The hidden cost of using MCP for context for enterprises

The AI bills conversation is taking over enterprise boardrooms.

Microsoft’s CEO, Satya Nadella, described the next phase of AI in terms that had nothing to do with capability: cheaper, more controllable, more trusted. Nadella’s not the only one. More and more believe cheaper, swappable, widely distributed models would answer the soaring costs problem.

You can already see this playing out. AI leaders debate whether ChatGPT or Copilot will be more cost-effective for enterprise data than Claude, despite Claude leading enterprise adoption over the past year. To me, that's another sign that model capability is no longer the real differentiator. Context is what will save you twice. It'll save you money, and it'll save you from AI you can't trust.

Many teams try to solve this by wiring an agent to a dozen MCP servers so it can access every system independently. The ability to access rich metadata from data platforms via MCP is a big step forward. Tools like Snowflake Horizon and DatabricksUnity expose metadata such as lineage and usage within their own platforms. On paper, that sounds like complete context. In practice, it isn't. They’re inherently siloed. Each one understands only its own system. And even if it were, the runtime costs would quickly become unreasonable.

Enterprises are never single-platform. It’s common to see multiple warehouses, multiple BI tools, different ETL/ELT tools, observability systems, and security platforms running side by side. So enterprise context is cross-platform by nature:

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Enterprise context is naturally distributed:

• Business definitions live in transformation and BI tools
• Usage signals come from query logs + dashboard impressions
• Security classifications live in DSPM platforms
• Lineage spans the entire stack

If an agent has to connect to every MCP independently at query time, it must reconcile conflicting definitions, stitch together lineage across systems, and fit everything into an ever-growing context window at runtime. That quickly becomes slow, super expensive in tokens, and inaccurate. Agents shouldn't have to reconcile raw enterprise metadata across MCP endpoints themselves. They should query a single, structured context graph that has already done the hard work and get exactly the context they need.

That's exactly what Euno does.

We continuously capture, reconcile, and auto-label enterprise context ahead of time, so every AI call retrieves only the context that matters. Each context call is more accurate and token-efficient, enabling enterprise-grade reasoning instead of siloed system-level responses. The result is way fewer tokens, faster reasoning, lower costs, and trustworthy answers. It also aligns perfectly with the future Nadella hinted at. If models become swappable, context becomes the durable layer.

The multi-platform MCP world is only going to expand. Claude, ChatGPT, Gemini, Copilot, Glean, Snowflake Intelligence, Databricks Genie... they all become interchangeable consumers of the same trusted enterprise context. And yes, the accuracy improvements come with it.

Context saves you money: It gives AI the right context at runtime with far fewer tokens, so it spends less time searching and more time answering correctly. So perhaps the AI bills conversation shouldn't be about which agent or model you'll choose.

It's a context question.