AI Spend Audit
A fast diagnostic to understand where your AI money is going and where savings may exist.
Best for teams that need a clear view of workload cost, quality, and migration options.
AI Spend Optimization
Lower AI cost without lowering the bar.
VeerOne evaluates production workloads, tests alternative routes against task-specific criteria, and designs fallback and rollback around the result.
Routing changes require evidence; lower token rates alone do not establish a better production route.
Routing decision notebook
Illustrative example — not a customer deployment
Support drafting
- Request enters the current premium route.
- Candidate route is evaluated before any traffic moves.
- Task
- Draft replies to recurring support requests from approved help-center material.
- What must be tested
- Accuracy against resolved tickets, tone and citation of approved sources, escalation behavior on missing information.
- Candidate route
- Lower-cost hosted API
- Fallback
- Low-confidence or sensitive drafts route to the current premium model or to a human agent.
- Rollback
- Quality score below the agreed threshold for two review cycles returns traffic to the current route.
What earns a route change
Five things must be true before traffic moves.
A route change is a release decision, not a settings tweak. Each requirement below has to hold for a specific request class before any production traffic moves.
Request class
The workload is named and bounded: what the requests look like, how many arrive, and what a good answer means for that class.
Evaluation requirement
A task-specific evaluation built from representative work, with an approved quality threshold the candidate route must clear.
Release decision
A named owner approves the route change against frozen evaluation evidence — not against a published-rate comparison.
Fallback
Low-confidence, sensitive, or failing requests keep a path to the current premium route or to a person.
Rollback
The conditions that return traffic to the previous route are written down before the release, with an owner who can execute them.
Candidate routes
Compare route categories, then verify the rates.
Route categories frame what an engagement tests. Published token rates are date-stamped with provider, tier, context, caching, and input/output mix before any comparison is used — and a rate difference alone is not an accepted-task cost reduction.
Premium hosted route
Stays in place for hard, sensitive, or unevaluated work. It is the fallback for every candidate route, not the target of removal.
Must be verified: Nothing to verify — this is the route already in production.
Lower-cost hosted API
First candidate for routine request classes that clear evaluation. Provider, tier, and dated token rates are verified before comparison.
Must be verified: Task-specific evaluation results; dated input/output token rates at the intended tier; latency under production load.
Open-weight or self-hosted deployment
Candidate where volume, privacy, or unit economics justify operating a deployment. Hosting and operations cost enter the scenario openly.
Must be verified: Evaluation results; hosting, operations, and engineering cost; scaling and latency behavior at production volume.
Rate comparison awaiting verification
No provider rate is shown here because rates change and this page cannot date-verify them. During an engagement, every rate claim is recorded with its retrieval date, tier, and assumptions before it informs a decision.
Scenario planner
Model the route, not the promise.
A transparent token-charge scenario: your current spend, the share eligible for a tested route, and an assumed rate difference on that share. It is not a savings guarantee, a service quote, or ROI.
Slider covers 10,000–500,000 USD as a convenience; the numeric field accepts 0–10,000,000 USD.
Illustrative starting values
Example only: 60,000 USD/month, 35% eligible, 75% assumed reduction. Not your spend, and never sent as a company fact.
Reconciled scenario
Illustrative example — not a customer deployment
Enter all three values to compute the scenario. Empty fields stay empty; invalid entries are flagged above.
Assumptions and exclusions
The change applies only to the eligible share of token charges: eligibleSpend = spend × share, then the assumed reduction applies to eligible spend. Not included, and able to change the outcome:
- Service fees, migration, and engineering work
- Hosting changes and evaluation cost
- Retries and fallback traffic
- Latency constraints and output/token-mix differences
Illustrative calculations are not forecasts. Actual savings depend on the traffic that passes your evals, current vendor rates, caching, retries, tool use, latency targets, engineering cost, and deployment constraints.
Method
Six steps from spend to an approved route.
The same ordered method for every engagement. Each step produces evidence the next one depends on.
- 1
Identify
Map where spend comes from across vendors, models, teams, prompts, volume, retries, latency, and cost.
- 2
Classify
Split production usage into task classes with different quality, latency, risk, and fallback needs.
- 3
Evaluate
Build task-specific evaluation sets and approved quality thresholds from representative real work.
- 4
Benchmark
Test candidate hosted, specialized, and open-weight routes against quality, cost, latency, and reliability.
- 5
Route
Move only approved workloads and keep a premium fallback for low confidence, failure, or quality drift.
- 6
Monitor
Track quality, accepted-task cost, retries, fallback, alerts, route changes, and rollback conditions.
Engagements
Choose the depth that matches the decision.
Engagements are scoped planning and implementation work. Outcomes depend on what the evaluations show; no engagement guarantees deployed results or a specific saving.
AI Cost Reduction Sprint
A complete 30 day engagement covering audit, evals, benchmarking, replacement recommendations, and routing architecture.
Best for teams ready to benchmark candidates and design a controlled routing plan.
AI Model Migration
Hands on implementation support for moving eligible workloads to lower-cost hosted models, open-weight models, or private deployments.
Best for production teams ready to reduce vendor costs.
AI Routing Layer
Design and implementation of a model gateway that dynamically routes requests by cost, quality, latency, and fallback requirements.
Best for teams using multiple AI models across products and workflows.
The 30-Day AI Cost Reduction Sprint
A scoped plan inside thirty days.
The 30-day sprint is a scoped plan: audit, evaluation build, benchmarking, and a routing design. Thirty days is the working window for the plan — production rollout follows only when the evidence supports it.
Audit
Review invoices, usage logs, prompts, workflows, vendors, and production constraints.
Evals
Build task specific evals from real workloads and define quality thresholds.
Benchmark
Test frontier, independent hosted, open-weight, and specialized models.
Routing Plan
Design the routing layer, migration path, modeled cost scenario, and rollout checklist.
Questions
What teams ask before a spend review.
Does lower spend mean lower quality?
Not by design. A workload only moves when a candidate route clears a task-specific evaluation built from your representative work. Anything that does not clear the threshold stays on the current route, and low-confidence traffic keeps a premium fallback.
How are the economics calculated?
Assumptions stay visible: current token spend, the share eligible for a tested route, and the assumed rate difference on that share. Service fees, migration, hosting, evaluation, engineering, retries, and fallback traffic are called out separately rather than hidden inside a savings figure.
What data do you need?
Usage and invoice data, representative requests per workload class, and access to the people who own quality for each class. Source access and retention boundaries are agreed before any evaluation work begins.
Do we have to leave our current providers?
No. The current route stays in place as the premium path and as the fallback for every candidate. The goal is the right route per workload with rollback you control — not provider replacement for its own sake.
Who owns the routing policy after the engagement?
Your team. The evaluation sets, thresholds, routing rules, fallback behavior, and rollback conditions are handed over with documentation so the policy stays under your control.
Start with the workloads, not the invoice.
A spend review maps where token charges come from, which request classes are candidates for a tested route, and what evidence a route change would need.