Maturity Model (Performance Efficiency)

The Performance Efficiency maturity model enables structured self-assessment and defines a clear development path from reactive performance observation to autonomous, predictive performance management.

The Five-Level Model

Level	Label	Characteristics
Level 1	Reactive / Undocumented	No performance baselines. SLOs are missing or informally defined. Performance problems are only discovered after user complaints. Auto-scaling not configured; static capacity. Database queries never analyzed; no slow query logs. No load test process; performance under load unknown.
Level 2	Documented & Defined	Basic metrics are collected (CPU, memory, average latency). Informal performance targets exist, but not as formal SLOs. Auto-scaling configured for some workloads, but not validated. Slow query logging enabled; reviews ad hoc. Manual load tests before major releases. Caching partially implemented, but without a strategy.
Level 3	Enforced & Monitored	Formal SLOs defined and instrumented for all production services. Auto-scaling configured for all stateless workloads and validated through load tests. Load tests configured as CI/CD deployment gate. Caching strategy documented; hit rates measured and monitored. Database performance insights active; index strategy documented. Performance debt register introduced.
Level 4	Measured & Automated	Error budget management: deployments blocked on budget exhaustion. Performance regressions automatically detected in CI. All storage volumes migrated to optimal performance types. VPC endpoints and CDN configured for all relevant services. Quarterly performance reviews with debt paydown tracking. Serverless profiling for Lambda/functions performed.
Level 5	Optimized & Predictive	Predictive capacity modeling: capacity needs are modeled before traffic spikes. Auto-scaling fully automated without manual intervention required. Continuous performance debt reduction: backlog always contains active paydown tasks. SLOs are explicitly referenced in architectural decisions (ADRs). ML-assisted anomaly detection in performance metrics.

Level

Label

Characteristics

Level 1

Reactive / Undocumented

No performance baselines. SLOs are missing or informally defined. Performance problems are only discovered after user complaints. Auto-scaling not configured; static capacity. Database queries never analyzed; no slow query logs. No load test process; performance under load unknown.

Level 2

Documented & Defined

Basic metrics are collected (CPU, memory, average latency). Informal performance targets exist, but not as formal SLOs. Auto-scaling configured for some workloads, but not validated. Slow query logging enabled; reviews ad hoc. Manual load tests before major releases. Caching partially implemented, but without a strategy.

Level 3

Enforced & Monitored

Formal SLOs defined and instrumented for all production services. Auto-scaling configured for all stateless workloads and validated through load tests. Load tests configured as CI/CD deployment gate. Caching strategy documented; hit rates measured and monitored. Database performance insights active; index strategy documented. Performance debt register introduced.

Level 4

Measured & Automated

Error budget management: deployments blocked on budget exhaustion. Performance regressions automatically detected in CI. All storage volumes migrated to optimal performance types. VPC endpoints and CDN configured for all relevant services. Quarterly performance reviews with debt paydown tracking. Serverless profiling for Lambda/functions performed.

Level 5

Optimized & Predictive

Predictive capacity modeling: capacity needs are modeled before traffic spikes. Auto-scaling fully automated without manual intervention required. Continuous performance debt reduction: backlog always contains active paydown tasks. SLOs are explicitly referenced in architectural decisions (ADRs). ML-assisted anomaly detection in performance metrics.

Maturity per Control

Control	L1	L2	L3	L4	L5
WAF-PERF-010 Compute Sizing	No standard; over-provisioned	Experience-based; documented	Measured baseline; CI validation	Compute Optimizer integrated	ML-based predictive sizing
WAF-PERF-020 Auto-Scaling	Static capacity	ASG configured, not validated	Validated by load test	Predictive scaling configured	Autonomous capacity management
WAF-PERF-030 Caching	No cache	Ad-hoc cache without strategy	Strategy documented; hit rate measured	Cache hit >= 80% enforced	Adaptive TTLs, intelligent warming
WAF-PERF-040 Database Performance	No analysis; slow queries unknown	Slow query log enabled	Performance Insights active; index strategy	Automatic regression detection	Query SLOs, automatic tuning
WAF-PERF-050 SLOs & Monitoring	No SLOs	Informal targets, average values	Formal SLOs; P99 alerting	Error budget management	Predictive burn rate alerts
WAF-PERF-060 Load Tests	No load tests	Manual tests before releases	Automatic in CI/CD gate	Regression detection automatic	Continuous + chaos engineering
WAF-PERF-070 Network Performance	No topology design	CDN for static content	VPC endpoints + CDN configured	Latency baseline measured	Anycast, edge computing
WAF-PERF-080 Serverless & Managed	Default config; not optimized	Memory adjusted without measurement	Profiling performed; optimized	Provisioned concurrency where needed	Cost per invocation optimized
WAF-PERF-090 Storage I/O	gp2; no I/O monitoring	Storage type selected	gp3 migration; I/O alerts	Disk type fully optimized	Intelligent tiering; auto-tuning
WAF-PERF-100 Performance Debt	No documentation	Informal tracking in tickets	Register + quarterly review	Business impact quantified	Automatic debt detection

Control

WAF-PERF-010 Compute Sizing

No standard; over-provisioned

Experience-based; documented

Measured baseline; CI validation

Compute Optimizer integrated

ML-based predictive sizing

WAF-PERF-020 Auto-Scaling

Static capacity

ASG configured, not validated

Validated by load test

Predictive scaling configured

Autonomous capacity management

WAF-PERF-030 Caching

No cache

Ad-hoc cache without strategy

Strategy documented; hit rate measured

Cache hit >= 80% enforced

Adaptive TTLs, intelligent warming

WAF-PERF-040 Database Performance

No analysis; slow queries unknown

Slow query log enabled

Performance Insights active; index strategy

Automatic regression detection

Query SLOs, automatic tuning

WAF-PERF-050 SLOs & Monitoring

No SLOs

Informal targets, average values

Formal SLOs; P99 alerting

Error budget management

Predictive burn rate alerts

WAF-PERF-060 Load Tests

No load tests

Manual tests before releases

Automatic in CI/CD gate

Regression detection automatic

Continuous + chaos engineering

WAF-PERF-070 Network Performance

No topology design

CDN for static content

VPC endpoints + CDN configured

Latency baseline measured

Anycast, edge computing

WAF-PERF-080 Serverless & Managed

Default config; not optimized

Memory adjusted without measurement

Profiling performed; optimized

Provisioned concurrency where needed

Cost per invocation optimized

WAF-PERF-090 Storage I/O

gp2; no I/O monitoring

Storage type selected

gp3 migration; I/O alerts

Disk type fully optimized

Intelligent tiering; auto-tuning

WAF-PERF-100 Performance Debt

No documentation

Informal tracking in tickets

Business impact quantified

Automatic debt detection

Self-Assessment Checklist Level 2

Does the following apply to your organization?

CPU, memory, and latency metrics are collected for all production services
Performance targets exist informally (e.g. "should be under 500ms")
Auto-scaling is configured for at least one workload
Slow query logging is active in at least one database
Manual load tests are performed before major releases
Caching is implemented for static assets

If >= 4 apply: Level 2 reached. If >= 4 do not apply: Level 1 action required.

Self-Assessment Checklist Level 3

Does the following apply to your organization?

Formal SLOs (with P95/P99 latency targets) defined for all production-critical services
SLOs are instrumented: SLIs are continuously measured
SLO burn rate alerting is configured
Auto-scaling configured for all stateless production workloads
Auto-scaling validated by load test under realistic load
Load tests run automatically in the CI/CD pipeline as a deployment gate
Acceptance criteria for load tests are defined and enforced
Caching strategy is documented (layer, TTL, invalidation)
Cache hit rates are measured and monitored
Performance Insights or equivalent database monitoring is active
Index strategy for high-frequency queries is documented
Performance debt register exists and is kept up to date
All EBS/managed disks for new deployments use gp3 or Premium SSD

If >= 10 apply: Level 3 reached.

Self-Assessment Checklist Level 4

Does the following apply to your organization?

Error budgets are tracked and considered in deployment decisions
Performance regressions are automatically detected in CI/CD (baseline comparison)
Performance debt register contains business impact estimates
Quarterly performance reviews with engineering leadership take place
Debt paydown is prioritized in the sprint backlog
VPC endpoints configured for all major cloud service APIs
CDN cache hit rate >= 95% for static content
Lambda/function memory optimized through profiling (power tuning or equivalent)
gp2-to-gp3 migration fully completed
Stress test reports (2x, 5x peak load) for all critical services

If >= 8 apply: Level 4 reached.

Recommended Entry Path

For organizations currently at Level 1:

Week	Action	Related Control
Week 1–2	SLO workshop: define P95/P99 targets for all production services. Review monitoring stack.	WAF-PERF-050
Week 3–4	Slow query analysis: enable Performance Insights, optimize top-20 queries.	WAF-PERF-040
Week 5–6	Auto-scaling configuration for the three most important stateless services.	WAF-PERF-020
Week 7–8	Run first load test in staging; define acceptance criteria.	WAF-PERF-060
Week 9–10	Start gp2-to-gp3 migration for all EBS volumes.	WAF-PERF-090
Week 11–12	Create performance debt register: capture known problems, prioritize.	WAF-PERF-100

Week

Action

Related Control

Week 1–2

SLO workshop: define P95/P99 targets for all production services. Review monitoring stack.

WAF-PERF-050

Week 3–4

Slow query analysis: enable Performance Insights, optimize top-20 queries.

WAF-PERF-040

Week 5–6

Auto-scaling configuration for the three most important stateless services.

WAF-PERF-020

Week 7–8

Run first load test in staging; define acceptance criteria.

WAF-PERF-060

Week 9–10

Start gp2-to-gp3 migration for all EBS volumes.

WAF-PERF-090

Week 11–12

Create performance debt register: capture known problems, prioritize.

WAF-PERF-100

Start with measuring (WAF-PERF-050) and database optimization (WAF-PERF-040). These two controls typically have the highest impact per unit of investment.