Why Enterprise AI Costs Are Spiralling & How Sovereign AI Fixes the Economics
Blog post from deepset
In 2026, enterprises face a new challenge with AI: managing escalating costs due to token-based pricing models, as demonstrated by Uber and Microsoft, which have both struggled with budget overruns and license cancellations. The shift from flat-rate to consumption-based billing has made AI costs a board-level concern, compounded by agentic workflows that multiply token usage beyond initial estimates. Enterprises now recognize the importance of sovereign AI infrastructure to regain cost control, allowing them to manage AI expenses by owning the orchestration, context, and deployment layers. This approach provides flexibility to optimize model usage, reduce context bloat, and enforce governance, thus transforming AI cost management from a reactive budget exercise to a proactive engineering discipline. The key to sustainable AI lies in developing sovereign systems that offer transparency and adaptability in response to evolving market conditions, ensuring economic control alongside data and compliance sovereignty.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| AI Agents | 9 | 6,005 | 1,359 | 264 | +22% |
| Observability | 6 | 4,166 | 768 | 194 | +22% |
| LLM | 5 | 6,196 | 1,155 | 243 | -32% |
| RAG | 3 | 1,000 | 260 | 106 | -52% |
| AI Coding Assistant | 1 | 2,151 | 535 | 165 | +20% |
| Multi-agent systems | 1 | 532 | 166 | 79 | -3% |
| OpenTelemetry | 1 | 967 | 177 | 57 | +2% |
| Vector Search | 1 | 1,895 | 382 | 133 | -16% |
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