When cheaper isn't always better: comparing consumer and enterprise AI

In June, Google One ranked among the fastest-growing software vendors on Ramp. This piqued our interest: Google One is a consumer AI product, but it gained an increasing share of business spend.

This finding appears as overall AI adoption among U.S. businesses appears to be plateauing, according to the Ramp AI Index. Together, these two datasets may indicate that instead of adopting enterprise AI solutions, companies may be relying on individual (or free) plans to meet their teams’ needs.

The data raises important questions for finance and business leaders. How should companies implement AI across their organizations? Should organizations encourage the use of free AI tools, reimburse employees for their personal plans, or invest in enterprise platforms?

Here’s a closer look at the pros and cons of each option, and our thoughts on which route may offer the best long-term value and compliance.

Free AI: zero cost, but fewer benefits and greater risks

Free AI tools are an easy entry point. Teams can generate first drafts and summarize documents without touching the technology budget. For early experimentation or small teams, this might feel like a smart, lean way to leverage AI.

But free tools come with strict rate limiting, less advanced models, and fewer features—which means your team isn’t using AI to its full potential. Plus, these limits can make it nearly impossible to track adoption or ensure consistent use across teams.

Key concerns

A major concern with non-enterprise plans is data privacy. Consumer-grade plans may use your inputs to help train the LLM, and they aren’t compliant with data privacy frameworks like SOC 2.

In OpenAI’s ChatGPT, users can opt out of data collection, whereas in Anthropic’s Claude, data is saved if users provide feedback on outputs. But because non-enterprise plans don’t have centralized admin controls, you can’t verify if your employees are using these settings appropriately. This can make your company’s data vulnerable.

If employees use personal email addresses on their accounts, compliance becomes an even greater concern. The line between company and personal use can become muddled, which is problematic when IP is involved. And if an employee leaves the firm, they may still have access to proprietary company information.

Pros

• Free
• Quick and easy to get started

Cons

• No centralized admin controls
• Doesn’t adhere to data privacy standards (e.g., SOC 2)
• Often limited to basic models (e.g., GPT-3.5 or Claude 3 Sonnet)
• Usage caps on daily or hourly interactions
• No advanced features like custom GPTs, file uploads, or APIs

Premium consumer AI: a step up, but equally risky and hard to track

The growth in Google One spend on Ramp highlights how organizations may be reimbursing employees for consumer AI subscriptions instead of using free or enterprise tools. These plans offer access to more powerful models (albeit with rate limits that prohibit heavy usage) without requiring a full enterprise rollout. For teams that use AI to generate marketing materials or conduct research, that might be enough.

Yet from a financial perspective, this can be messy. With no central billing, oversight becomes difficult, which makes budget forecasting more challenging. Additionally, different teams may end up using different models, resulting in inconsistent outputs.

Key concerns

The same data privacy, security, and compliance risks that apply to free plans are a concern here as well. Like free plans, premium AI plans aren’t recommended for tasks involving sensitive information—even if you trust your employees’ judgment.

Pros

• Cheaper than enterprise
• Access to advanced models (e.g., GPT-4-turbo or Claude 3 Opus and Haiku)
• File upload support (e.g., PDFs, spreadsheets)
• Access to code interpreter and advanced data analysis tools
• Personalized memory
• Custom chat organization

Cons

• No centralized admin controls
• Doesn’t adhere to data privacy standards (e.g., SOC 2)
• Rate limits on advanced models
• No collaborative spaces, custom GPTs, or APIs
• Individually managed data governance

Enterprise AI: higher cost, but better for compliance, productivity, and spend management

Enterprise AI platforms (like ChatGPT Team or Gemini for Workspace) can offer technical and strategic benefits. These plans deliver unified access to higher-performing models, better privacy assurances, and helpful collaboration features that lower-tier plans don’t. They’re often the best fit for forward-thinking businesses that are serious about their AI adoption efforts.

Key concerns

Enterprise plans from leading LLM providers typically have built-in data privacy. They comply with major data privacy frameworks and never use your data to train models. Admin controls allow for the strongest data governance practices, and you can remove users immediately after employee departures.

Plus, enterprise licensing is managed centrally by IT and procurement rather than by individuals or managers. This makes it easier to track spend, eliminates unauthorized employee purchases, and enables secure scaling.

Pros

• Higher limits and better uptime on the most advanced models
• Admin controls for user provisioning, access, and billing
• Compliance with data privacy standards (e.g., SOC 2, ISO 27001)
• Data is excluded from model training by default
• Model customization and organization-wide prompt libraries and templates
• Centralized audit logs and usage analytics
• Domain-level collaboration (e.g., shared chats or docs across company accounts)
• SSO integration and enterprise-grade security settings

Cons

• Higher upfront cost
• Procurement and licensing management required

The plateau in overall adoption as shown by the Ramp AI Index doesn’t mean people are losing interest in AI, but it could mean companies aren’t scaling their enterprise investments—and that decision could hurt them in the long run.

Follow the Ramp Economics Lab for the latest proprietary AI spend data and what it suggests.