About RunPlacement

The AI compute cost decision layer above pricing pages.

RunPlacement helps builders answer two practical questions: what will this AI or compute workload really cost, and where should it run? The site focuses on confusing inference, GPU, cloud bill, and migration decisions where headline prices hide the real cost driver.

What we coverAI inference cost, API vs self-hosted inference, GPU cloud pricing, cloud bill shock, and migration payback.
What we avoidFake precision, generic AI content, paid-link schemes, and provider fan fiction.
How pages workShort answer, rough math, tradeoffs, sources, decision rule, and quiz handoff.

Author

Andrew Cooper, Founder of RunPlacement

Andrew Cooper is the founder of RunPlacement, a provider-neutral AI compute cost and workload placement tool for comparing API inference, managed inference, GPU cloud, default cloud, bare metal, and managed infrastructure options. He writes practical frameworks for AI inference cost, AI infrastructure cost, cloud bill shock, GPU quote comparison, and migration tradeoffs.

RunPlacement is practical, provider-neutral infrastructure decision content, not a pricing authority, benchmark lab, or procurement advisor.

Editorial methodology

How RunPlacement pages are made

RunPlacement pages use public provider documentation, source-linked pricing pages where relevant, estimate-labeled examples, and practical decision frameworks. Estimates are directional and should be verified against provider pricing pages before buying or migrating.

Current provider prices, availability, terms, and credits should always be verified from provider sources.

Why it exists

AI compute decisions are often made from the wrong number.

Builders compare token prices, GPU hourly rates, monthly cloud totals, or migration promises before isolating the actual workload constraint. RunPlacement turns those messy inputs into a narrower decision: API, managed inference, specialized GPU cloud, default cloud, smaller cloud, bare metal, or managed platform.

GPU pricingGPU Cloud Quote ChecklistChecklist / 7 sections / source-linked

A practical checklist and visual worksheet for comparing GPU cloud quotes beyond the advertised hourly rate.

 AWS bill shockAWS Bill Shock Triage ChecklistChecklist / 7 sections / source-linked

A first-pass checklist and visual triage flow for finding the AWS line items that usually make a bill jump.

 Cloud migrationCloud Exit Cost ChecklistChecklist / 7 sections / source-linked

A checklist and payback worksheet for pricing the real cost of leaving AWS, GCP, or Azure before migration starts.

 Workload placementWorkload Placement WorksheetChecklist / 7 sections / source-linked

A practical worksheet and decision map for deciding where a workload should run before provider choice hardens.

Topics

RunPlacement topics

These are the main problem areas RunPlacement covers for builders comparing AI inference, GPU, cloud bill, and migration tradeoffs.

GPU Cloud Pricing Decisions GPU Cloud Pricing Decisions A practical library for comparing GPU clouds, H100 quotes, utilization, hidden costs, and provider tradeoffs.

Use these pages when the GPU hourly rate is visible but the workload placement decision is still muddy. The useful question is usually about useful GPU-hours, data movement, provider variance, commitment, and operations.

 AWS Bill Shock Decisions AWS Bill Shock Decisions A practical library for diagnosing high AWS bills before deciding whether to optimize, migrate, or re-place the workload.

Use these pages when an AWS bill feels wrong and the team is not sure whether the answer is cleanup, architecture change, or leaving AWS.

 Cloud Migration Decisions Cloud Migration Decisions A practical library for deciding whether to leave AWS, move a workload, or stay put and fix the expensive parts.

Use these pages when the bill makes migration tempting but the real question is whether the workload is portable enough, expensive enough, and operationally worth moving.

 AI Inference Cost Decisions AI Inference Cost Decisions A practical library for estimating AI inference cost, API versus self-hosted tradeoffs, batch versus realtime serving, managed inference, and GPU idle cost.

Use these pages when an AI app is moving from prototype to production and the real question is what inference will cost per request, per month, and per completed workload.

 Framework library Definitions and formulas Workload placement, useful GPU-hours, bill shock, exit payback

Use these when a teammate, article, or planning doc needs a clear concept instead of a single checklist.

 Resource library Checklists and worksheets Quote review, triage, exit cost, and placement assets

Use these when a publication, teammate, or planning doc needs a practical worksheet rather than another opinion page.