Production AI economics
Method
How decost.ai measures production AI costs, evaluates alternatives, and distinguishes modeled savings from verified results.
01 / Scope
The method starts with an existing production workload.
decost.ai reviews model APIs, agent workflows, inference infrastructure, and the operating work attached to them. The method addresses the economics of production systems. It does not cover general AI strategy, staff augmentation, or employee software-seat management.
02 / Evidence levels
Each claim carries an evidence label.
- Observed
- A source file, invoice, export, trace, evaluation, or production measurement directly supports the statement.
- Inferred
- Available evidence supports a hypothesis that still requires production measurement.
- Modeled
- A calculation uses stated inputs and assumptions to project cost, benefit, or payback.
- Verified
- A normalized production comparison measured the result over a stated observation period.
03 / Review sequence
Baseline, constrain, change, and compare.
- Baseline.Match invoices and usage to one production workload and a defined outcome.
- Constrain.Set required quality, latency, reliability, security, and operating thresholds.
- Change.Implement one bounded adjustment with fallback and rollback behavior.
- Compare.Normalize volume, provider prices, workload mix, and quality before calculating savings.
04 / Cost model
Measure the cost of a successful outcome.
Operating cost includes provider usage, retries, fallbacks, supporting infrastructure, human review, and recurring operating labor affected by the decision. The denominator counts outcomes that meet a defined success rule. Implementation expense appears as a separate investment in payback and first-year net-benefit calculations.
05 / Alternatives
The current system remains part of the comparison.
A review can recommend keeping the current path, optimizing it, routing work by difficulty, or migrating a bounded workload. Commercial APIs, smaller hosted models, local models, and hybrid systems compete against the same workload evidence and operating constraints.
06 / Savings verification
Raw bill changes do not establish savings.
The comparison separates measured spending, modeled operating savings, implementation expense, cost avoidance, and verified savings. Verification uses comparable workload segments and observation periods. It accounts for changes in traffic, price, workload mix, quality, latency, reliability, review effort, and fixed commitments.
07 / Vendor independence
decost.ai is vendor-neutral.
decost.ai is paid by its clients, not by the vendors it evaluates. It does not receive vendor compensation or referral fees, act as a vendor-paid reseller or implementation partner, or hold investments relevant to its recommendations. Public tools contain no sponsored rankings or paid placements.
The method does not presume a model purchase, migration, or infrastructure project. Keeping the current system remains a valid recommendation. If a relevant commercial interest arises in the future, decost.ai will disclose it before the affected client acts on a recommendation.
08 / Public tool limits
Public tools create baselines and experiment plans.
Defaults and examples are illustrative. The calculators do not verify provider prices, benchmark workload quality, inspect live systems, or prove production savings. Use their output to identify missing evidence and design a test. Review each tool's assumptions and limitations beside its inputs and results.
Open the production AI cost tools or read the supporting guides.