Optimize AI economics as adoption scales
As AI use grows, the most capable model is not always the right model for every workload. Matching workloads to the right intelligence can improve unit economics, reduce unnecessary spend and create more room to scale AI where it delivers the greatest value.
We help identify where frontier capability is worth the premium, where lower-cost alternatives are sufficient and how to route workloads accordingly.
Retain flexibility as the AI landscape evolves
Model performance, pricing and availability are changing faster than traditional enterprise planning cycles. A hybrid AI estate gives you the flexibility to choose across frontier, open and specialist models as your needs evolve, without locking your future architecture to today’s choices.
We help design the roadmap, architecture and operating model needed to retain choice and adapt as technologies, workloads and economics change.
Own the learning that builds advantage
Enterprise AI use generates a valuable learning loop through data, feedback, evaluations and context. Retaining ownership and governance of that learning helps intelligence compound inside your organization, building proprietary capability that becomes more valuable over time.
We help protect the learning loop, reduce unnecessary dependency and turn enterprise knowledge into proprietary intelligence that strengthens your advantage.
Talk to us about a strategic enterprise AI estate
Give us a snapshot of your organization’s AI use, scale and priorities so we can come to the conversation with the right context.
AI economics starts with architecture
As enterprise AI scales, its economics increasingly become an architecture issue. The models organizations choose, how workloads are routed, how context is managed and how agents are designed all shape the economics.
The opportunity is to move these decisions upstream, matching the right intelligence to the right workload and designing an architecture that can adapt as model performance, pricing and infrastructure evolve.
| $37b | spent on enterprise GenAI in 2025 — 22x growth in just two years. The bill arrived before the ROI. |
| 63% | of enterprise organizations are already using open source models as their AI estates diversify. |
| 96.6% | cost reduction with open models vs. frontier models on non-frontier tasks. |
How enterprise AI estates evolve at scale
Stage 0
Rent the frontier
Most organizations start here, using premium AI models to accelerate experimentation and prove value. However, as adoption grows, costs can rise quickly because every workload uses the most expensive intelligence.
Best for: Early AI adoption and experimentation
Stage 1
Rent alternative intelligence
Leading organizations reduce AI costs by matching each workload to the right model. Routine tasks often achieve the same outcomes with lower-cost models, without sacrificing quality.
Best for: Scaling AI while controlling costs
Stage 2
Rent the hardware
As AI usage grows, infrastructure becomes the next opportunity for savings. Running models on dedicated GPU infrastructure lowers costs further while increasing flexibility and performance.
Best for: High-volume enterprise AI
Stage 3
Own the hardware
Organizations with AI at the core of their business gain maximum control, predictable economics and full data sovereignty by owning their AI infrastructure. The tradeoff is greater operational responsibility.
Best for: Mission-critical AI platforms
~80% of companies sit at Stage 0, with some of the strongest near-term opportunities to improve AI economics at Stage 1 and Stage 2.
