Over the past 18 months, we've watched enterprise AI spending quietly explode. The tools are powerful, the workflows are real, but the costs are catching up to the hype.
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According to industry surveys, companies are now spending anywhere from $100K to $400K per year on LLM tools like ChatGPT Enterprise, Microsoft Copilot, and Google Gemini. What started as a handful of pilot licenses is turning into a full line item on the IT budget.
The prices aren't static either. Gartner expects most enterprise software will increase at least 40% due to generative AI features baked into core products. This isn't a temporary spike—it's the new baseline.
So the question we've been getting more and more from companies—especially security-conscious ones in SaaS, fintech, and health tech—is: What happens when we stop renting AI and start owning it?
The True Cost of Enterprise AI
Most enterprise AI services have converged on a $30–$60 per user/month price point. Microsoft Copilot costs $30 per user, Google Workspace AI (Gemini) runs $30 per user, Claude Team sits at $30 per user, and ChatGPT Enterprise hovers around $60 per user with a 150-seat minimum.
That means even modest deployments cost six figures annually. ChatGPT Enterprise, for instance, reportedly starts at $108,000/year—and that's before usage expands across teams. Push it to 300–500 seats and you're easily spending $200K–$400K+ per year for a single AI assistant.
To be clear, the tools work. Many teams are seeing productivity boosts, faster research cycles, and better support outcomes. But the more AI gets integrated into daily operations, the more those per-user license fees scale out of proportion.
And that's the rub: you don't own any of it. Every interaction is metered, every insight leaves a data trail in someone else's cloud, and every new use case means either another tool or another tier.
The Quiet Economics of Hosting Your Own LLM
What most companies don't realize yet is that LLMs no longer require hyperscaler infrastructure to run effectively. Thanks to the explosion of open-source models like LLaMA 3, Mistral, and Mixtral, it's now entirely feasible to run a state-of-the-art LLM behind your firewall, connect it to your existing tools like CRM, Google Drive, Jira, and internal docs, build a secure, internal-only chatbot for your team, and do it all on a single GPU box or private cloud instance.
The numbers are striking. Hosting a 7B parameter LLM (like Mistral or LLaMA 3 8B) costs $850–$1,500/month in infrastructure. That gives you thousands of requests per day, sub-second latency, and zero external dependencies. The model is open source, the usage is unmetered, and the data never leaves your infrastructure.
Even scaling up to a 13B or 70B model brings you to $2K–$5K/month in total operating costs—still significantly lower than the ongoing license and API fees from cloud LLM vendors. And critically, cost doesn't scale linearly with usage. Once deployed, a self-hosted LLM serves as many requests as your hardware and tuning allow. No token taxes, no seat limits, no vendor gating.
If your company is spending more than $50K/year on LLMs, it's worth running the math. Because unlike SaaS licenses, infrastructure gets cheaper over time.
But It's Not Just About Cost
For many teams, the bigger driver is control. Every CIO or CTO we talk to is starting to ask the same questions: How do we guarantee that no PII is sent to public APIs? What if OpenAI changes its pricing again next quarter? What's our fallback plan if we lose access to the provider?
It's not just security teams pushing back anymore. It's finance, legal, and procurement.
Self-hosting changes the equation. You own the models, you set the policies, you decide where the logs live and what gets stored. You define who can do what—by role, by group, by access scope.
It also opens up integration in a way SaaS platforms can't. We've worked with companies who use a self-hosted LLM to summarize support tickets before escalation, extract insights from sales calls automatically, generate product briefs from Notion content, run embedded QA against engineering documentation, and automate reporting across disconnected internal systems. All from a single chat interface—running locally.
What We're Building at Magnetiz
Because this shift is already happening, we're now offering Private LLM System Integration as part of our platform. Here's what that means: We install a fully containerized, enterprise-ready LLM stack on your infrastructure (on-prem or private cloud). It includes vLLM inference, Keycloak for SSO and RBAC, Qdrant or Postgres for vector search, OpenWebUI, and observability via Prometheus + Grafana. Your team gets a private, secure, OpenAI-compatible endpoint that can integrate with any tool you already use. No data leaves your environment. No token costs. No SaaS limitations.
We'll configure it, deploy it, monitor it, and support it. You end up with a chat-ready, automation-capable, secure-by-default LLM system—purpose-built for your workflows.
Is It Time to Own Your AI?
Here's what we're seeing. Companies are happy to rent AI—until they realize they're paying premium prices for limited control. Once they cross a certain usage threshold, they start asking: What if we brought this in-house?
If you're spending over $50K/year on LLM tools—or if your security team is blocking broader adoption—this is worth a conversation. We're not anti-SaaS. But we are pro-alignment. And in many cases, a private LLM stack delivers better economics, better security, and better integration than relying on external APIs.
If you want to explore what that looks like for your team, reach out. We'll help you run the math.
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