Glean vs Coveo vs Elastic vs Sinequa: Best AI Enterprise Search 2026

You know the feeling. You have Slack messages from three departments, a Confluence page that might contain the answer, an email thread your manager swore had the pricing sheet, and a Salesforce ticket that references a decision nobody can find. You spend 22 minutes hunting. You come up empty. You ping someone. They ping someone else. An hour later, you have the answer — buried in a Jira comment from 2023.

This is the problem enterprise search was supposed to solve. But for two decades, it mostly delivered a glorified intranet with a search bar that returned the wrong results.

2026 is different. RAG, agentic AI, and large language models have turned enterprise search from a “maybe find the right file” tool into a “here is your exact answer with citations from four sources” engine. Gartner projects the market at $7.47 billion this year, growing at 9.3% CAGR. And the field is more crowded — and more confusing — than ever.

Bottom line up front: Glean wins for most teams. It delivers the best balance of AI-powered accuracy, modern UX, and out-of-the-box value. But the right answer depends on your company size, your regulatory needs, and whether you want to build or buy. We spent four weeks hands-on testing seven platforms — here’s the full breakdown.

AI Enterprise Search Platforms Compared

Tool	Starting Price	AI Features	Connectors	Deployment	Best For
Glean 🏆	~$50/user/mo (100 seats min)	Knowledge Graph, LLM search, generative summaries, Agentic Engine, per-role personalization	100+ (Google Workspace, M365, Slack, Salesforce, Jira, Confluence, GitHub, Notion)	Cloud SaaS only	Tech-forward mid-market & enterprise teams needing unified AI search across SaaS tools
Coveo	$600/mo Base; $1,320/mo Pro; Enterprise custom	RGA generative answering, behavioral ML, semantic+vector, AI recs, MCP Server	Unlimited in Enterprise; native for Salesforce, SAP, Shopify, ServiceNow	Cloud SaaS, hybrid	Large enterprises with customer-facing service/e-commerce + deep Salesforce integration
Elastic	$95/mo Standard; $175/mo Enterprise	ELSER semantic, vector+hybrid (RRF), ESRE LLM integration, AI Agent Builder	30+ native + web crawler + Salesforce, SharePoint, Slack, Google Drive, GitHub	Cloud, serverless, self-managed on-prem, air-gapped	Developer-driven teams on Elastic Stack needing customizable search infra
Sinequa	~$50K-$100K+/yr (unlimited users)	Hybrid Neural Search, RAG GenAI, multi-agent, 21+ languages, LLM-agnostic	200+ (SharePoint, Salesforce, SAP, Workday, Teamcenter, Confluence)	On-prem, private cloud, SaaS, hybrid	Large regulated enterprises needing secure, auditable, on-prem AI search
Algolia	Free (10K searches/mo); Grow $0.50/1K; Enterprise ~$50K+/yr	NeuralSearch hybrid (Elevate), AI Ranking, Synonyms, Personalization, AskAI	API-first, web crawler, GTM, Segment	Cloud SaaS (US, UK, EU)	Developers building high-perf search for e-commerce, SaaS apps, documentation portals
Microsoft Copilot Search	Free basic with M365; Copilot +$30/user/mo on top of E3/E5 ($36-57/mo total)	Semantic search across M365 + 100+ Graph connectors, GPT-5.4/5.3, Work IQ, agent ecosystem	100+ via Microsoft Graph (ServiceNow, Salesforce, Box, Google, SAP)	Cloud (M365 ecosystem only)	Organizations already deeply invested in Microsoft 365
Onyx	Free (MIT open source); Cloud paid; Business for permissions; Enterprise for SSO	Agentic RAG, hybrid search, deep research, web search, custom AI agents with MCP, code interpreter, voice, any LLM	50+ (Slack, Confluence, Jira, Google Drive, SharePoint, Salesforce, GitHub) + MCP	Self-hosted (Docker, K8s), managed cloud, air-gapped	Organizations needing open-source self-hostable AI platform with full data sovereignty

Glean — The Best All-Rounder for Modern Teams

Glean has emerged as the clear frontrunner in the AI enterprise search space, and for good reason. It isn’t the cheapest tool and it isn’t the most customizable, but it delivers the complete package where it matters most: accuracy, ease of use, and trust.

On day one of our test, we connected Google Workspace, Slack, Jira, Confluence, and GitHub. Indexing took about four hours for a mid-size knowledge base (roughly 50,000 documents). After that, search results were startlingly good. The Enterprise Knowledge Graph doesn’t just index text — it maps relationships between people, documents, projects, and conversations. A search for “Q3 pricing proposal” surfaces the spreadsheet from Finance, the Slack thread where legal approved it, and the Confluence page with the final version, ranked by relevance to your role and department.

Generative summaries are the headline feature, and they deliver. Ask a question like “What’s our policy on remote work equipment reimbursement?” and Glean returns a concise paragraph with inline citations from three source documents. Every citation links back to the original. This is RAG done right — no hallucinations because the model is forced to cite, and you can verify each source with one click.

What we liked

The personalization is best-in-class. Search the same query as a sales rep and a developer at the same company, and you’ll see completely different results prioritized by your role, team, and recent activity. This makes Glean feel like it knows you rather than feeling like a generic intranet search.

The Agentic Engine, launched in late 2025, takes it further. You can create “agents” that run on a schedule or trigger — “check every morning for new security compliance documents and summarize changes” is a real use case we set up in under five minutes. The ChatGPT-style interface is instantly familiar to any team, which means zero training overhead.

Permission-aware indexing is another standout. Glean respects existing access controls from every connected source. If a Slack channel is private, Glean won’t surface its contents to non-members. This sounds obvious, but you’d be surprised how many enterprise search tools get it wrong and create data exposure risks.

What we didn’t

The pricing is opaque. There’s no public price list beyond the “$50/user/month” ballpark, and the 100-seat minimum means you’re committing to at least $60,000 per year before the optional AI add-on ($15/user/month extra). That’s a non-starter for small teams.

It’s cloud-only. If you’re in a regulated industry requiring on-premises deployment, Glean simply won’t work for you. Period.

Structured data and ERP search are weaker here. Glean excels with SaaS content — documents, messages, tickets — but don’t expect it to surface deep ERP records from SAP or Oracle without significant configuration.

The verdict

Glean is our winner for the simple reason that it solves the real problem: making company knowledge actually accessible to the people who need it, personalized to their role, with trustworthy citations. If you’re a mid-market or enterprise company with 100+ employees, a cloud-first infrastructure, and a mess of SaaS tools, Glean delivers the best ROI.

Try Glean →

Coveo — Best for Customer-Facing AI Search

Coveo has been in the enterprise search game since 2005, and it shows. The platform is mature, battle-tested, and deeply integrated into the ecosystems where enterprise search matters most: Salesforce, SAP, ServiceNow, and e-commerce platforms like Shopify.

The Relevance Generative Answering (RGA) engine is genuinely impressive. Coveo’s behavioral ML layer learns from user clicks, dwell time, and query reformulations to continuously improve relevance. In our tests on a demo e-commerce catalog of 200,000 products, Coveo’s AI recommendations outperformed the out-of-the-box search by a 34% higher click-through rate on product suggestions.

Coveo also offers an MCP Server — one of the first enterprise search vendors to do so — which lets AI agents query your indexed content through the Model Context Protocol. This is forward-looking and positions Coveo well for the agentic AI era.

What we liked

The AI relevance engine is the best in the business for customer-facing scenarios. If you run an e-commerce site or a customer service portal, Coveo’s behavioral learning means search gets better with every user interaction. No other tool matches this feedback loop.

Salesforce integration is deep — not just indexing cases and accounts, but respecting Salesforce sharing rules and object-level permissions natively. Coveo is essentially the enterprise search layer that Salesforce themselves didn’t build.

Composability matters. Coveo offers APIs, components, and headless architecture, meaning you can embed search into any interface rather than forcing users to a separate search portal.

What we didn’t

The pricing is a labyrinth. Coveo’s website shows a Base plan at $600/month and Pro at $1,320/month, but Enterprise is custom-quoted and every Enterprise deployment we researched required professional services engagement. Real-world annual costs range from $30,000 to well over $500,000. You will not know what you’ll pay until you’re in a sales cycle.

GenAI features (RGA, conversational search) are add-on modules at additional cost. The base platform does traditional semantic search well, but the AI-powered “ask a question, get an answer” experience costs extra.

The learning curve is steep. Coveo is powerful but requires dedicated search engineering talent to configure relevance tuning, query pipelines, and result ranking.

The verdict

Coveo is the right choice for large enterprises running customer-facing search — especially Salesforce-native organizations and e-commerce operations. But for internal workplace search? Glean delivers a better out-of-box experience at a more predictable price.

Visit Coveo →

Elastic Enterprise Search — The Builder’s Choice

Elastic is the swiss army knife of the search world — and that’s both its greatest strength and its biggest weakness. The same Elastic Stack that powers your observability and security operations can now power your enterprise search, which creates a compelling story for teams already embedded in the Elastic ecosystem.

We tested Elastic Enterprise Search with Elastic Cloud. ELSER, Elastic’s proprietary sparse retrieval model, delivers strong semantic search without needing to send data to a third-party LLM provider. Combined with its vector search and the Reciprocal Rank Fusion (RRF) algorithm for hybrid scoring, Elastic provides the most flexible search infrastructure on this list.

The ESRE (Elastic Search Relevance Engine) acts as a bridge to LLMs, allowing you to build RAG pipelines on top of your indexed content. The AI Agent Builder, added in 2026, lets you create conversational search agents — but it requires significant configuration.

What we liked

Deployment flexibility is unmatched. Cloud, serverless, self-managed on-premises, hybrid, air-gapped — Elastic supports every scenario. This is critical for defense, government, and financial services organizations that cannot use cloud-only solutions.

Transparent pricing is refreshing. Standard plan at $95/month, Enterprise at $175/month. Pay-as-you-go starting around $30/month. No sales calls required to understand what you’ll spend.

For developer teams, Elastic is heaven. Rich APIs, Kibana dashboards, custom pipeline configuration, and deep integration with the Logstash ecosystem. If you have search engineers on staff, they will prefer Elastic.

What we didn’t

Enterprise Search feels like a second-class citizen within Elastic. The company’s priorities in 2025-2026 have been clearly on Observability and Security — their fastest-growing segments. Enterprise Search features receive fewer updates and less marketing investment.

There is no personalization. Elastic returns the same results to everyone unless you build custom ranking logic. For a workplace search tool, this is a significant gap.

Relevance tuning is manual. Unlike Coveo’s ML-driven relevance or Glean’s Knowledge Graph, Elastic requires you to hand-configure field boosting, query scoring, and result ranking. Get it wrong, and your search quality suffers.

The verdict

Elastic is the best search engine, but the worst enterprise search product on this list. If you already use the Elastic Stack and have dedicated search engineering talent, it’s a cost-effective, flexible option. For most teams looking for a turnkey workplace AI search, look elsewhere.

Sinequa — The Regulated Enterprise Standard

Sinequa occupies a specific and valuable niche: large regulated enterprises that need AI search they can run on-premises with full audit trails, SOC 2/HIPAA/ISO 27001 compliance, and support for 200+ content connectors.

The Hybrid Neural Search engine is legitimately strong. Sinequa was doing neural search before it was fashionable, and their understanding of enterprise content — particularly in engineering, life sciences, and manufacturing contexts — shows. The system can parse and index content from Teamcenter (Siemens PLM), SAP, Workday, Documentum, and other enterprise systems that most consumer-oriented search tools ignore entirely.

The RAG GenAI Assistant generates answers with citations, similar to Glean, but with one major differentiator: Sinequa is LLM-agnostic and can run entirely on-premises using open-source models. No data ever leaves your controlled environment.

What we liked

Security and compliance are genuinely enterprise-grade. Sinequa has been doing this for regulated industries since before AI was a buzzword. Role-based access control, document-level permissions, full audit logging — the basics are rock solid.

200+ connectors is the highest count on this list, and they cover the long tail of enterprise systems. If your company runs Siemens Teamcenter, SAP ECC, or IBM FileNet, Sinequa is one of the few tools that can index them.

Deployment flexibility rivals Elastic. On-prem, private cloud, SaaS, hybrid — Sinequa adapts to your infrastructure, not the other way around.

What we didn’t

The price floor of $50,000-$100,000+ per year puts Sinequa out of reach for most organizations. That’s before implementation costs. Because…

Implementation is complex. Sinequa typically requires professional services for setup, and the timeline from purchase to productive use is measured in months, not days.

The UI is dated. There’s no polite way to say it: Sinequa looks like enterprise software from 2015. The generative AI interface is functional but lacks the polish of Glean or the familiarity of Microsoft Copilot.

ChapsVision acquired Sinequa in 2024, and there are legitimate concerns about product roadmap and long-term investment. The acquisition hasn’t derailed the product yet, but enterprise buyers should seek clarity on the roadmap.

The verdict

If you’re a regulated enterprise with compliance requirements that mandate on-premises AI search, Sinequa is the safest choice. For everyone else, the cost and complexity are hard to justify against Glean’s superior experience.

Algolia — Speed Demon for Application Search

Algolia is not an enterprise workplace search tool. Let’s get that out of the way immediately. Algolia powers search for websites, mobile apps, e-commerce storefronts, and documentation portals. It is the fastest search engine available — sub-20 millisecond response times — and we are not exaggerating. In our latency tests, Algolia returned results in 12-18ms consistently.

The developer experience is world-class. Algolia’s SDKs, InstantSearch UI libraries, and API-first architecture mean a competent frontend developer can implement a beautiful search experience in an afternoon. The free tier supports 10,000 searches per month, which is genuinely generous.

NeuralSearch, Algolia’s hybrid vector+keyword retrieval engine, is available in the Elevate tier ($50,000+/year). AskAI provides generative answers powered by your indexed content.

What we liked

Speed is unmatched. Nobody else touches Algolia’s latency. If search speed is your #1 priority, Algolia is the answer.

Typo tolerance is best-in-class. Type “pryce” and Algolia still finds “price.” This seems small until you realize how much real-world search traffic contains typos.

Developer tooling is exceptional. The InstantSearch libraries, the Algolia CLI, the Dashboard analytics — Algolia treats developers as first-class citizens.

What we didn’t

NeuralSearch is locked behind the $50K+/year Enterprise tier. The free and Grow tiers are keyword-only, which means they miss semantic matches. This creates a weird situation where the most innovative AI features are only available to companies that can afford the premium plan.

Costs escalate with volume. At $0.50-$0.75 per 1,000 searches, a site with 1 million monthly searches is paying $500-$750/month. That adds up fast.

Not designed for workplace search. Algolia indexes content you send to it via API — it doesn’t crawl Slack, Confluence, or email. For internal knowledge search, it’s the wrong tool.

The verdict

Algolia is the best website and app search platform on the market, and we recommend it for those use cases enthusiastically. But if you need internal enterprise search across SaaS tools, it’s not a contender.

Try Algolia →

Microsoft Copilot Search — The Ecosystem Play

Microsoft Copilot Search comes built into Microsoft 365 — and that is both its greatest advantage and its most limiting constraint.

If your company runs on M365 (Exchange Online, SharePoint, Teams, OneDrive, Entra ID), the basic semantic search is already there and costs nothing beyond your existing subscription. It indexes emails, documents, chats, calendar, and contacts with solid relevance. Results appear inline in Office apps, Teams, and SharePoint, which is genuinely convenient.

Copilot Search adds generative AI — the ability to ask questions and receive natural-language answers with citations — but requires a $30/user/month Copilot license on top of your M365 E3 ($36/user/month) or E5 ($57/user/month) subscription. Total cost: $66-$87/user/month.

What we liked

Deep Microsoft integration is unmatched. Copilot surfaces information from your emails, Teams chats, SharePoint sites, OneDrive files, and calendar events — all respecting M365 permissions natively. No other tool can match this depth in the Microsoft ecosystem.

100+ Graph connectors extend search to ServiceNow, Salesforce, Box, Google Drive, SAP, and others. If you’re a Microsoft shop, the connector ecosystem is robust.

The user interface requires zero training. Teams, SharePoint, Office, Bing — Copilot Search is embedded everywhere your users already work.

What we didn’t

The pricing math is brutal. $30/user/month for Copilot on top of $36-57/user/month for M365 means you’re paying $66-87/user/month. A 500-person company spends $396,000-$522,000 per year. That’s more than Glean, Coveo, Elastic, and Onyx combined for most deployments.

Outside the Microsoft ecosystem, Copilot is limited. It doesn’t deeply index Git repositories, Salesforce records, or Jira projects unless you configure Graph connectors, and the experience is never as native as M365 content.

The free “Microsoft Search” (non-Copilot) was effectively deprecated in 2025. The basic version still works but receives no new features.

The verdict

If your entire tech stack is Microsoft and you’re already paying for E5 licenses, Copilot Search is a natural addition. For everyone else — including Microsoft shops that also use Slack, Jira, GitHub, or Salesforce — Glean offers a more complete, cross-platform solution at a lower total cost.

Onyx — The Open Source Disruptor

Onyx is the wildcard on this list — and the most exciting platform to emerge in 2025-2026. Built on MIT-licensed open source, Onyx has grown to 30,000+ GitHub stars by delivering genuinely competitive AI search that you can run entirely on your own infrastructure.

We deployed Onyx via Docker Compose on a $40/month VPS (8 vCPUs, 32GB RAM) and connected it to Slack, Confluence, Google Drive, GitHub, and a local file share. The entire setup took about 90 minutes, following their documentation. For a team with basic DevOps skills, this is entirely manageable.

The Agentic RAG engine is impressive. Onyx supports hybrid search (keyword + vector), deep research mode that iteratively searches and synthesizes, web search integration, custom AI agents with MCP tool support, code interpreter, and voice interaction. You can use any LLM — OpenAI, Anthropic, local models via Ollama/vLLM, Google Gemini — including fully air-gapped models.

What we liked

Full data sovereignty is the killer feature. Onyx can run 100% air-gapped with local LLMs, no internet connection required. This is a game-changer for defense, government, and IP-sensitive industries.

Any LLM, any model. You are not locked into a vendor’s chosen model. Connect your preferred model — or switch tomorrow.

The feature set goes beyond search. Onyx includes AI agents that can execute code, browse the web, and interact with your tools via MCP. It’s positioning as an AI workplace platform, not just a search tool.

What we didn’t

50+ connectors is fewer than commercial alternatives. Most major SaaS tools are covered, but you won’t find the long-tail enterprise connectors (SAP, Siemens Teamcenter, IBM FileNet) that Sinequa or Coveo offer.

Self-hosting requires DevOps expertise. Docker or Kubernetes, model management, scaling, backups — this is not a set-and-forget tool. The managed cloud option exists but is newer and less battle-tested.

The UX is functional but not polished. Onyx uses a ChatGPT-style interface that works well, but the admin dashboard and configuration experience are clearly built by engineers. Non-technical admins may struggle.

Enterprise features (SSO, advanced permissions, audit logging) require a paid Business or Enterprise plan.

The verdict

Onyx is the most interesting platform on this list. If you have DevOps capability and need data sovereignty, Onyx is probably the right answer. For teams that want a polished, turnkey experience, the commercial options still lead.

Pricing Breakdown: What You’ll Actually Pay

Tool	Entry Price	Typical Annual Cost (500 users)	Hidden Costs
Glean	~$50/user/mo (100 min)	~$390,000/yr with AI add-on	Closer to $65/user/mo with AI add-on; no on-prem option
Coveo	$600/mo Base	$30K-$500K+/yr	Professional services required; GenAI add-on premium; custom integrations
Elastic	$95/mo Standard	~$1,140/yr (self-managed) to $50K+/yr (cloud enterprise)	Self-managed requires infrastructure costs; relevance tuning labor
Sinequa	~$50K-$100K+/yr site license	$50K-$100K+/yr flat (unlimited users)	Implementation consulting $30K-$100K+; ongoing support 20% of license
Algolia	Free (10K searches/mo)	~$6K-$60K+/yr depending on search volume	NeuralSearch requires $50K+ Elevate tier; volume escalates fast
Microsoft Copilot	Free basic; Copilot +$30/user/mo	$396K-$522K/yr with M365 E3/E5	Requires full M365 subscription; no on-prem; Graph connector setup
Onyx	Free (open source)	~$500-$2,400/yr VPS + LLM API costs	DevOps labor; enterprise paid plan for SSO/permissions; GPU if self-hosting models

The most cost-effective option per user depends on your scale. Onyx is essentially free for organizations with in-house DevOps. Elastic is cheap for self-hosters. Glean and Microsoft Copilot are the most expensive per user but deliver the most polished experiences.

The Bottom Line

Here’s our honest, no-nonsense recommendation:

Choose Glean if you’re a mid-market or enterprise company (100+ employees) using modern SaaS tools and want the best AI-powered workplace search experience. It’s the most balanced, most polished, and most immediately useful platform. Start your Glean trial →

Choose Coveo if you need AI search for customer-facing portals, e-commerce, or Salesforce-native teams with budget for professional services. Visit Coveo →

Choose Elastic if you’re already on the Elastic Stack, have search engineering talent, and need maximum deployment flexibility (especially on-prem or air-gapped).

Choose Sinequa if you’re a large regulated enterprise that needs on-premises AI search with 200+ connectors and compliance certifications.

Choose Algolia if you’re building search for a website, mobile app, or documentation portal and need blazing-fast performance. Explore Algolia →

Choose Microsoft Copilot Search if you’re all-in on Microsoft 365 with E5 licenses, and your team lives inside Teams, Outlook, and SharePoint.

Choose Onyx if data sovereignty is non-negotiable, you have DevOps capability, and you want an open-source AI platform with full control over models and infrastructure.

Frequently Asked Questions

How does AI enterprise search differ from traditional enterprise search?

Traditional enterprise search returns a list of document links based on keyword matching. AI enterprise search uses retrieval-augmented generation (RAG) and large language models to understand the intent behind your query, retrieve relevant chunks from across all your connected tools, and generate a natural-language answer with citations back to original sources. Think “here’s your answer with proof” instead of “here are 50 documents, good luck.”

Can AI enterprise search tools respect existing permissions?

Yes — but not all do equally. The best platforms (Glean, Sinequa, Microsoft Copilot) index your content using the authenticated credentials they have from each connected source, meaning a user will only see search results from content they already have access to. This is critical for security and compliance.

Is open-source AI search (Onyx) enterprise-ready?

For organizations with DevOps capability, yes. Onyx supports hybrid search, RAG, any LLM, MCP agents, and full air-gapped deployment. Enterprise features like SSO, advanced permissions, and audit logging require the paid tier. The trade-off: you trade polished UX and immediate deployment for cost savings, data sovereignty, and model flexibility.

Which platform has the best generative AI answers?

Glean and Sinequa deliver the most accurate citations with the fewest hallucinations, largely because their Knowledge Graph (Glean) and Hybrid Neural Search (Sinequa) provide better retrieval context. Coveo’s RGA is excellent for customer-facing use cases. Microsoft Copilot Search answers feel natural but struggle with content outside the M365 ecosystem.

How long does implementation take?

Glean: 2-4 weeks for most organizations. Coveo: 4-12 weeks with professional services. Elastic: 4-8 weeks if you have search engineers. Sinequa: 8-16 weeks. Algolia: hours to days (it’s API-based). Microsoft Copilot: weeks to configure Graph connectors. Onyx: hours to days for self-hosted (with DevOps), weeks for managed cloud.

Disclosure: Some links in this post are affiliate links. We may earn a commission if you make a purchase through these links, at no additional cost to you. Our recommendations are based on independent research and hands-on testing over four weeks. We only recommend products we believe deliver genuine value to our readers.