We follow spend from a customer action through every model request, embedding call, tool, retry, fallback, queue, and supporting compute resource needed to complete it. Provider invoices and token charts are inputs, but they rarely show whether the work succeeded. The useful denominator is a completed product outcome, segmented by workflow and tenant where appropriate, with failed and abandoned attempts visible rather than averaged away.
AI Reliability Engineering
Find what each completed AI workflow costs—and where waste enters.
LLM cost optimization services for production AI. Trace cost per completed workflow, remove retry and context waste, and protect quality with evals.
Book a 30-minute consultation
30 minutes. No deck. Leave with a clear next step.
System-level interventionProduction-shaped diagnosisInspectable verification
The production trigger
Recognize the failure before naming the service.
Tie spend to completed workflows, then remove measured waste behind eval guardrails.
The provider dashboard shows tokens and requests, but not which customer workflow created value, retried silently, loaded unnecessary context, or used a frontier model for a routine step.
Trigger detail 1We instrument cost per completed workflow, establish eval guardrails, and implement the highest-confidence routing, caching, batching, and retry changes.
Trigger detail 2System diagnosis
Find where the failure actually lives.
The visible symptom is rarely the whole problem. The first pass follows it through the production path until the controllable boundary is clear.
The trace separates structural cost from accidental waste. Structural cost comes from the model capability, context, and workload genuinely required. Waste appears as duplicate retries, runaway loops, unnecessary history, low-value frontier calls, cache misses, overprovisioned serving, repeated retrieval, or fallbacks that execute after the user has already left. We quantify candidates before changing them so engineering time is directed toward material, controllable sources.
Quality, latency, security, and customer experience establish the optimization boundary. A cheaper route that fails more often may raise cost per completed workflow; a shared cache can violate tenant isolation; aggressive context trimming can remove the instruction that makes an answer useful. This service fits a live product with meaningful spend, trace access, and representative eval cases. It is not a promise of savings before the baseline identifies where cost actually originates.
The intervention
Change the critical path, not the surrounding theatre.
Each workstream targets a different failure boundary. Together they connect product behavior, infrastructure, controls, and verification.
Unit-economics instrumentation
Connect request and workflow identifiers across model providers, application traces, queues, tools, and completion events. We attribute input, output, cached, reasoning, embedding, and retry usage according to available provider data, then define workflow success and failure. The dashboard exposes distributions and cohorts rather than only averages, including high-cost outliers that can hide in a stable monthly total.
Waste and failure-path analysis
Inspect retry storms, timeout overlap, fallback cascades, duplicated work, context growth, excessive retrieval, abandoned streams, and infrastructure idle capacity. Each candidate includes frequency, unit impact, response path, quality risk, and evidence. We distinguish a temporary traffic anomaly from a repeatable product pattern so the remediation backlog reflects expected value rather than one alarming trace.
Guardrailed optimization
Test model routing, prompt and context reduction, deterministic and semantic caching, batching, concurrency, retry controls, and serving changes against production-shaped evals. Experiments compare completed-workflow cost alongside quality and latency. Changes with uncertain behavior stay behind controlled exposure or remain recommendations rather than being presented as savings already achieved.
Budget controls and response
Implement alerts and limits close to the source of spend, with enough context for an operator to act. Controls may include workflow budgets, retry caps, per-tenant anomaly detection, provider quotas, or capacity thresholds. Every alert has a responsible responder, diagnostic link, and response path; a budget control that simply shuts down a critical customer workflow is not considered complete.
Inspectable changes
See what changes in the system.
- Cost-per-workflow instrumentation
- Model and prompt cost baseline
- Retry, timeout, and cascade diagnosis
- Quality-guardrailed model routing
- Semantic and deterministic cache assessment
- Context, batching, and token-use changes
- Dashboard, alerts, and optimization backlog
Engineering judgment
The decisions that determine whether the change holds.
Optimization objective
Choose the unit that represents product value: completed support resolution, processed document, accepted draft, successful agent task, or another observable outcome. We define associated quality and latency guardrails. This prevents token reduction from becoming the goal when customers experience more retries, lower completion, or slower end-to-end work.
Routing and cache policy
Define which workflows are eligible for smaller models or cached results, which quality thresholds apply, and when escalation occurs. Cache keys include the context that affects correctness, freshness, permissions, and tenant boundaries. Routing decisions are versioned and traceable so a later incident can identify which policy selected the model or response.
Savings confidence
Separate measured historical opportunity, tested change, and projected future impact. Seasonality, volume, provider pricing, and user behavior can move the result. We document assumptions and confidence instead of converting a short sample into a guaranteed annual number, and we prioritize changes that remain worthwhile across a reasonable range of demand.
Strong fit
- Your AI product is live and provider or infrastructure spend is material.
- You cannot tie spend to completed user workflows today.
Probably not the right fit
- Your AI spend is too small for the savings to justify a focused audit.
- Your AI product is not live yet.
Scope, timing, and ownership
Know the commercial shape before the call.
A bounded engagement with a service-specific delivery sequence, an agreed price, and artifacts that stay under your control.
Day 1
Cost trace
Map requests, retries, context, models, infrastructure, and completed workflows.
Day 2
Guardrailed changes
Evaluate routing, caching, batching, context reduction, and retry controls against quality thresholds.
Day 3
Implementation and dashboard
Ship priority changes and hand off cost-per-workflow reporting with owners and alerts.
Typical fixed scope
Mid-four to low-five figures
Most engagements fall in this planning range. Your exact fixed price is agreed in writing after we review the production surface, access needs, and success criteria. The range is guidance, not a quote.
Commercial terms
No open-ended consulting meter.
- Fixed scope agreed in writing
- Fixed timeline agreed in writing
- Fixed price agreed before work starts
- Client-owned deliverables from day one
- No hourly meter or surprise overages
Client ownership
The implementation remains yours.
- Your repository
- Your infrastructure
- Your evals and evidence
- Yours from commit one
Book a 30-minute consultation
30 minutes. No deck. Leave with a clear next step.
AI Cost Rescue
Questions worth settling before the call
Technical, commercial, and handoff questions answered before you book.
Turn provider spend into an operating metric your team can act on.
Bring the failing workflow, current evidence, and the constraints your engineers cannot ignore.
Book a 30-minute consultation
30 minutes. No deck. Leave with a clear next step.