Assessment · Governance · Run continuously

19Continuous monitoring and incidents

Is everything still inside the envelope today?

Cost, quality, drift and policy checked every day, with incidents handled to closure.

AutomatedStakeholder interviews 3 controls 3 engines

Executable policy

  • Incident playbooks tied to the controls that failed

Continuous checks

  • Watchers journal every run, including clean ones
  • Cost anomalies traced to the workload that moved, not just the total
  • Findings tracked until fixed

Engines that run it

Governance Watch enginePrice Watch engineSavings Finder engine

Price and govern a workflow →

Built on governance engine modules

P18 PlannedAI Incident Response & Resilience

When AI breaks or misbehaves, what happens in the first hour?

Security & Assurance
P6 LiveAI Cost Governance & FinOps

What does AI cost you, and who is accountable for it?

Value & Optimization

AICost governance controls in this module

AICost-GovControl801Partly automated
Continuous monitoring and drift
What it is

Ongoing checks on quality, behavior, cost, access and data use after launch, with owners and review dates.

Why it is needed

AI changes after approval: models update, data shifts, permissions grow and prices move.

Why enterprises need it

A once-a-year review cannot keep up. Teams need alerts when something material changes.

What you get:Drift caught earlyAccess growth spottedCost changes explained
Run by
AI Cost Governance and FinOps engineAI Quality and Acceptance Gate engineGovernance Watch enginePrice Watch engine
Data
AI gateway telemetry, Microsoft Graph and Entra ID
Evidence produced
Monitoring log: system, metric, threshold, current value, change, owner.
Maps to
NIST AI RMF: Measure, Manage; ISO/IEC 42001: monitoring and measurement
AICost-GovControl802Runs today
AI incident response
What it is

A plan to detect, contain, investigate and report AI failures, with a kill switch and recovery steps.

Why it is needed

AI incidents are different: wrong answers at scale, data leaks through prompts, agents acting on bad input.

Why enterprises need it

Regulators may need notice within set times. Customers need a fast, honest response.

What you get:Faster containmentNotification duties metLessons fed back into controls
Run by
AI Incident Response engine
Data
AI gateway telemetry, Questionnaire
Evidence produced
Incident record: date, system, what happened, impact, containment time, notification, root cause, fix.
Maps to
EU AI Act: serious incident reporting; NIST AI RMF: Manage
AICost-GovControl805Runs today
Remediation that closes findings
What it is

A workflow that assigns each finding to an owner, agrees the fix or an exception, and verifies closure.

Why it is needed

Listing issues does not reduce risk. Fixing them does.

Why enterprises need it

Leaders want to see risk going down, not a growing list of findings.

What you get:Findings closed, not just loggedExceptions with review datesRisk trend visible
Run by
Shared Responsibility Register
Data
Questionnaire
Evidence produced
Remediation table: finding, owner, action, exception, due date, status, verified on.
Maps to
ISO/IEC 42001: corrective action; NIST AI RMF: Manage

Frameworks

NIST AI RMF and NIST AI 600-1Govern, Map, Measure and Manage activities, including the generative AI risks
EU AI ActCategory and role per system, prohibited uses, high-risk duties, transparency, AI literacy, incident reporting

Stakeholders

OperationsCISOFinOps
← 18. Executable policy All modules 20. Evidence, audit and executive reporting →
📖 Data sources & methodology 172 text models · 9 embeddings · 44 vision · 62 audio · 8 vector DBs across 10 vendor pages · last verified 2026-10-18

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-10-18.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

Anthropic
2026-10-18
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 704 days captured
Anthropic Docs
2026-10-18
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 704 days captured
OpenAI
2026-10-18
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 705 days captured
Google AI
2026-10-18
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 680 days captured
Google Vertex
2026-10-18
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 680 days captured
DeepSeek
2026-10-18
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 619 days captured
xAI
2026-10-18
https://x.ai/api
Daily snapshot since Nov 2024 · 537 days captured
Mistral
2026-10-18
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 678 days captured
Cohere
2026-10-18
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 704 days captured
Voyage AI
2026-10-18
https://docs.voyageai.com/docs/pricing

Inferred values (marked with * in calculator tables)

Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

Vendor / Model Field Why it’s inferred
Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2 batchInput Derived at 50% of input.
OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
OpenAI — GPT-5 cachedInput Derived at 10% of input.
OpenAI — GPT-5 batchInput Derived at 50% of input.
OpenAI — GPT-5 batchOutput Derived at 50% of output.
OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.1 batchInput Derived at 50% of input.
OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →