OVHcloud

Frontier model pricing is falling fast enough that the infrastructure layer, not the model itself, now determines whether an AI retainer holds its margin. Agencies that route client work through a gateway with caching, fallbacks, and per-client cost telemetry can reprice deliverables every quarter; agencies hard-wired to one provider absorb every price move and outage as a margin event. The strategic work is orchestration, not model selection.

Concentration Risk Ledger is a framework for tracking how much of an agency's delivery capacity depends on any single model provider, region, or price tier. The unit of analysis is not the vendor relationship but the retainer: for each client engagement, list which workflows break if one provider raises prices, degrades quality, or restricts access. Forrester warned in October 2026 that AI supply chains hide single points of failure in plain sight, and the same week Anthropic cut Claude Haiku 5.5 to $0.10 per million input tokens while OpenAI shipped GPT-6 to 1.2 billion weekly users, both reminders that pricing and capability floors move fast. An agency running every client summarization job through one API has an unpriced liability. The ledger converts that into a number: percentage of monthly delivery hours exposed, and the cost of a routing layer that reduces it.

Inference Cost Floor is the practice of tracking the lowest available price per million tokens for a capability tier, then treating every drop as a trigger to re-price client retainers rather than a windfall to bank. Agencies that price AI work on today's model economics get undercut the moment a cheaper tier ships, because the client's procurement team reads the same launch posts. Anthropic's Claude Haiku 5.5 arrived at $0.10 per million input tokens with a 1 million token context window, which resets what high-volume document summarization and campaign analysis should cost a client. The framework has three moves: benchmark your current blended cost per deliverable, set a review cadence tied to model releases, and pre-agree with clients that savings split rather than vanish. Agencies running fixed-fee AI retainers without a floor review are quietly donating margin every quarter.

Model Substitution Window treats every frontier model dependency as a timed option, not a permanent commitment. The framework holds that the value of a multi-model orchestration layer is realized only when a provider's pricing or capability shifts, and that shift is the moment an agency can renegotiate scope. Anthropic's Claude Haiku 5.5 arrived at $0.10 per million input tokens with a 1 million token context window, a roughly 90% cut against prior small-model pricing, which resets the cost baseline for high-volume client work like document summarization and campaign analysis. Agencies that abstracted model calls behind a gateway can pass that saving into margin or into a lower retainer bid within days. Agencies that hardcoded one vendor absorb the change on the client's timeline instead of their own. The window closes when the next contract or statement of work is signed.

Treat provider portability as a delivery requirement: put a gateway or routing layer in front of every model call, and price the migration path into the retainer before the first token is billed.

Map every recurring client task to a model tier, then route through a gateway so a price cut or model swap is a config change rather than a rebuild.

IF your agency runs more than two client AI workloads in production and any single provider exceeds roughly 40% of inference spend, THEN build a routing layer that abstracts model calls behind one interface. IF client work is confined to one deliverable type, one model family, and under $2,000 monthly inference, THEN integrate the provider API directly and revisit the decision when either number doubles.

A productized engagement that puts a routing and failover layer between client applications and frontier model providers, so agencies can swap models on price or capability shifts without rewriting delivery code. The offer converts a one-provider dependency into a governed, observable, multi-vendor stack the client keeps paying a retainer to maintain.