Enterprise AI & Agentic AI
Should we build or buy enterprise AI?
Buy the commodity layer — models, CCaaS, orchestration platforms — and build only what encodes your differentiation: retrieval over your content, integrations with your systems of record, and evaluation of your outcomes. The real comparison is not licence versus engineering; it is total cost of ownership including integration, evaluation and the run state on both sides.
Last reviewed 2026-08-31 · pronix.ai
Key takeaways
- Differentiation testIf a capability would look identical at a competitor, buy it. If it depends on your data, policies or workflow, build and own it.
- Exit cost belongs in the modelVendor lock-in shows up as migration effort later; portability of prompts, retrieval indexes and evaluation sets keeps the option open.
- Hybrid is the common answerMost enterprises buy the platform and build the retrieval, integration and evaluation layer on top — which is where the measurable advantage sits.
What the numbers show
First-party figures from Pronix research. Each links to the report or playbook that publishes it.
- 25–35%
- Foundation-model tokens account for 25% to 35% of total cost of ownership for a mature enterprise LLM workload.Source: Enterprise LLM Cost & TCO Benchmarks 2026 →
- 60–75%
- Cheap-first cascade routing — attempt the cheapest capable model, escalate on a confidence check — moves 60% to 75% of traffic to the cheap tier with no measurable quality regression.Source: Enterprise LLM Cost & TCO Benchmarks 2026 →
- 31%
- Retrieval, integration and evaluation infrastructure is now the single largest line in the enterprise AI budget at roughly 31% of spend.Source: State of Agentic AI in the Enterprise 2026 →
External references
- ISO/IEC — ISO/IEC 42001 — AI management systems (2023)The certifiable management-system standard enterprise procurement increasingly asks about.