AI compute cost decisions for builders

AI compute cost decisions for builders.

RunPlacement helps teams estimate what an AI workload may cost, expose the hidden driver, and decide whether API, managed inference, GPU cloud, default cloud, smaller cloud, bare metal, or another placement category deserves the next look.

Open the AI inference cost calculator Use the placement quiz Read the cost model

Common routes

Start with the problem, not the provider.

Start here

Route the cost question before comparing providers.

Pick the question that matches the decision in front of you. RunPlacement keeps the read directional and estimate-labeled until you replace assumptions with current pricing, logs, bills, or quotes.

Estimate AI cost Start with the broad estimator, then run the full calculator with traffic, token, GPU, managed-serving, or warm-capacity assumptions. Diagnose high LLM/API spend Check output size, retries, tool calls, caching, routing, and batchability before moving off APIs. Compare API vs self-hosted inference Use volume, utilization, control, latency, and operations burden instead of token-price shortcuts. Separate batch from realtime Find which work really needs low latency and which work can queue to raise utilization. Compare managed inference vs GPU cloud Price platform premium against autoscaling, batching, support, storage, networking, and control. Compare GPU quotes Capture storage, network, support, availability, queueing, and commitment terms before ranking providers. Diagnose cloud bill shock Classify the largest service, account, region, and driver before choosing optimization or migration. Price migration payback Estimate exit cost, recurring savings, rollback risk, and the work needed before moving workloads.

AI inference cost

Make API, managed inference, and self-hosted GPU comparable.

Start with total monthly serving cost and effective cost per successful request. Then decide whether realtime, batch, managed serving, or GPU control fits the workload.

AI inference cost calculatorDirectional monthly cost and cost per successful request.
AI inference cost checklistWorksheet fields before comparing APIs, managed inference, or GPUs.
AI inference cost modelFormula and comparison model for effective inference cost.
Inference cost per requestThe denominator that makes API, managed, and GPU options comparable.
Useful GPU-hourSeparate paid accelerator time from completed useful work.

GPU pricing

Hidden quote terms before hardware looks cheap.

AWS bill shock

A triage path before blaming the whole cloud.

Cloud migration

Payback, data movement, rollback, and service replacement.

Workload placement

Choose the category before comparing vendors.

Method

What RunPlacement checks before recommending a category

RunPlacement is provider-neutral and estimate-labeled. It does not invent current pricing, rank providers from fake benchmarks, or turn a quote into a procurement recommendation.

WorkloadBatch, inference, training, app, data pipeline

Cost driverCompute, GPU, storage, logs, egress, managed service

Operating toleranceLow ops, internal infra team, or hardware-like control

Placement outputDefault cloud, GPU cloud, smaller cloud, bare metal, managed platform

Step 1

What AI or compute workload are we pricing?

Workload type Running now Workload pattern

Step 2

What constraint is making the decision hard?

What feels wrong? Budget range Data movement

Step 3

What tradeoff can the team tolerate?

Priority Cheap Fast Simple

GPU need Ops tolerance Real bill, quote, or confusing line item

Decision library

Recent workload placement breakdowns

Practical pages that turn cloud and GPU pricing confusion into placement rules you can actually use.

Cloud migrationCloud Exit Cost Checklist: What To Price Before You Leave AWScost_breakdown

A practical checklist for cloud exit costs, including data transfer, rewrites, managed service replacement, downtime risk, and operations.

Cloud migrationAWS vs Bare Metal: When Owning The Machine Makes Sensecomparison

A practical comparison of AWS and bare metal for steady workloads, predictable utilization, operations, and cost control.

Cloud migrationWhen Not To Leave AWS Even If The Bill Looks Highdecision

A practical decision page for knowing when a high AWS bill should be optimized inside AWS instead of triggering a migration.

Cloud migrationShould You Move From AWS To A Cheaper Cloud?decision

A practical decision page for deciding whether AWS savings justify migration work, data movement, and operational risk.

AWS bill shockAWS vs Smaller Cloud For Simple Workloads: When Default Cloud Is Too Muchcomparison

A practical decision page for deciding when a simple workload should stay on AWS or move to a smaller cloud, managed platform, or bare metal.

Resources

Checklists people can actually use

Useful assets for comparing quotes, bill surprises, migration risk, and workload placement without pretending the first number is the answer.

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.