Executive Summary
A frontier model is only as trustworthy as what you feed it — so the decision that actually shapes enterprise AI in 2026 is the retrieval layer that grounds it on your own knowledge, and whether that layer honors who is allowed to see what.
The model is no longer the hard part. Every serious assistant and agent now runs the same loop — retrieve the relevant facts from your systems, hand them to a language model, and answer with citations — and the quality of the answer is decided almost entirely by the retrieve step, not the model. That is why retrieval-augmented generation has become the central enterprise-AI purchase of the year, and why four very different kinds of vendor are now selling into the same budget line. Turnkey work assistants (Glean) ship a permission-aware index and hundreds of connectors as a finished product. Hyperscaler-native suites (Microsoft 365 Copilot, Google Gemini Enterprise, Amazon Q Business) bolt grounded search onto the cloud and productivity estate you already own. Build-on search engines (Elastic, Coveo, Sinequa, OpenSearch, Lucidworks) give you the retrieval primitives to assemble exactly the experience you want. And RAG-as-a-service APIs (Vectara) hand you the whole ingest-embed-retrieve-ground pipeline behind a single endpoint.
This guide provides a vendor-neutral evaluation framework for 8 leading platforms, weighing the four things that actually decide a deployment: permission-aware retrieval that enforces every source system’s access controls at query time, answer quality with faithful grounding and verifiable citations, the breadth and freshness of connectors into where your knowledge actually lives, and how far the platform extends from answering questions to taking agentic action. Get those right and the model underneath becomes a swappable commodity; get permissions wrong and you have built a data-leak engine with a chat box.
Conflating the four camps is the first and most expensive mistake. A turnkey assistant and a search SDK are not competing products at different prices — they are different commitments of engineering time, control, and lock-in. Most real shortlists therefore compare across camps, framing the choice around how much you want to build versus buy, which estate your knowledge and identities already live in, and how sensitive that knowledge is.
Why Enterprise Search & RAG Matters for Enterprise Strategy
Generative AI fails in production far more often on grounding and permissions than on raw model quality. An assistant that confidently invents an answer, or one that surfaces a document an employee was never cleared to read, destroys trust faster than a slow or terse one ever could. The retrieval layer is where that trust is won or lost, which is why enterprise search has quietly become the gating decision for every downstream AI ambition — copilots, knowledge assistants, customer-service deflection, and autonomous agents all sit on top of it.
Three forces converge in 2026. Knowledge has scattered across hundreds of SaaS applications, so no single ecosystem holds the full picture and connector breadth becomes a first-order requirement. The center of gravity is moving from chat to agents, raising the stakes on permission enforcement because a system that can act on a document is far more dangerous than one that merely shows it. And boards now expect AI that is auditable and governed, not a black box — making citations, access logging, and provable retrieval boundaries procurement requirements rather than nice-to-haves. Weigh each platform on its retrieval and permission spine at least as heavily as on the polish of its assistant.
Build vs. Buy & Sourcing Decision
Almost no enterprise should hand-assemble a full RAG stack from scratch — stitching together a vector store, an embedding service, a reranker, a chunking pipeline, a connector framework, a permission model, and a chat UI is technically possible and almost always a worse use of engineering time than buying or self-hosting a finished product. The real decision is which layer you buy at: a turnkey assistant that hands you connectors, index, and UI as a product; a hyperscaler suite that grounds on the estate you already run; a search engine you build a bespoke experience on; or a RAG API you call from your own application. The right answer turns on how much you want to build, where your knowledge and identities already live, and how sensitive that knowledge is — and large programs frequently run two of these patterns at once.
The single most consequential dimension cutting across every option is the permission model. Two architectures dominate, and the difference is not cosmetic. Early-binding systems copy each document’s access-control list into the index at crawl time and filter results against the user’s group memberships; late-binding (query-time) systems check the source system’s live permissions on every request. Early binding is fast but can go stale between crawls; late binding is current but heavier. Whichever a vendor uses, insist on seeing what happens the moment access is revoked — that gap is where the breaches live.
| Your Situation | Recommended Path | Rationale |
|---|---|---|
| Knowledge spread across many SaaS apps, want fast time-to-value, lean platform team | Turnkey work assistant (Glean-class) | A finished product with a permission-aware index and hundreds of pre-built connectors gets a horizontal, cross-app assistant live in weeks — you configure and govern rather than build retrieval from parts. |
| Already standardized on Microsoft 365, Google Workspace, or AWS | Hyperscaler-native (Copilot / Gemini Enterprise / Q) | Grounding rides your existing identity, permissions, and content; licensing rolls into the enterprise agreement; and the assistant lands inside tools employees already use, lowering adoption friction dramatically. |
| Need a bespoke search or commerce experience embedded in your own product | Build-on search engine (Elastic, Coveo, OpenSearch) | When the experience is the differentiator — site search, product discovery, an embedded support agent — you want retrieval primitives, relevance tuning, and APIs, not someone else’s pre-built UI. |
| Have an app and an LLM already, just need grounded retrieval behind an API | RAG-as-a-service (Vectara-class) | A managed ingest-embed-retrieve-ground pipeline behind one endpoint adds grounding and citations to an existing application without standing up retrieval infrastructure or an MLOps team. |
| Highly regulated, sovereign, or air-gapped with sensitive corpora | Private/on-prem search platform (Sinequa, self-hosted Elastic) | Deep connector security models, on-prem and sovereign deployment, and end-to-end auditability matter more here than a polished SaaS assistant whose index sits outside your boundary. |
| Heavy Salesforce / ServiceNow service operation wanting agent grounding | Retrieval layer for an existing agent platform (Coveo, AWS) | A passage-retrieval or knowledge-base API can ground the agent platform you already run (Agentforce, Bedrock) on secure enterprise content without ripping out the workflow tooling around it. |
Key Capabilities & Evaluation Criteria
Weight these domains against your own corpora, identity estate, and risk posture. The instinct is to over-index on how clever the assistant sounds in a demo, but in production the deployment lives or dies on the unglamorous layers underneath — whether retrieval respects permissions, whether connectors stay fresh, and whether answers are faithfully grounded and cited. Those are what your CISO, your auditors, and your skeptical first users will actually test.
| Capability Domain | Weight | What to Evaluate |
|---|---|---|
| Permission-Aware Retrieval & Security | 25% | Faithful enforcement of every source system’s ACLs at query time; early- vs. late-binding permission model and how quickly revoked access is reflected; handling of groups, sharing links, and external users; document-level security trimming; encryption and tenant isolation; and complete access and query audit logging |
| Answer Quality, Grounding & Citations | 20% | Retrieval relevance on your domain (hybrid lexical + semantic, reranking), faithfulness of generated answers to retrieved sources, inline verifiable citations, hallucination detection and refusal behavior when evidence is thin, freshness and recency handling, and graceful degradation on ambiguous queries |
| Connector Breadth, Freshness & Ingestion | 20% | Number and depth of pre-built connectors to your actual systems (collaboration, ITSM, CRM, code, wikis, file stores); incremental crawl frequency and near-real-time updates; ACL ingestion alongside content; permission-aware handling of structured data and long/complex documents; and the cost of building a custom connector |
| Agentic Actions & Orchestration | 15% | Pre-built and custom agents grounded on the same permission-aware index; reliable tool/function calling and write-back actions into source systems; support for open agent protocols (MCP, A2A); a managed agent runtime with scoped permissions, human-in-the-loop checkpoints, and step-level tracing |
| Deployment, Governance & Compliance | 10% | Deployment boundary options (multi-tenant SaaS, in-VPC, on-prem, air-gapped, sovereign), data-residency regions, no-training-on-your-data commitments, SOC 2 / ISO 27001 / HIPAA / FedRAMP coverage and EU AI Act alignment, DLP and sensitivity-label honoring, and admin controls over what is indexed and exposed |
| Extensibility, Analytics & Operations | 10% | APIs for retrieval and custom UIs, model choice and bring-your-own-LLM, relevance tuning and feedback loops, search and answer analytics, gap and content-quality reporting, latency at production scale, and observability over usage, cost, and answer quality |
Vendor Landscape
Sort the field into four camps before you compare anyone. Turnkey work assistants (Glean) ship a horizontal, permission-aware index plus hundreds of connectors and an assistant as a finished product. Hyperscaler-native suites (Microsoft 365 Copilot, Google Gemini Enterprise, Amazon Q Business) ground generative AI on the productivity and cloud estate you already run, riding your existing identity and permissions. Build-on search engines (Elastic, Coveo, Sinequa, and open-source OpenSearch or Lucidworks) hand you retrieval primitives, relevance tuning, and APIs to assemble a bespoke experience. RAG-as-a-service APIs (Vectara) deliver the whole ingest-embed-retrieve-ground pipeline behind a single endpoint.
The camps blur deliberately. The hyperscalers expose their grounding as APIs (Microsoft’s Copilot Retrieval API, Amazon’s Kendra GenAI Index, Google’s Agent Search) so you can build on them too; the search engines ship turnkey assistants and agent builders on top of their primitives; and a retrieval specialist like Coveo will happily ground someone else’s agent platform. So most real shortlists compare across camps — a turnkey assistant against your incumbent hyperscaler’s grounded search against a search engine you’d build on — rather than within one. The deciding question is rarely “whose assistant is smartest” but “whose index, permission model, and connectors fit our knowledge, our identities, and our risk tolerance.”
Strengths: The reference turnkey work assistant: a horizontal, permission-aware index built on a knowledge graph that spans hundreds of pre-built connectors (collaboration, ITSM, CRM, code, wikis) and respects each source’s access controls so users see only what they should. Strong personalized relevance, an assistant and pre-built plus custom agents on the same index, and a growing agent platform — all independent of any one cloud or productivity suite, which is its core advantage over the hyperscalers when knowledge is scattered across many SaaS apps. Considerations: Premium, quote-based enterprise pricing, with agent usage metered on a credit model that takes care to forecast; value depends on connector coverage for your specific stack; as an overlay it duplicates some search the hyperscalers bundle into licenses you already own; and a fast-moving independent whose roadmap and commercial terms warrant the usual diligence on a category-defining startup.
Strengths: Grounds on your Microsoft Graph through the semantic index and Microsoft Search, honoring existing Microsoft 365 permissions so retrieval respects what each user can already access; Copilot connectors (Graph connectors) extend the index to third-party repositories; the now-GA Copilot Retrieval API and Copilot Search expose that grounded retrieval to your own apps; and Copilot Studio plus Agent 365 add custom agents and a governance control plane — all inside the Entra identity and compliance estate most enterprises already run. Considerations: Deepest value assumes a committed Microsoft 365 estate; retrieval quality leans on well-governed SharePoint and Graph, so over-sharing and stale permissions in your tenant become Copilot’s problem too; the surface area is broad and renames quickly across overlapping Copilot, Search, and Foundry branding; and per-seat licensing at scale is a material line item.
Strengths: The platform formerly launched as Agentspace, now Gemini Enterprise, unifies intranet search, a multimodal assistant, and an agent platform over your organization’s data with permissions-aware access; pre-built connectors to Confluence, Jira, SharePoint, ServiceNow, Salesforce, and more; agentic RAG and a RAG Engine built on the proven Vertex AI Search (now Agent Search) retrieval stack; and native strength on multimodal content and Google Workspace grounding. Considerations: Strongest for Google Cloud– and Workspace-centric organizations; rapid product and brand churn (Vertex AI Search to Agent Search, Agentspace to Gemini Enterprise) makes documentation a moving target; enterprise adoption still trails Microsoft and Glean in many shops; and Google’s history of sunsetting products colors commitment-longevity diligence.
Strengths: The default retrieval foundation to build on: mature hybrid search (BM25 lexical plus dense and ELSER sparse vectors, fused with reciprocal rank fusion), the semantic_text field and Inference API that remove most embedding boilerplate, broad deployment freedom (self-managed, Elastic Cloud, or serverless), and Agent Builder to turn the stack into a retrieval and reasoning engine. Document-level security and huge scale make it a workhorse for custom, regulated, or on-prem RAG. Considerations: A developer platform, not a turnkey workplace assistant — you build the connectors, permission mapping, and UI, or buy them elsewhere; getting relevance right is real engineering work; operating self-managed clusters at scale demands expertise; and licensing across open-source, Elastic License, and managed tiers takes care to navigate.
Strengths: An AI-relevance platform with deep heritage in commerce, customer-service, and website search, now extended to GenAI grounding: a Passage Retrieval API and RAG-as-a-Service that ground custom and third-party agents (including Salesforce Agentforce and Amazon Bedrock) in secure, permission-trimmed enterprise content. Strong relevance tuning, unified indexing across content sources, and analytics make it a fit where search is the customer experience. Considerations: More a relevance and retrieval layer than a finished internal-knowledge assistant; realizing the value assumes you are building the surrounding experience or agent; strongest in digital-experience, commerce, and service use cases rather than horizontal employee search; and enterprise pricing reflects the platform’s breadth.
Strengths: A managed, permissions-aware assistant over enterprise data with 40-plus connectors that index source ACLs alongside content and filter answers to what each user may access, with inline citations; the decoupled Amazon Kendra GenAI Index provides high-accuracy semantic retrieval reusable across Q Business and Bedrock Knowledge Bases, so the same index can ground both a packaged assistant and your own agents; native fit with AWS IAM, PrivateLink, and VPC; and consumption pricing on the AWS bill. Considerations: Deepest value assumes an AWS-standardized estate; the overlapping portfolio (Q Business, Kendra, Bedrock, and the newer Quick Suite branding) takes effort to navigate; the packaged assistant is less of a polished horizontal product than the turnkey leaders; and connector depth, while broad, should be checked against your specific systems.
Strengths: A managed RAG-as-a-service platform that handles the full pipeline behind one API — ingestion, embedding, hybrid retrieval, reranking, grounded generation, and citations — so teams add grounded answers to an existing app without standing up retrieval infrastructure. Distinctive focus on faithfulness: a hallucination-evaluation model and a factual-consistency API score how well an answer is supported by its sources, with SaaS, customer-managed VPC, and on-prem deployment options. Considerations: An API and pipeline, not a turnkey assistant or a horizontal connector fleet — you bring the application and often the connectors; smaller brand and ecosystem than the hyperscalers; permission enforcement depends on the metadata and filters you feed it at ingest; best realized when faithful, low-hallucination grounding behind your own UI is the core need.
Strengths: A long-standing enterprise-search platform — now part of European group ChapsVision — built for the hardest corpora: 200-plus deep connectors, strong document-level security and multilingual handling, and an LLM-based GenAI Assistant and agentic layer that synthesize precise, grounded answers over sensitive content. Sovereign, on-prem, and private-cloud deployment make it a fit for defense, life sciences, financial services, and engineering knowledge. Considerations: Aimed at large, complex, and regulated deployments rather than quick turnkey rollouts; implementation and tuning are a project, not a switch-on; smaller mindshare than the hyperscalers and Glean; and value is realized at the scale and security demands of regulated enterprises, where it is strongest.
Pricing Models & Cost Structure
Pricing in this category is a tangle because the four camps charge on entirely different units. Turnkey assistants price per seat, often with separate agent or query credits on top. Hyperscaler suites bundle grounded search into per-seat add-ons to licenses you may already hold, or bill retrieval and index usage as consumption. Build-on search engines price on infrastructure, data volume, or managed-tier compute. And RAG APIs bill per query, per ingested unit, or per token. The unit of consumption, far more than any headline rate, determines what you pay as usage grows — and an agent or retrieval loop silently multiplies that unit on every call.
Two cost traps recur. First, the index itself: re-crawling and embedding hundreds of connectors with frequent refreshes is an ongoing cost that scales with corpus size and freshness, not seat count. Second, agentic usage: a single agent run may fan out across connectors, take many steps, and call a model repeatedly, so per-seat math badly understates spend once agents are in scope. No dollar figures appear below because published rates move constantly and most enterprise pricing is quote-based — model cost against your own seat count, corpus size, refresh frequency, and projected query and agent volume.
| Vendor | Pricing Model | Relative Tier | Key Cost Drivers |
|---|---|---|---|
| Glean | Per-seat subscription (quote-based) + agent/query credits | Premium | User count, connector scope, agent run volume and complexity (steps, connectors, model used), platform and support tier |
| Microsoft 365 Copilot | Per-seat add-on to M365; consumption for connectors & Retrieval API | Premium per seat | Copilot seat count, Graph connector volume and refresh, Copilot Studio agent usage, Retrieval/Search API consumption, existing M365 agreement |
| Google Gemini Enterprise | Per-seat platform plans; consumption for retrieval/agent usage | Enterprise-tier | Seat edition, indexed data and query volume, connector usage, agent and RAG Engine consumption, Workspace and Google Cloud commitment |
| Elastic | Consumption / resource-based; self-managed, Cloud, or serverless | Moderate; infra-driven | Data volume and retention, compute and memory for vector/hybrid search, deployment tier, inference/embedding usage, support level |
| Coveo | Platform subscription by queries / content sources / API usage | Enterprise; usage-led | Query and Passage Retrieval API volume, number of content sources indexed, modules (commerce, service, search), seats and support tier |
| Amazon Q Business | Per-user subscription tiers + Kendra GenAI Index & connector usage | Moderate; pay-as-you-go | Q Business user tier, Kendra GenAI Index capacity, connector and document volume, query usage, AWS commitment level |
| Vectara | Usage-based API: ingested volume, queries, generation; tiered plans | Moderate; consumption | Volume of data ingested and stored, query and generated-answer volume, deployment model (SaaS vs. VPC vs. on-prem), support tier |
| Sinequa (ChapsVision) | Enterprise license / subscription by data & deployment footprint | Enterprise; deployment-led | Indexed data volume, connector count, on-prem/sovereign vs. cloud deployment, GenAI Assistant and agent usage, professional services |
Implementation & Rollout
Sequence by trust, not by breadth. The fastest way to kill an enterprise-search program is to launch widely on an over-shared corpus and let the first users find documents they shouldn’t — or hallucinated answers they can’t verify. Prove permission enforcement and grounding on a contained, well-governed corpus first; expand connectors and audiences only once the retrieval and permission spine is demonstrably solid.
Pick one or two high-value use cases with named owners and success metrics. Inventory the source systems that hold the relevant knowledge and audit their permissions for over-sharing — the index will faithfully reflect whatever access mess exists today. Choose the camp (turnkey, hyperscaler, build-on, RAG API) and deployment boundary that fit your data sensitivity, and define the permission and grounding tests you will hold the platform to.
Stand up the platform, wire in identity, and connect the first set of source systems with their access controls ingested alongside content. Before any wide release, run the permission test on real sensitive documents and the grounding test on questions your corpus can and cannot answer. Tune relevance and confirm citations are faithful, then ship to a small, friendly pilot audience behind those controls.
Broaden connector coverage and audience as confidence grows, monitoring index freshness and permission accuracy as each source is added. Introduce agentic actions where they earn their place — grounded on the same permission-aware index, with scoped write-back permissions, human-in-the-loop checkpoints, and step-level tracing — and stand up analytics on usage, answer quality, and content gaps.
Operationalize the program: routine permission and freshness audits, a feedback loop that tunes relevance from real usage, content-gap remediation, and FinOps on query, index, and agent spend. Re-test grounding and access enforcement after major source or model changes, and measure realized productivity and deflection against the original case to drive the roadmap.
Selection Checklist & RFP Questions
Use this checklist during evaluation to confirm each shortlisted platform covers what actually decides a production enterprise-search and RAG deployment — not just what demos well on a clean corpus.