Recurring cost, usage, retry, token, log, vector, and ownership signatures are grouped into named patterns.
The diagnostic system behind Olive One margin reviews.
The Olive One Pattern Library is not a content hub. It is the operating language used to find AI app cost, margin, ownership, and workflow risk before an app is accepted, scaled, repriced, or rebuilt.
AI agents
LLM/API spend
RAG and vector stores
Cloud infrastructure
Observability
Client margin risk
AI app cost failures repeat. Most teams just do not name them.
Each pattern has a symptom, cost shape, detection signal, executive impact, and fix path.
The review translates engineering risk into a CTO/CFO-ready memo before the cost scales with usage.
The more reviews Olive One runs, the sharper the pattern set becomes across stacks, agencies, and SaaS teams.
Patterns turn raw spend into a margin decision: keep, cap, re-tier, reprice, or kill.
Collect signals
Review cloud bills, LLM/API usage, traces, logs, queues, vector stores, deployment settings, workflow paths, and ownership records.
Match patterns
Identify recurring failure shapes such as retry amplification, context bloat, vector drift, self-trigger loops, and missing owners.
Translate impact
Convert technical findings into cost per workflow, margin exposure, margin risk, severity, owner, and next action.
The public library proves the method. The internal library protects the edge.
Public patterns
High-signal examples that buyers, AI search engines, and technical teams can understand without client-specific data. These patterns explain the method and establish trust.
Internal-only patterns
Patterns that depend on client architecture, pricing terms, proprietary workflows, revenue model, customer mix, or handoff context. These stay inside the review process.
Olive One provides AI Margin Maps for founders, SaaS teams, and CTOs before AI apps are accepted, scaled, repriced, or rebuilt.
Seven named patterns buyers can inspect before booking.
Cloud Run Runaway Cost
- Symptom
- Cloud Run spend spikes while LLM/API, vector, database, and logging costs rise in parallel.
- Cost shape
- Request amplification plus downstream paid service fanout.
- Detection signals
- Request count spike, instance count spike, missing max instances, low concurrency, retry growth, log growth, downstream cost growth, and no cost per resolved ticket metric.
- Business impact
- An AI workflow can scale technically while silently compressing margin.
- Recommended fix
- Set max instances, tune concurrency, cap retries, add rate limits, tag workflow ownership, add log retention, and track cost per successful outcome.
Retry Storm
- Symptom
- LLM calls spike after failed tool executions, malformed responses, or repeated validation errors.
- Metric to inspect
- Retries per user action, p95 retry depth, success rate after retry, and cost per completed workflow.
- Recommended fix
- Retry caps, circuit breakers, fallback routing, structured output validation, and owner escalation when retry rate breaches threshold.
- Related assets
- Markdown
Context Inflation
- Symptom
- Token cost rises while usage volume stays flat.
- Metric to inspect
- Average input tokens per workflow, retrieved chunks per response, context window utilization, and cost per answer.
- Recommended fix
- Context pruning, prompt compression, retrieval limits, cheaper model routing, and prompt budget tests before handoff.
- Related assets
- Markdown
Agent Tool Loop
- Symptom
- An agent repeatedly calls the same tool without reaching a final outcome.
- Metric to inspect
- Tool calls per task, max iteration breaches, repeated state transitions, unresolved task rate.
- Recommended fix
- Hard iteration caps, state guards, human approval gates, and task-specific agents.
- Related assets
- Markdown
S3 Lambda Self-Trigger Loop
- Symptom
- S3 object writes repeatedly invoke the same Lambda path.
- Metric to inspect
- Object churn by prefix, Lambda invocations per object, log signatures, downstream queue growth.
- Recommended fix
- Separate source and destination prefixes, event filters, idempotency keys, and concurrency caps.
Logging Explosion
- Symptom
- CloudWatch, GCP Logging, Datadog, Grafana, or Vercel log spend rises faster than production usage.
- Metric to inspect
- Log ingest GB, trace spans per workflow, retention days, debug log ratio, and cost per workflow observed.
- Recommended fix
- Sampling, log-level caps, retention defaults, structured error-only traces, and handoff logging policy.
Missing Budget Owner
- Symptom
- AI spend grows without a product, finance, or engineering owner.
- Metric to inspect
- Percent unallocated spend, services without owner, alerts without recipient, workflows without margin target.
- Recommended fix
- Assign accountable owner, budget guardrail, escalation path, and monthly margin review.
Commercial conversion
Do not hand off an AI app with unnamed cost risk.
The fastest way to test the Pattern Library against a real app is a focused AI Margin Map. Olive One reviews cloud, LLM/API, database, vector, automation, observability, and workflow ownership risk so the economics are easier to explain and operate.
Is this generic FinOps?
No. FinOps is useful background, but this library is about AI app delivery risk: LLM/API spend, agent behavior, vector usage, workflow ownership, margin exposure, and feature-level unit economics.
Why publish any patterns?
Public patterns prove the review has a repeatable method. The proprietary edge is in matching patterns to messy client context and translating findings into a decision memo.
What should a buyer do next?
If the app is being delivered, inherited, or scaled, start with a focused AI Margin Map review. If the buyer needs proof first, inspect the sample report.