Workload profile
Users, request and batch patterns, concurrency, latency, availability, throughput, context, model size, CPU or GPU, storage, and growth assumptions.
Guide · AI Infrastructure
Reviewed by Kubto · 9 August 2026
AI infrastructure readiness is a workload and operations decision
Before selecting cloud services, containers, GPUs, or model providers, document the workload, data path, latency and recovery needs, security boundary, cost drivers, and team that will operate it.
Who this is for
Platform, cloud, infrastructure, ML, application, security, finance, and operations teams preparing production AI or reviewing an architecture proposal.
Problem to solve
AI workloads combine variable model behavior, external providers, data systems, queues, caches, batch work, and application dependencies that create failure and cost paths a demo does not expose.
This guide does not prescribe a cloud, model provider, GPU platform, database, or orchestrator. Current capabilities, limits, pricing, and terms must be verified during design.
Scope
Readiness areas to document
Model and provider path
Hosted or self-managed inference, region, quotas, rate limits, fallback, version changes, data terms, observability, and portability.
Data and state
Sources, sensitivity, residency, relational and vector stores, object storage, cache, queue, state, backup, restore, retention, and deletion.
Runtime and delivery
Services, jobs, containers, orchestration, artifacts, environments, secrets, configuration, migrations, deployment, canary, and rollback.
Network and security
Trust boundaries, ingress and egress, identities, authorization, encryption, provider access, tool permissions, logging, abuse, and incident paths.
Operations and economics
Service objectives, logs, metrics, traces, quality signals, alerts, on-call, capacity, recovery, unit cost, budgets, and ownership.
Architecture
A readiness review before target-state design
- 01
Map current state
Inventory applications, data, environments, providers, identities, pipelines, operations, costs, constraints, incidents, and owners.
- 02
Define requirements and failure budgets
Set user, quality, latency, availability, recovery, privacy, security, cost, scaling, and support requirements with priorities.
- 03
Compare patterns
Evaluate hosted and self-managed components, regions, topology, resilience, observability, portability, and cost against those requirements.
- 04
Validate the risky assumptions
Use load, failure, restore, security, quality, quota, and cost tests before approving the production path.
Deliverables
What the engagement can produce
✓
Workload and dependency profile
Traffic and job patterns, quality path, providers, data systems, integration dependencies, constraints, owners, and uncertainty.
✓
Target architecture and cost model
Components, topology, regions, trust boundaries, capacity assumptions, unit drivers, scaling, resilience, and alternatives.
✓
Production-readiness checklist
Deployment, access, observability, incident, capacity, backup, restore, fallback, rollback, provider, and support gates.
✓
Validation evidence
Test conditions, results, limitations, open risks, remediation, accepted residual risk, and production decision.
Reliability
Design degradation, not only the healthy path
Provider and quota failure
Define timeout, retry, circuit breaking, fallback, queueing, user communication, and recovery when inference or an external API is degraded.
Data dependency failure
Plan stale or unavailable indexes, databases, object stores, queues, caches, and synchronization with clear consistency and recovery behavior.
Quality failure
Monitor retrieval and output quality separately from uptime; provide refusal, review, rollback, and provider or model version controls.
Cost
Model cost as units, not one monthly guess
Serving units
Requests, tokens, model tier, GPU time, concurrency, latency target, cache effectiveness, and fallback drive inference cost.
Data and platform units
Index size, storage, database and vector operations, queue traffic, network transfer, logs, traces, backups, and environments add operating cost.
People and risk units
Evaluation, support, incident response, content maintenance, security review, upgrades, and provider change are part of total cost.
Boundaries
Boundaries and decisions to verify
Good work is easier to trust when the team knows what is included, what still needs proof, and who owns each decision.
A diagram is not readiness
Production approval requires implemented controls, test evidence, owner acceptance, support preparation, and contractual dependencies.
Autoscaling does not solve every limit
Provider quotas, data bottlenecks, cold starts, GPU availability, state, cost budgets, and downstream systems can constrain scale.
Recovery must be tested
Backup existence is not restore evidence. Recovery objectives require tested procedures, permissions, dependencies, and accountable owners.
Use the highest-risk assumption to define the first test
Kubto can help map the workload, compare architecture options, and design validation for performance, recovery, security, quality, or cost.
Plan an infrastructure readiness review