure indexing, deletion, permission revocation, API throttling, connector outage, retry, replay, and backfill times.
- Pilot with representative users. Record time to useful result, successful-search rate, reformulations, zero-result queries, answer quality, and perceived trust.
- Document ownership. Assign owners for content, connectors, taxonomy, relevance, security, model evaluation, and incident response.
- Negotiate exit terms. Confirm index and raw-content export, synonym and ranking-rule portability, deletion procedures, termination assistance, and renewal terms before signing.
Red flags that should stop a purchase
- A required connector is listed but does not preserve ACLs, custom fields, deletions, or incremental changes.
- The vendor cannot state maximum permission-revocation or deletion latency.
- A demonstration uses vendor-supplied content instead of your representative corpus.
- “AI-powered” is not defined as retrieval, generation, citations, agent actions, or a specific model.
- There is no abstention behavior, citation evaluation, prompt-injection testing, or answer audit trail.
- A critical connector or indexer is preview-only, especially for a production security boundary.
- Pricing cannot be modeled from documents, records, queries, storage, answers, support, and implementation.
- There is no practical export or migration path for content, metadata, embeddings, synonyms, and ranking rules.
- The proposed employee-search product is being used to solve a public catalog problem, or vice versa.
Shortlist by buyer situation
| Buyer situation | Start with | Why |
|---|---|---|
| Mostly Microsoft 365, employee search | Microsoft Search; consider Microsoft 365 Copilot or connectors | Native identity, SharePoint, and OneDrive experience with no separate basic-search charge |
| Mostly Google Workspace and Google Cloud | Google Cloud Agent Search | Managed search and generative applications integrated with Google Cloud |
| One employee experience across many SaaS tools | Glean or a comparable packaged workplace product | Prebuilt experience and broad connector coverage, subject to validation |
| Maximum developer control | Elastic, Azure AI Search, Algolia, OpenSearch, or Solr | APIs, ranking, schema, deployment, and retrieval controls |
| Customer, support, or ecommerce search | Algolia, Coveo, Elastic, Azure AI Search, or Google Cloud | Latency, catalog, merchandising, analytics, and customer-facing controls |
| Document extraction and passage retrieval | IBM Watson Discovery or comparable document intelligence | OCR, extraction, classification, and document-oriented analysis |
| Strict hybrid or on-premises requirements | Self-managed Elastic, IBM Cloud Pak for Data, or verified hybrid offerings | More deployment and data-location control |
Credible alternatives such as Coveo (coveo.com), Lucidworks (lucidworks.com), Sinequa (sinequa.com), Apache Solr (solr.apache.org), and OpenSearch (opensearch.org) belong on a controlled shortlist when their connectors, support, pricing, and deployment model match your requirements. They are candidates, not universal winners.
Frequently Asked Questions
Is Microsoft Search the same as Azure AI Search?
No. Microsoft Search is the Microsoft 365 employee-facing search experience. Azure AI Search is a developer-oriented service for building custom search, retrieval-augmented generation, and agent applications.
How many vendors should a proof of concept include?
Usually two or three finalists that represent the best-fitting categories. Include the native suite as a baseline when it covers a substantial share of the requirement.
Should click-through rate determine the winner?
No. Pair it with precision, recall, successful-search rate, reformulations, zero-result rate, freshness, latency, citation correctness, and permission tests.
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Choose by use case, not by connector count or an AI label. Prove permission fidelity, freshness, retrieval and answer quality, operational effort, and full cost on your own corpus before committing to a long contract.
Quick Recap
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