Workload placement
Workload Placement Assumptions Index
Short answer: Use this when the team needs evidence before choosing an infrastructure category.
- This is a decision checklist, not a final price quote.
- Verify final numbers against provider pricing pages and your own bill or quote.
Use this when
- The team is choosing between default cloud, specialized GPU cloud, smaller cloud, bare metal, managed platform, or hybrid placement.
- A provider quote or bill line is pulling the decision before workload fit is clear.
- The next step should be evidence collection rather than vendor ranking.
Not for
- Live pricing tables, current provider rankings, or benchmark claims.
- Replacing security, compliance, procurement, or architecture approval.
- Final vendor selection without observed bills, quotes, and workload-specific testing.
Assumption map
Placement fit starts with workload evidence.
Provider comparison is easier once the category assumptions are explicit.
01
Shape
What kind of workload is this, and how often does it run?
02
Driver
Which cost or reliability constraint is actually painful?
03
Gravity
Where does data live, and what must move?
04
Owner
Who operates the chosen placement after launch?
Worksheet Fields
Use this as the working version before copying the decision into a doc, ticket, or vendor email.
| Field | Capture | Why it matters |
|---|---|---|
| Workload shape | Batch, inference, training, web app, data pipeline, steady service, or experiment. | Defines what the placement must optimize for. |
| Cost driver | Compute, GPU, storage, transfer, logs, managed services, support, idle time, or operations. | Prevents a generic provider decision from hiding the real constraint. |
| Data path | Where data lives, how much moves, region needs, latency tolerance, and retrieval patterns. | Shows whether a cheaper placement creates transfer or locality problems. |
| Operations owner | Who owns incidents, scaling, patching, monitoring, and cost review. | Keeps lower unit cost from becoming hidden engineering work. |
Research-ready
Copy Into A Placement Evidence Sheet
Use this before comparing provider quotes. Each row should be observed, quoted, estimated, or unknown.
Field What to verify Evidence type Why it matters Workload shape Inference, training, batch, web app, data pipeline, steady service, or experiment Architecture notes or logs Defines the placement category. Usage pattern Always-on, bursty, queued, seasonal, experimental, or commitment-backed Metrics, logs, or forecast Shows whether fixed capacity or elastic capacity fits. Dominant cost driver Compute, GPU, storage, transfer, observability, managed service, support, idle time, or engineering work Bill export, quote, or estimate Keeps the decision focused on the real constraint. Data path Data source, destination, region, transfer frequency, retention, retrieval, and latency Architecture map or official pricing fields Finds transfer and locality risks. Operational owner Team or provider responsible for incidents, scaling, patching, monitoring, and cost review Ownership notes or contract Prevents hidden ops work. Control need Customization, compliance, portability, managed-service dependency, and exit path Architecture review Explains when direct infrastructure is worth the burden. Commitment horizon Expected workload duration, growth confidence, contract tolerance, and rollback path Forecast or planning doc Prevents premature long commitments. Category fit Default cloud, specialized GPU cloud, smaller cloud, bare metal, managed platform, or hybrid Synthesis Creates the shortlist before provider comparison.
AI prompt
Prompt To Stress-Test Placement Assumptions
Paste this with your evidence sheet when the team is comparing infrastructure categories.
You are reviewing workload placement assumptions. Do not choose a provider by brand, assume current pricing, or rank providers unless I supply source data. Here are the assumptions: [Paste workload shape, usage pattern, dominant cost driver, data path, operational owner, control need, commitment horizon, and category fit here] Please: 1. Separate observed facts, quoted inputs, estimates, and unknowns. 2. Identify which infrastructure categories fit the workload and which assumptions drive the answer. 3. Flag missing evidence around data movement, utilization, reliability, operations, support, and commitment risk. 4. Recommend the next evidence to collect before requesting provider quotes. 5. Keep the recommendation provider-neutral and category-level. 6. Avoid live pricing, benchmarks, legal, compliance, procurement, or provider-ranking claims unless supplied in the source data.
Short Answer
- Workload placement should be researched before provider comparison.
- Start with workload shape, dominant driver, data movement, utilization, operations owner, and commitment horizon.
- Use official pricing pages and actual bills to verify fields, but keep live rates out of reusable decision content.
Assumptions To Capture
- Workload type: inference, training, batch, web app, data pipeline, steady service, or experiment.
- Usage pattern: always-on, bursty, queued, seasonal, experimental, or commitment-backed.
- Dominant cost driver: compute, GPU, storage, transfer, observability, managed service, support, idle time, or engineering work.
- Data path: source, destination, region, transfer frequency, retention, retrieval, and latency requirements.
- Operational owner: who handles incidents, monitoring, scaling, patching, backup, and cost review.
- Control need: customization, compliance, portability, managed-service dependency, and exit path.
- Commitment risk: expected duration, growth confidence, contract terms, and rollback path.
Source Checks
- Use provider pricing pages and calculators to identify the fields that matter, not to publish static rates.
- Use observed bills or exports to separate recurring cost from one-time experiments.
- Use provider quotes for target assumptions, then label them as quoted or estimated.
- Document which assumptions are observed, quoted, estimated, or unknown before comparing categories.
Placement Criteria
- Default cloud: managed services, compliance, integration, or team familiarity matter more than lowest unit cost.
- Specialized GPU cloud: useful GPU-hours, capacity access, and data movement dominate the decision.
- Smaller cloud or bare metal: the workload is steady, portable, and operational ownership is clear.
- Managed platform: simplicity, delivery speed, and absorbed operations outweigh infrastructure control.
- Hybrid placement: one expensive slice can move while data-heavy or managed-service-dependent parts stay put.
FAQ
What should I collect before choosing workload placement?
Collect workload shape, dominant cost driver, usage pattern, data movement, latency needs, operational owner, portability constraints, and commitment tolerance.
Is workload placement the same as provider selection?
No. Placement chooses the infrastructure category first. Provider selection comes later, after the category and assumptions are clear.
How does this help AI infrastructure decisions?
It separates AI serving, batch, training, and data-heavy workloads by usage shape, utilization, data movement, and operations instead of forcing every workload into one provider model.
Sources
RunPlacement quiz
Pressure-test this workload
Choose a placement category only after workload shape, cost driver, data movement, utilization, operating owner, and commitment risk are visible.
Uses workload type, budget, GPU need, data movement, priority, and ops tolerance.