01 / CI/CD
Pre-deploy cost checks
Surface projected monthly impact when a model route, token ceiling, retry policy, or fallback chain changes in a service release.
Platform
Decision control, not another telemetry view.
Argmin resolves who used which model, from which service, under whose budget, and what should happen next before the expensive route becomes the default route.
- Variance
- The same AI model can cost up to 700x more depending on how it is used.
- Chain
- Model -> service -> code -> identity -> org -> budget.
- Posture
- Read-only connectors, advisory by default, and inside-your-environment deployment.
Comparison
Why "we already have Datadog" is the wrong answer.
If you already have telemetry and cost views, the real question is whether they tell you who owns the spend and what to change before the next release. Usually they do not.
Dimension
Telemetry / Cost Views
Argmin
Primary object
Telemetry, traces, logs, and spend totals
AI cost decisions with accountable ownership
Main question answered
What happened in the system?
Who owns the spend, what changed it, and what should happen next?
Decision moment
Mostly after runtime behavior or invoice arrival
Before deploy, during approval, and during budget review
Attribution model
Service or tag centric
Model -> service -> code -> identity -> org -> budget
Control Points
The job is intervention, not reporting.
Argmin earns its place by changing the approval, deployment, and budgeting workflows that create AI spend.
02 / Approvals
Budget-aware approvals
Attach service owner, runtime identity, org unit, and budget path to changes that could materially affect AI spend.
03 / Operations
Fast owner resolution
When spend jumps, jump from model usage to the code owner and team that actually control the route.
04 / Planning
Portfolio benchmarks
Compare model routes, services, and teams across the estate to find where a different model, cache strategy, or policy would actually matter.
Attribution Flow
Resolve the chain progressively instead of hiding it behind a total.
Each stage adds evidence. The result is not just attribution. It is an attributable decision record with visible confidence and a next action.
Example record
- Owning service
- support-router-prod
- Primary owner
- Customer Ops Platform
- Budget path
- FY26 Assistants / Support Automation
- Projected monthly delta
- +$28.6k
- Lower-cost route
- GPT-4.1 mini for tier-1 intents
- Confidence
- 0.88 overall / 0.97 at model usage
Recommendation: pause the broad rollout, shift default tier-1 classification to the lower-cost route, and review the budget exception with Engineering plus FinOps before wider deployment.
Why Argmin
Every inference request is a decision problem.
Argmin asks a simple question at request time: which action minimizes total risk-adjusted cost?
Decision Rule
arg min
a ∈ Actions
TRAC(a)
The decision engine retrieves attribution data and conditionally permits, modifies, or redirects the request based on the lowest-risk, lowest-cost available action.
Actions
The available moves.
Model choice, routing decision, region, and configuration. Argmin evaluates the viable options before the request goes through.
TRAC
Total Risk-Adjusted Cost.
Direct cost plus a confidence risk premium. Not just what the request costs, but how risky that choice is for the business.
Plain English
Argmin chooses the best request path available, then enforces that choice in real time.
Deployment
A buyer should know what week one looks like.
Complex infrastructure still needs a believable time-to-value story. Buyers need to know when they get an attributable baseline, when teams can review it, and what has to be connected along the way.
Week 1
Connect cloud, code, and identity context
Read-only connectors attach cloud telemetry and billing to source control, CI/CD, identity, and org data inside the customer environment.
Week 2
Establish the attribution baseline
Argmin starts resolving model calls to services, owners, and budgets with visible confidence so teams can audit the chain before acting on it.
Week 3+
Layer decisions into approvals and budget review
The record becomes operational: projected deltas in change review, budget-aware approvals, and portfolio optimization across services.
No rip-and-replace instrumentation required Read-only connectors by default Advisory workflows first
Next Step
Bring the spend spike, not a generic demo request.
The best starting point is a recent AI spend change, model-routing decision, or approval bottleneck that currently lacks cost context.