For most CIOs, deciding whether to buy or build GenAI solutions is one of the most important technology choices of the decade. The stakes are high. Move too slowly and risk falling behind; move too fast on the wrong path and costs can balloon with little payback. As pilots mature into production systems, the questions around risks, staffing, privacy, integration, and business value become sharper.

Why not default to just buying or building?

Newer models like OpenAI’s GPT-5 and GPT-4o, Anthropic’s Claude 4 and Claude 3.5 Sonnet, Google’s Gemini 2 and 1.5 Flash, and Meta’s Llama 4 and 3.1 all offer strong out-of-the-box performance. Buying access to platforms built on these models helps companies start pilots quickly and manage risk. For most internal copilots or digital assistants, beginning with a buy-first approach is common. For a practical guide to comparing private cloud, SaaS, and open-weight LLM options, plus key differences in compliance and results, read our deep dive on privacy-first GenAI adoption.

But growth brings new demands:

- Stricter privacy and compliance, especially in finance, health, or law

- Need to deeply connect GenAI with legacy data and systems

- Demand for control over cost and ongoing support

- Pressure for unique fine-tuning and domain accuracy

At this point, most CIOs realize the choice isn’t binary. Blending “buy for speed” and “build for control” is the new standard.

What do CIOs actually compare?

Key questions:

- Can a SaaS tool support custom business logic and integrations?

- Is the data sensitive or regulated, pushing toward private or on-prem hosting?

- Will IT or engineering have time and people to own ongoing improvements?

- How likely is the business to want to swap the underlying model in the future?

What leading enterprises do

Most take a stepwise approach:

- Run quick-buy-based pilots using proven vendors and public models

- Validate real-world user needs using analytics, not just technical logs

- Scale with “hybrid” tactics: use SaaS for non-critical functions, build for competitive secrets or compliance

- Add open-source models (like Llama 3.1) for R&D and optionality

- Use analytics tools like Nebuly to track adoption, satisfaction, and friction

A closer look at model choice

Here is how CIOs rate some of the best-known models for enterprise context:

(Model numbers shown for both current and previous widely adopted versions. Choose based on what is available and supported for your business context.)

Why user analytics matter more in 2025

Buying or building means nothing without real adoption. Technical success does not guarantee employee or customer engagement. User analytics show:

- Which workflows actually get adopted

- Where failures, friction, or drop-off happen

- Which departments unlock real business value (and which need a change)

Nebuly provides these answers, plugging gaps that simple system monitoring misses. Buying gives benchmarks. Building brings control. Analytics show what to fix, change, or scale in either case.

Takeaways for CIOs

- Buy tools for speed at the pilot stage, then let analytics highlight where deeper build investment will pay off

- Be ready to switch models and platforms as the field matures—avoid vendor lock-in

- Measure real user experience and ROI from day one

If you want to see how leading CIOs use analytics to guide buy-versus-build decisions, book a Nebuly demo.