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To choose an enterprise search platform, first match the product category to the job: Microsoft Search for Microsoft 365 employee search, Glean for packaged cross-SaaS workplace search, Elastic or Azure AI Search for developer-controlled retrieval, Algolia for customer and ecommerce search, Google Cloud Agent Search for Google Cloud applications, and IBM Watson Discovery for document intelligence. Then validate permissions, freshness, relevance, total cost, and AI grounding on your own data.
Enterprise search is not one market or one product. A platform that works well for an employee searching Slack, SharePoint, and Salesforce may be a poor choice for an online catalog, public help center, or retrieval-augmented generation application. The defensible buying decision is the platform that retrieves important information with correct permissions, acceptable freshness, useful relevance, manageable operating cost, and measurable business results.
Key takeaways
- Microsoft Search is the natural first evaluation for organizations centered on Microsoft 365 because basic Microsoft Search has no separate search charge within Microsoft 365.
- Packaged workplace-search products reduce implementation work, while search infrastructure platforms provide more control over schemas, ranking, deployment, APIs, and retrieval.
- Permission enforcement, deletion propagation, and permission-revocation testing are launch-blocking requirements for internal and AI-powered search.
- Enterprise search pricing can include licenses, indexed records, queries, storage, embeddings, generated answers, connectors, private networking, implementation, and ongoing administration.
- A vendor proof of concept should use representative company data, real queries, restricted documents, deleted content, changed permissions, and measurable relevance and grounding tests.
What does enterprise search mean?
Enterprise search is a system that connects to organizational data sources, extracts and normalizes content and metadata, builds an index or retrieval layer, applies identity and access controls, interprets queries, ranks results, and presents documents, records, snippets, answers, or actions.
The term covers several materially different jobs. IBM’s overview of enterprise search describes retrieval from disparate organizational sources and connects modern enterprise search with generative AI, retrieval-augmented generation, and agentic AI. Elastic’s definition also includes both internal organizational search and customer-facing search across websites, ecommerce, knowledge bases, and customer service.
#1 Best Overall
| Search category | Typical users | What the product must do well |
|---|---|---|
| Workplace search | Employees, contractors, and executives | Search across SaaS applications while preserving user and group permissions |
| Intranet search | Employees and authenticated partners | Find policies, pages, document libraries, people, and current internal information |
| Customer or support search | Customers, agents, and support teams | Retrieve help articles, tickets, product documentation, and approved answers |
| Ecommerce or product discovery | Shoppers and application users | Handle catalog search, filters, merchandising, personalization, recommendations, and analytics |
| Search infrastructure | Developers and product teams | Provide APIs, indexes, ranking controls, vector retrieval, facets, analytics, and deployment options |
| Document intelligence | Legal, operations, research, and knowledge teams | Perform OCR, passage retrieval, classification, extraction, contract analysis, and question answering |
What kind of enterprise search platform do you need?
The first buying decision is the product category and operating model, not whether a vendor labels its technology “AI search.” Use the following fit test to create an initial shortlist.
| Buyer situation | Likely first candidates | Important qualification |
|---|---|---|
| Microsoft 365 is the main productivity environment and employees need search | Microsoft Search; Microsoft 365 Copilot or Microsoft Graph connectors where appropriate | Confirm non-Microsoft source coverage, connector quotas, guest behavior, and whether a separate custom application is needed |
| Google Workspace and Google Cloud are central | Google Cloud Agent Search / Vertex AI Search | Model query, indexing, storage, semantic, and generative-answer charges separately |
| Employees need one search experience across many SaaS tools | Glean or a comparable packaged workplace-search product | Validate each required connector, synchronization behavior, permissions, data residency, and exportability |
| Developers need ranking, schema, retrieval, and deployment control | Elastic, Azure AI Search, Algolia, or another search infrastructure platform | Budget for ingestion, relevance tuning, security design, operations, and the user experience |
| Customer-facing, ecommerce, or product search is the main problem | Algolia, Coveo, Elastic, Azure AI Search, or Google Cloud, depending on the workload | Prioritize latency, catalog scale, merchandising, personalization, uptime, and conversion analytics |
| Document extraction and passage-level analysis are central | IBM Watson Discovery or a comparable document-intelligence platform | Check OCR, tables, extraction quality, document limits, and deployment requirements |
| On-premises, hybrid, sovereign, or unusually strict deployment control is required | Self-managed Elastic, IBM Cloud Pak for Data, or a vendor with verified support for the required model | Verify that every connector and AI capability—not only the core index—supports the required environment |
Microsoft Search and Azure AI Search are different products. Microsoft Search is the Microsoft 365 search experience. Azure AI Search is a developer-oriented retrieval service for building custom search, RAG, and agent applications. Treating them as interchangeable can produce the wrong architecture and cost model.
What questions should you answer before comparing vendors?
A requirements statement should describe the information problem in operational terms. “We need AI search” is not sufficient because AI search can mean semantic ranking, vector retrieval, summarization, grounded answers, or agent actions.
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- Who searches? Identify employees, guests, contractors, customers, support agents, administrators, or anonymous visitors.
- What repositories are searched? List systems such as SharePoint, OneDrive, Google Drive, Gmail, Slack, Teams, Confluence, Jira, Salesforce, ServiceNow, Box, Dropbox, GitHub, file shares, databases, websites, and proprietary applications.
- What content is involved? Specify documents, records, PDFs, scanned PDFs, presentations, spreadsheets, images, videos, tickets, source code, product records, and structured fields.
- What output is required? Decide whether users need links, records, snippets, cited answers, summaries, recommendations, or actions.
- What happens when no answer exists? Define whether the system should abstain, show related content, route the user to a team, or trigger a workflow.
- How fresh must the index be? Incident response, HR, legal, sales, and customer support may have different tolerances for stale content.
- What is the cost of a wrong answer or exposed document? A poor product recommendation, an incorrect internal policy answer, and a leaked legal document have different risk profiles.
- What governance applies? Record data residency, encryption, audit, retention, regulatory, private-network, hybrid, and on-premises requirements.
Which content sources must the platform connect?
Build a source inventory before accepting a vendor’s connector-count claim. The inventory should include Microsoft SharePoint and OneDrive; Google Drive and Gmail; Slack and Microsoft Teams; Confluence and Jira; Salesforce and ServiceNow; Box and Dropbox; GitHub and internal code repositories; databases and warehouses; network drives and file shares; websites; proprietary line-of-business systems; and PDFs, scans, presentations, spreadsheets, images, and videos.
For each required source, ask whether the connector has native support, incremental synchronization, deletion propagation, permission and group mapping, custom-field support, on-premises or private-network access, rate-limit handling, monitoring, retries, and general-availability status. Also determine whether the vendor or your team maintains the connector.
Elastic lists connection paths for sources including SharePoint, ServiceNow, MongoDB, Google Cloud, Google Drive, Salesforce, GitHub, Slack, Jira, Box, OneDrive, S3, databases, and custom connectors. Glean claims more than 275 app connectors; treat that figure as a vendor claim and test the specific applications and behaviors your organization requires rather than comparing connector counts alone.
| Connector question | Why the answer matters |
|---|---|
| Does the connector support incremental sync? | Full reindexing can increase cost, delay freshness, and stress the source system. |
| Are deletions propagated? | Deleted material must not remain searchable or appear in generated answers. |
| Are inherited and group permissions mapped? | Incorrect ACL mapping can expose content or hide necessary results. |
| Are custom fields and dates indexed? | Metadata supports filters, authority, freshness, and business-specific ranking. |
| Does it handle rate limits and outages? | A small proof of concept may not reveal production synchronization failures. |
| Is the connector generally available? | Preview functionality can have different support, reliability, and production restrictions. |
| Can the connector reach private or on-premises sources? | SaaS-only connectivity may disqualify a platform in regulated or hybrid environments. |
Can the platform preserve permissions?
Permission enforcement is a launch-blocking requirement, not a feature-table checkbox. A platform must prevent unauthorized documents, snippets, highlights, generated answers, citations, autocomplete suggestions, and inferred facts from reaching users.
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Test document-level ACLs, folder and site permissions, inherited permissions, group changes, access revocation, guest users, contractors, external sharing, structured-data row or field security, permission changes during incremental indexing, and citations that a user cannot open.
Microsoft states that authenticated users see only content they can access in the trusted cloud. Microsoft also documents a narrower guest behavior: guests can search content in SharePoint sites to which they have been invited but do not receive organization-wide search results. See Microsoft’s Microsoft Search FAQ for the product’s documented behavior and licensing qualifications.
Security can fail at multiple points. The source system may send incorrect permissions, the index may become stale, the query layer may apply authorization too late, or a language model may summarize restricted passages. Microsoft’s Azure AI Search SharePoint indexer documentation warns that customers are responsible for preserving and honoring permissions at query time, configuring network controls, and auditing the pipeline.
Ask every vendor this exact question: Can the platform guarantee that a user never receives a result, snippet, generated answer, citation, or inferred fact from content the user is not authorized to access? Require a technical explanation and a live test rather than a general security statement.
How should you evaluate enterprise-search quality?
Search quality should be measured on your own corpus and real queries. A polished vendor demo cannot reveal how the platform handles internal acronyms, duplicate policies, scanned PDFs, contradictory documents, or recently revoked permissions.
Retrieval methods to compare
- Lexical search matches words and fields and is often strong for exact names, identifiers, and technical terms.
- Semantic search uses meaning to retrieve relevant content when query and document wording differ.
- Vector search retrieves based on embeddings and is useful for similarity and meaning-based retrieval.
- Hybrid search combines lexical and semantic or vector signals and can handle both exact terminology and natural-language questions.
- Learning-to-rank and personalization use behavior, authority, user context, metadata, or business rules to reorder results.
Evaluate exact-name lookup, acronyms, misspellings, synonyms, natural-language questions, long questions, ambiguous queries, recent documents, duplicate documents, conflicting versions, structured filters, multilingual queries, tables, spreadsheets, scanned PDFs, restricted content, no-result queries, and adversarial or prompt-injection content.
Look for field boosts, synonyms, query rules, freshness and authority signals, entity recognition, result diversification, and controls for answer grounding. Elastic documents hybrid and semantic search, filtering, faceting, typeahead, customizable templates, and document-level security as workplace-search capabilities, but each buyer should verify results on its own workload.
Rank #2
What does AI search actually include?
AI search is a bundle of capabilities rather than a single feature. Separate semantic retrieval, vector indexing, hybrid retrieval, query rewriting, summarization, generated answers, citations, follow-up questions, conversational memory, multimodal retrieval, entity extraction, personalization, agent actions, model selection, safety controls, and evaluation tooling.
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- hallucinated or unsupported claims;
- incomplete synthesis that omits contradictory evidence;
- stale information despite a plausible answer;
- citations that do not support the exact statement;
- prompt injection contained in indexed documents;
- accidental disclosure through a summary;
- overconfident answers when retrieval is incomplete.
A credible platform should expose retrieved passages and citations, support answer abstention, and let the buyer evaluate answers against a curated question-and-answer set. Evaluate retrieval and generation separately: retrieving the right documents does not prove that the model will produce a complete or supported answer.
How fresh is the search index?
Freshness is the time between a source change and safe, accurate search visibility. Require vendors to document initial indexing time, incremental-sync frequency, event-driven versus scheduled ingestion, deletion latency, permission-change latency, source API failure handling, replay and backfill procedures, monitoring, and alerting.
A search platform that is relevant but a day out of date may be unsuitable for incident response, sales operations, HR, legal work, or customer support. Test a newly created document, an edited document, a deleted document, a changed permission, and a deprovisioned user. Record the time at which each change stops or starts appearing in results and answers.
How much technical control does your team need?
Choose a platform with APIs, SDKs, query controls, schema design, custom analyzers, ranking rules, synonyms, typo tolerance, field boosts, facets, vector and hybrid retrieval, custom embeddings, webhooks, batch ingestion, observability, test environments, infrastructure-as-code, regional deployment, private networking, and export options when those controls are essential to the product.
Elastic is a strong fit among the reviewed examples for engineering-led organizations that want flexible search and vector infrastructure with cloud-hosted, serverless, or self-managed choices. Elastic’s pricing page distinguishes hosted, serverless, and self-managed models and advertises a 99.95% monthly uptime SLA for Platinum and Enterprise cloud tiers. The SLA applies to the specified tiers and should not be generalized to every Elastic deployment or workload.
Azure AI Search is similarly oriented toward custom applications. Azure provides APIs, SDKs, identity, networking, indexes, vector retrieval, semantic capabilities, and integration with Azure AI services, but the customer owns more of the ingestion and permission architecture than with a finished workplace-search product.
What are the trade-offs between packaged workplace search and search infrastructure?
Packaged workplace search generally offers faster deployment and a ready-made employee experience. Search infrastructure generally offers more control but transfers more implementation and operational responsibility to the buyer.
| Decision factor | Packaged workplace search | Search infrastructure platform |
|---|---|---|
| Initial deployment | Usually faster because connectors and user experience are prebuilt | Requires ingestion, schema, security, relevance, and application work |
| Ranking and schema control | Usually more limited or vendor-managed | Greater control over fields, ranking, analyzers, filters, and retrieval |
| Employee experience | Often included | Usually must be designed and maintained by the buyer |
| Connector dependency | Coverage and behavior depend on the vendor | Buyer may build or operate missing connectors |
| Customer-facing applications | May be a poor fit | Usually better suited to custom websites, products, and workflows |
| Operations | More infrastructure and relevance work is vendor-managed | Buyer owns more monitoring, tuning, scaling, and failure recovery |
| Lock-in risk | Can increase through proprietary connectors, ranking, and UX | Can still exist, but APIs and export options may provide more portability |
Which enterprise search platforms fit common use cases?
Microsoft Search
Best fit: Microsoft 365-centric organizations seeking employee search with minimal additional platform deployment.
Microsoft Search provides a native Microsoft 365 experience, permission-aware results, and strong integration with SharePoint, OneDrive, and Microsoft identity. Basic Microsoft Search is included in the Microsoft 365 search experience without a separate search charge, although Microsoft notes that some Microsoft 365 Copilot connector capabilities have licensing quotas and purchasable additional quota. Review the official Microsoft Search FAQ for current qualification and guest-search details.
Microsoft Search is less suitable as a neutral, highly customized retrieval layer across a heterogeneous application estate. A custom application, RAG system, or non-Microsoft data architecture may point toward Azure AI Search instead.
Azure AI Search
Best fit: Azure teams building custom search, RAG, or agent applications.
Azure AI Search offers developer APIs and SDKs, Azure identity and networking integration, indexes, vector retrieval, semantic capabilities, and application-level control. The trade-off is that the customer owns more ingestion, authorization, monitoring, and cost design.
Microsoft documents a SharePoint indexer using the 2026-05-01-preview API version. The SharePoint indexer requires Azure AI Search Basic tier or higher, is documented as preview and best-effort supported, and is not recommended for production workloads until it is generally available. Treat that feature as a proof-of-concept dependency, not as an assumed production capability. See the SharePoint indexer documentation and Azure AI Search pricing page.
Elastic
Best fit: Engineering-led organizations needing customizable search and vector infrastructure, including hybrid or self-managed deployments.
Elastic supports broad connection paths, APIs, lexical, semantic, vector, and hybrid search, filtering, faceting, and ranking control. Elastic can serve both internal workplace search and customer-facing applications. The main limitation is responsibility: connector permissions, synchronization, relevance tuning, operations, and the polished search experience require careful ownership.
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Best fit: Organizations wanting a packaged employee-search and AI knowledge experience across many SaaS applications.
Glean emphasizes broad application connector coverage, personalized workplace search, and permission enforcement. Glean’s public materials claim more than 275 app connectors, but connector counts do not establish ACL fidelity, custom-field indexing, deletion speed, on-premises support, or maintenance quality. Pricing is generally sales-led on the reviewed product page, so require a workload-based proposal and validate exportability, data residency, and synchronization behavior.
Glean may be excessive for a single website, product catalog, or narrow repository, and it generally provides less low-level search control than a platform such as Elastic or Azure AI Search.
Algolia
Best fit: Customer-facing, ecommerce, product, catalog, and application search where speed, relevance tuning, analytics, and merchandising matter.
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Google Cloud Agent Search / Vertex AI Search
Best fit: Google Cloud customers building search and generative-answer applications over structured, unstructured, or website data.
Google Cloud provides managed search and generative-answer capabilities with published usage-based pricing. Buyers must distinguish standard search, enterprise search, and advanced generative answers, then model query, indexing, storage, semantic, and generation costs together.
IBM Watson Discovery
Best fit: Document-heavy workloads requiring document understanding, passage retrieval, content mining, or extraction.
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Other candidates worth screening
Coveo can be considered for enterprise relevance, commerce, customer service, and personalization. Lucidworks can be considered for customizable enterprise search and AI retrieval. Sinequa can be considered for complex knowledge discovery and regulated or information-intensive environments. Apache Solr and OpenSearch can be considered where open-source control and in-house search engineering are priorities. These are shortlist candidates, not universal winners; confirm current product status, connectors, support, pricing, and deployment directly with each vendor.
How much does an enterprise search platform cost?
Enterprise search total cost is the platform bill plus the work required to make retrieval secure, current, relevant, and useful. A practical model is:
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
+ relevance tuning
+ content cleanup
+ security and compliance work
+ ongoing administrationThe major cost drivers are indexed documents or records, extracted text volume, monthly and peak query volume, connector count, synchronization frequency, embedding generation and refresh, generated-answer volume, tenants or indexes, SLA and support tier, data residency, professional services, and internal engineering headcount.
| Platform | Published pricing signal | How to interpret it |
|---|---|---|
| Microsoft Search | Basic Microsoft Search has no additional search charge within Microsoft 365 | Copilot connector quotas and additional quota can affect the broader Microsoft search and AI cost |
| Google Cloud Agent Search | According to Google Cloud’s pricing page reviewed August 18, 2026, standard search is $1.50 per 1,000 queries; enterprise search with core generative answers is $4 per 1,000 queries; advanced generative answers add $4 per 1,000 queries | Indexing, storage, semantic features, and generation can add charges; treat the figures as US-dollar list-price signals, not a quotation |
| IBM Watson Discovery | According to IBM’s pricing page reviewed August 18, 2026, Plus starts at $500 per month for up to 10,000 documents and 10,000 queries per month | IBM says country, taxes, duties, and product availability can change pricing; the page also lists a 30-day no-cost trial |
| Algolia | According to Algolia’s pricing page reviewed August 18, 2026, Build is free; Grow includes 10,000 search requests and 100,000 records before overage; Grow Plus lists $1.75 per additional 1,000 search requests and $0.40 per additional 1,000 records | Elevate is an annual, volume-based enterprise plan; model queries, records, crawler use, and AI features together |
| Azure AI Search | Tier-based Azure pricing | Microsoft says displayed Azure AI Search prices are estimates and actual pricing varies by agreement, purchase date, currency, and other factors |
| Elastic | Configuration-based cloud, serverless, or self-managed pricing | Expect a workload calculation or quote rather than one universal enterprise-search price |
| Glean | Sales-led pricing on the reviewed product page | Require pricing tied to users, connectors, indexed content, AI usage, support, and implementation |
Pricing and product signals in this table are based on official pages reviewed August 18, 2026. Regional taxes, currencies, contract terms, editions, usage, availability, and professional services can change the final price.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should a weighted enterprise-search scorecard include?
Use a scorecard to make trade-offs visible, then change the weights for the actual use case. The following baseline gives relevance and security the greatest influence.
| Criterion | Suggested weight | What to verify |
|---|---|---|
| Retrieval quality on real queries | 20% | Benchmark performance, relevance tuning, and semantic and hybrid behavior |
| Security and permission enforcement | 20% | ACL fidelity, revocation, guest access, and answer-level security |
| Connector coverage and freshness | 15% | Required sources, incremental sync, deletion latency, and permission latency |
| AI answer quality and grounding | 10% | Citations, abstention, prompt-injection controls, and evaluation tools |
| Deployment and data governance | 10% | SaaS, private cloud, hybrid, on-premises, regions, encryption, and compliance |
| Implementation effort | 10% | Connector work, migration, APIs, professional services, and staffing |
| Total cost of ownership | 10% | License, queries, records, storage, embeddings, compute, and implementation |
| Analytics and administration | 5% | Query logs, zero-result reporting, testing, audit logs, and admin controls |
For public ecommerce or customer search, increase the weight for latency, uptime, merchandising, personalization, conversion analytics, catalog scale, and indexing speed. For an internal AI knowledge system, increase the weight for permission fidelity, answer grounding, source freshness, document parsing, auditability, and model and deployment controls.
How should you run an enterprise-search proof of concept?
A serious proof of concept uses the buyer’s own data and representative users. Ask vendors to test the following corpus:
- current and outdated documents;
- duplicate and near-duplicate files;
- misleading filenames;
- PDFs and scanned PDFs;
- spreadsheets and slide decks;
- internal acronyms and terminology;
- conflicting policies;
- restricted and deleted documents;
- documents with changed permissions;
- multilingual content, if relevant;
- structured records alongside unstructured documents.
Build a benchmark containing exact titles, natural-language questions, acronyms, misspellings, synonyms, people or department lookups, date-sensitive queries, “latest policy” requests, structured filters, no-answer queries, queries whose best document is restricted, multi-document questions, scanned or tabular content, and contradictory sources.
Metrics to record
- precision at the top 1, 3, 5, and 10 results;
- recall for known-answer queries;
- zero-result and reformulation rates;
- time to a useful result;
- click-through and successful-search rates;
- stale-result rate;
- deletion-propagation and permission-revocation time;
- citation correctness and answer-abstention quality;
- latency at realistic concurrency;
- connector failure and recovery time.
Do not use click-through rate alone. Users can click a poor result because every alternative is worse. Pair behavioral metrics with judged relevance, security tests, freshness tests, and answer-grounding review.
Security test accounts
Create separate accounts for ordinary employees, executives, contractors, guests, users in multiple groups, recently deprovisioned users, and users who can access one folder but not another. Check restricted content in result titles, snippets, highlights, generated answers, citations, suggested follow-ups, autocomplete, and administrator-visible analytics.
What implementation work remains after selecting a platform?
- Define the use case, owners, risk tolerance, and success metrics.
- Inventory sources, permissions, owners, data quality, and retention rules.
- Select a representative test corpus and create benchmark queries.
- Build a permission-aware ingestion pipeline.
- Configure lexical, semantic, and hybrid retrieval as appropriate.
- Test unauthorized access, deletion, permission revocation, and connector outages.
- Tune ranking, metadata, synonyms, freshness, and authority signals.
- Pilot with representative users and record zero-result and reformulation behavior.
- Measure adoption, successful searches, answer quality, latency, and security outcomes.
- Assign ownership for content, connectors, taxonomy, relevance, security, and administration.
- Define data export, migration, termination assistance, and deletion procedures before signing a long contract.
Which red flags should stop an enterprise-search purchase?
- The vendor cannot explain whether authorization is enforced at ingestion time, query time, or both.
- The proof of concept does not test deleted documents or recently revoked permissions.
- A connector list gives no detail about ACLs, incremental sync, custom fields, rate limits, or maintenance.
- AI answers have no citations, abstention behavior, retrieved-passage view, or evaluation method.
- The production architecture depends on preview, beta, or best-effort functionality without an accepted risk plan.
- The pricing proposal excludes connector work, embeddings, generated answers, private networking, storage, support, or professional services.
- The vendor cannot explain how prompt injection in indexed documents is detected or mitigated.
- There is no export path for indexes, content, embeddings, synonyms, ranking rules, or configuration.
- The vendor claims security, compliance, accuracy, uptime, or productivity gains without specifying the service, region, workload, date, and evidence.
How do you make the final shortlist?
Choose two or three candidates from the category that matches the use case, then run the same corpus, queries, accounts, metrics, and cost assumptions against each candidate. A practical shortlist might be Microsoft Search versus Glean for employee discovery, Elastic versus Azure AI Search for a custom Azure or hybrid application, Algolia versus Coveo for customer-facing product search, or Google Cloud Agent Search versus Azure AI Search for cloud-native generative retrieval.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteDo not select a universal “best enterprise search platform.” Select the platform whose security model, connectors, freshness, retrieval quality, AI behavior, deployment model, operating burden, and total cost fit the business problem. A native-suite capability should win when it covers the requirement; a packaged workplace product should win when deployment speed and employee experience matter most; and infrastructure should win when application control, data-model flexibility, or deployment control justify the engineering effort.
Frequently Asked Questions
Is Microsoft Search the same as Azure AI Search?
No. Microsoft Search is the Microsoft 365 search experience for users, while Azure AI Search is a developer-oriented retrieval service for custom search, RAG, and agent applications. Microsoft 365-centric organizations should evaluate Microsoft Search first for employee discovery, but custom applications may require Azure AI Search or another infrastructure platform.
What is the most important enterprise-search buying criterion?
Permission enforcement is the most important launch-blocking criterion for internal enterprise search because unauthorized titles, snippets, citations, or generated answers can expose sensitive information. Test ACL mapping, group changes, deletion, revocation, guest access, and answer-level security on the buyer’s own data.
How much does Google Cloud Agent Search cost?
According to Google Cloud’s pricing page reviewed August 18, 2026, standard search is listed at $1.50 per 1,000 queries, enterprise search with core generative answers at $4 per 1,000 queries, and advanced generative answers add $4 per 1,000 queries. Indexing, storage, semantic features, region, taxes, and contract terms can change the total cost.
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No. Connector count does not prove ACL fidelity, incremental synchronization, deletion propagation, custom-field support, rate-limit handling, on-premises compatibility, or maintenance quality. Compare the specific connectors and behaviors required by the organization.
The Bottom Line
Bottom line: Choose an enterprise search platform by matching the product category to the job, then make permissions, freshness, retrieval quality, AI grounding, deployment, and total cost pass a real-data proof of concept. Microsoft Search is the logical first test for Microsoft 365 employee search; Glean suits packaged cross-SaaS workplace search; Elastic and Azure AI Search suit controlled custom retrieval; Algolia suits product and ecommerce search; Google Cloud Agent Search suits Google Cloud generative applications; and IBM Watson Discovery suits document-heavy intelligence. None is universally best.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
