You're Burning Money on AI Tokens. Here's Why.
Picture this. Your company just rolled out Claude Enterprise. Excitement is high. People start asking it questions about internal projects, sales data, customer feedback. It works, sort of. But then the invoice arrives. Token costs are through the roof. And half the answers feel... off. Generic. Like the AI doesn't actually know your company.
Sound familiar? You're not alone. This is the growing pain every enterprise hits when they bolt a powerful AI model onto their existing tools and hope for the best.
The real problem isn't the model. It's the plumbing underneath.
The Context Gap Nobody Talks About
Here's something most people don't realize when they start using AI at work. The model itself is only half the equation. The other half? Context. The quality, depth, and structure of information you feed it.
Think of it like hiring a brilliant consultant. Give them access to your full filing cabinet, org chart, and project history, and they'll blow you away. Give them a random pile of disconnected documents, and they'll give you surface-level answers that miss the point.
That's the fundamental difference between how Glean and Claude Enterprise approach enterprise AI. One builds a knowledge layer first. The other asks the model to figure it out in real time.
How Glean Builds a Knowledge Layer (And Why It Matters)
Glean connects to over 275 enterprise apps. Slack, Google Drive, Salesforce, Jira, Notion, Confluence, you name it. But it doesn't just pass data through. It creates a unified index that understands relationships between people, content, and activity across your entire organization.
So when someone asks, "What's the status of the Henderson deal?", Glean doesn't just search for documents with "Henderson" in the title. It knows who worked on that deal. It pulls the latest Salesforce updates, the relevant Slack threads, the proposal doc, and the email from last Tuesday. It connects the dots because the dots are already connected in its knowledge graph.
Claude Enterprise takes a different path. It uses MCP connectors to search across systems in real time. No persistent index. No pre-built understanding of your org. The model has to figure out what's relevant each time you ask, burning tokens as it goes.
The numbers tell the story:
- Glean results get preferred 2.5x more often than off-the-shelf MCP approaches
- Token consumption drops by 23% when Glean handles retrieval
- On complex enterprise tasks, Glean wins 73% of the time
That 23% token reduction might sound modest until you multiply it across thousands of daily queries. At enterprise scale, that's real money.
The Specialized Model Advantage
Here's where it gets interesting.
Glean built a small, specialized model called Waldo. It's not trying to write poetry or explain quantum physics. It's designed for one thing: agentic search and adaptive reasoning in enterprise environments.
Why does that matter? Because using a massive general-purpose model for every search query is like hiring a Michelin-star chef to make your morning toast. It works, but it's expensive and slow.
Waldo handles the retrieval and routing layer, reducing token consumption by 25% and latency by 50% for typical enterprise work. The big model (Claude, GPT, or Gemini) only steps in when you actually need deep reasoning or content generation.
Claude Enterprise doesn't have this routing layer. Every query goes straight to the full model. For simple lookups, that's overkill. For your budget, it's painful.
Security: Where "Good Enough" Isn't Good Enough
Let's talk about the thing that keeps CISOs up at night.
When an AI model accesses your company data, who can see what? This isn't a theoretical question. If someone in marketing asks about a confidential M&A document, does the AI know they shouldn't see it?
Glean enforces permissions at retrieval time. Before the model ever touches the data, Glean checks whether that specific user has access to that specific document. The sensitive data never reaches unauthorized eyes. It's not a filter on the output. It's a gate on the input.
Claude Enterprise uses OAuth, which typically means permissions are enforced at the data-source level. You might have access to the entire Salesforce instance or the whole Google Drive, but object-level permissions? That depends on how the MCP connector is configured, and the granularity varies.
For a 50-person startup, this difference is academic. For a bank or a healthcare company? It's a compliance requirement.
Deployment Options: One Size Doesn't Fit All
Where your AI lives matters. A lot.
Glean offers single-tenant deployment by default. You can run it as SaaS or in your own VPC. Your security team chooses where data lives, how it's isolated, and what compliance boundaries exist.
Claude Enterprise is multi-tenant SaaS only. There's no VPC option. For companies in regulated industries, finance, healthcare, government, this can be a dealbreaker. Not because Claude is insecure, but because their compliance frameworks require specific deployment architectures that Claude simply doesn't offer.
The Audit Trail Problem
Here's one that often gets overlooked until it's too late.
Your compliance team needs to know what data the AI accessed, who asked for it, and what it returned. Glean provides centralized audit logs and compliance controls across all interactions. IT and legal get the visibility they need without building custom monitoring from scratch.
Claude's Cowork activity sits outside its audit logs and compliance APIs. That means a chunk of AI-driven activity in your organization becomes invisible to your governance tools. For some companies, that's fine. For others, it's a non-starter.
Model Lock-In: A Real Risk in a Fast-Moving Market
One year ago, most people hadn't heard of Claude. Today, it's one of the top models available. But what about next year? Or the year after?
Glean lets you use Claude, GPT, or Gemini. If a better model appears, you switch. No migration, no re-platforming, no vendor drama. The knowledge layer stays the same. Only the reasoning engine changes.
Claude Enterprise, predictably, locks you into Anthropic's models. That's not a criticism. It's their product. But it means your AI strategy is tied to one company's roadmap.
In a market where the leaderboard reshuffles every six months, that's a bet most CTOs would rather not make.
So Who Should Choose What?
Claude Enterprise is a solid choice for teams that want a powerful, general-purpose AI with a clean interface. If you're a smaller org without strict compliance needs, and you're mostly using AI for individual productivity, it works well.
Glean makes more sense when:
- You need AI that understands your entire organization, not just individual queries
- Token costs matter because you're running thousands of queries daily
- Security and compliance are non-negotiable requirements
- You want the flexibility to swap models as the market evolves
- You're building agents
