AI Cost Optimization for Production Workloads

About

We start with the invoice. We calculate the production cost of a successful outcome, then change workloads when the economics justify the work.

Protecting your margin
as AI usage scales

Stop cost growth
before it tracks revenue

Usage-based inference costs rise with traffic. Without active controls, a successful feature can consume a growing share of infrastructure spend.

Protect output
quality under change

We test each routing or model change against your evaluation set. Your quality floor determines whether the change ships.

Own the system
after the engagement

Your team keeps the evaluation sets, routing policy, and runbooks. You can operate the system after the engagement ends.

We test each recommendation against the same requirements

4 checks

Cost · quality · latency · reliability

We may recommend one of four paths

01Keep the current system
02Optimize the existing path
03Route work by difficulty
04Migrate a bounded workload

Changing nothing remains a valid recommendation.

Cost per successful outcome

One bounded change
measured against the baseline

We join invoices, usage exports, and traces to task-specific quality results. Each recommendation includes its expected cost and benefit.

Baseline the billProduction AI Economics Diagnostic Benchmark the optionsModel + Infrastructure Assessment Ship one changeOptimization Pilot

Engagement fit

A focused diagnostic for teams with a production decision to make.

The starting engagement is designed for a defined production question, not a general exploration of AI. Spend alone does not determine fit.

A useful fit

Evidence and a decision owner are available.

A production workload or a bounded launch with measurable usage
A named cost, model, routing, or infrastructure decision
Required quality, latency, reliability, and security constraints
An owner who can provide billing, usage, and evaluation evidence

Not the starting offer

Open-ended strategy and resourcing work.

General AI strategy or pre-product ideation
Staff augmentation or ongoing managed operations
Employee AI subscriptions and software-seat procurement
A vendor selection made without representative workload evidence

Primary engagement

Production AI Economics Diagnostic

Baseline the cost, quality, latency, and operating burden of up to three production workloads.

Scope: Up to three production workloads and one decision frame: keep, optimize, route, or migrate.
Typical schedule: Two to three weeks after evidence access. The written proposal confirms the actual schedule.
Inputs: Invoices, usage exports, architecture and call-path context, representative traces, outcome definitions, evaluations, and operating effort. Detailed transfer starts only after an agreement and data plan.
Decision: An evidence-backed recommendation to keep the current system, optimize it, route work by difficulty, or migrate a bounded workload.
Exclusions: Production implementation, broad application redesign, security or compliance certification, procurement, legal advice, and ongoing operations.
Commercial terms: The fit conversation is free. A written proposal sets a fixed scope and fee; no public price is represented before the scope is known.

Decision package

Cost per successful outcome
Spend and workload attribution
Prioritized quick wins
Keep, optimize, route, or migrate decision
Executive decision package

Optional follow-on

A model and infrastructure assessment, one bounded optimization pilot, or team handoff can follow under a separate scope. None is required to receive the diagnostic recommendation.

Check engagement fit

Method

Your team can inspect, operate, and repeat the decision process.

Baseline

Match invoices and usage to one production outcome.

Constrain

Set the quality, latency, reliability, and operating requirements.

Change

Implement one bounded adjustment with fallback and rollback behavior.

Compare

Normalize volume, prices, workload mix, and quality before calling a difference savings.

We publish client examples and performance figures with documented permission and enough context to explain the baseline, change, and observation period.

Founder

Brad Smith

Brad Smith is a staff engineer with 15 years of experience building high-visibility web, mobile, and AI products at startups from seed to post-funding, including companies raising $20M+ rounds.

He started decost.ai because he believes frontier AI is sold below its real cost. Venture money covers the difference today. When it runs out, prices will rise to what the models and data centers cost to run. decost.ai helps teams cut their AI spend before that happens.

Start with the bill

The free 30-minute fit conversation confirms the production decision, workload scope, evidence available, and whether a two-to-three-week diagnostic is appropriate. It is not a free architecture review. If there is a fit, a written proposal sets the scope, schedule, and fixed fee before detailed evidence changes hands.

We take on two to three engagements per quarter.