What is SLI? Meaning, Examples, Use Cases, and How to Measure It?

Posted on February 20, 2026 | by Rajesh Kumar

Quick Definition

An SLI (Service Level Indicator) is a quantitative measure of how well a service is performing against an expected user-facing behavior.
Analogy: An SLI is like a car’s speedometer for a delivery service — it tells you the metric you care about (speed) so you can judge whether you will meet your arrival time.
Formal technical line: An SLI is a measurable, time-bound indicator derived from telemetry that quantifies the probability that a service meets a specific user-facing requirement.

What is SLI?

What it is / what it is NOT

SLI is a narrowly scoped measurement of system behavior tied to user experience (e.g., request latency under 300ms, success rate).
SLI is NOT an SLA, which is contractual, nor an SLO, which is the objective/target set against an SLI.
SLI is NOT raw logs or traces; it is a computed metric derived from telemetry.

Key properties and constraints

Must be measurable and reproducible from telemetry.
Should map to user-visible outcomes (latency, availability, correctness).
Time window and aggregation method must be explicit.
Sensitive to sampling, instrumentation bias, and time-series retention.

Where it fits in modern cloud/SRE workflows

Foundation of reliability guardrails: SLIs feed SLOs and error budgets.
Input to automation: scaling, canary promotion, automated rollbacks, and incident escalations.
Observability and postmortem: used to determine impact and regressions.
Security and compliance: informs availability and integrity SLIs for critical services.

A text-only “diagram description” readers can visualize

Users send requests -> Edge/Load Balancer -> Service cluster (k8s/serverless) -> Backing services (DB, caches) -> Responses to users. Telemetry collectors at edge and service produce logs/metrics/traces. SLI computation aggregates telemetry by time window and labels, outputs ratios or quantiles used by SLO evaluation and alerting systems.

SLI in one sentence

An SLI is a precise, measurable statistic representing how frequently a service delivers an expected user outcome over a defined time window.

SLI vs related terms (TABLE REQUIRED)

ID	Term	How it differs from SLI	Common confusion
T1	SLO	An objective or target applied to an SLI	People call the target the metric
T2	SLA	A contractual commitment with penalties	Confusion with non-contractual SLOs
T3	Metric	Raw telemetry point that can be used to compute an SLI	Metrics are not direct SLIs until defined
T4	Error budget	Allowable failure time derived from SLO and SLI	Treated as a metric instead of a policy input
T5	Alert	Notification based on thresholds or burn rate	Alerts are not SLIs but can use SLIs

Row Details (only if any cell says “See details below”)

None

Why does SLI matter?

Business impact (revenue, trust, risk)

SLIs translate technical behavior into business signals. Availability and latency directly affect conversions, retention, and contract compliance.
Clear SLIs help quantify risk exposure and prioritize investments to protect revenue and customer trust.

Engineering impact (incident reduction, velocity)

SLIs enable focused SLOs that limit firefighting and unnecessary rollbacks by framing acceptable risk.
Error budgets enable safe experimentation and faster delivery by granting measured leeway for change.

SRE framing (SLIs/SLOs/error budgets/toil/on-call)

SLIs are the measurement inputs to SLOs. SLOs define targets; error budgets define allowable deviation; error budget policies guide on-call and release decisions.
SLIs reduce toil by enabling automated guardrails that pause risky operations when budgets burn.

3–5 realistic “what breaks in production” examples

Database index corruption causing response code 500 for 0.5% of transactions.
Downstream rate-limiting causing high tail latency above 95th percentile.
Deployment misconfiguration causing partial service degradation in one region.
Network partition increasing request error rates for a subset of users.
Cache invalidation bug causing increased DB load and elevated p95 latency.

Where is SLI used? (TABLE REQUIRED)

ID	Layer/Area	How SLI appears	Typical telemetry	Common tools
L1	Edge / CDN	Request success and latency at edge	Edge logs, RT metrics	CDN metrics, logs
L2	Network	Packet loss and RTT affecting user paths	Network metrics, flow logs	CNI metrics, network monitoring
L3	Service / API	Success rate, p95/p99 latency	HTTP metrics, traces	APM, Prometheus
L4	Application UX	Render time and error surface	Frontend RUM, browser metrics	RUM SDKs, synthetic tests
L5	Data layer	Query success and latency	DB metrics, slow queries	DB monitoring tools
L6	CI/CD	Deployment success impact on SLI	Build/deploy events, canary metrics	CI tools, deployment metrics
L7	Serverless / PaaS	Invocation success and cold starts	Invocation metrics, trace coldstart labels	Cloud provider metrics
L8	Security / Integrity	Auth success, tamper indicators	Audit logs, integrity checks	SIEM, audit logging

Row Details (only if needed)

None

When should you use SLI?

When it’s necessary

For any customer-facing system where user experience matters.
To enforce risk-based release policies using error budgets.
When contractual obligations (SLA) or regulatory requirements exist.

When it’s optional

Internal-only tooling where failure has low business impact.
Prototypes or experiments with ephemeral lifetimes and low user exposure.

When NOT to use / overuse it

Avoid defining SLIs for every internal metric; too many SLIs dilutes focus.
Don’t use SLIs for pure developer productivity metrics unrelated to user impact.

Decision checklist

If user experience is at risk and you can measure it -> define SLI and SLO.
If system affects revenue/SLAs -> SLI required.
If ephemeral or internal and low impact -> SLI optional; use lightweight monitoring.
If you cannot reliably instrument the behavior -> postpone until instrumentation exists.

Maturity ladder: Beginner -> Intermediate -> Advanced

Beginner: Define 2–3 core SLIs (availability, latency, correctness) for critical services.
Intermediate: Add per-region and per-tenant SLIs; integrate error budget policies.
Advanced: Automated remediation, canary promotion based on SLI signals, SLI-driven capacity planning, ML-assisted anomaly detection.

How does SLI work?

Components and workflow

Instrumentation: SDKs or agents emit metrics, logs, traces.
Collection: Telemetry collectors aggregate events and metrics.
Processing: Compute SLI from raw telemetry (counts, ratios, quantiles).
Storage: Time-series DB retains SLI series for evaluation and alerts.
Evaluation: Compare SLI against SLO to compute error budget and triggers.
Action: Alerts, automated controls, or postmortems are triggered.

Data flow and lifecycle

Events generated at service edge -> labeled and shipped to collectors -> pre-aggregation or raw ingest -> SLI computation pipeline (sliding window) -> SLI series stored -> SLO evaluator consumes series -> alerts/actions.

Edge cases and failure modes

Missing telemetry due to agent failures can cause false SLI drops.
Sampling bias can undercount errors, skewing SLI.
Time-window misalignment across regions leads to inconsistent SLI reporting.
Retention or aggregation loss prevents incident reconstruction.

Typical architecture patterns for SLI

Edge-first SLI – When: CDN or gateway-centric services. – Use: Measure end-to-end availability and latency at the edge.
Client-side RUM SLI – When: Web/mobile UX critical. – Use: Measure page render and interaction latencies from real users.
Server-side API SLI – When: Microservices offering API endpoints. – Use: Measure success rate and p95/p99 latency from service logs/traces.
Composite SLI – When: User journey spans multiple services. – Use: Combine multiple SLIs (edge + api + db) into a single composite SLI with weighted aggregation.
Canary-driven SLI – When: Deployments require staged rollout. – Use: Compute SLIs for canary and baseline; automate promotion based on SLI delta.

Failure modes & mitigation (TABLE REQUIRED)

ID	Failure mode	Symptom	Likely cause	Mitigation	Observability signal
F1	Missing telemetry	Sudden gap in SLI series	Agent crash or pipeline outage	Healthcheck telemetry path, fallback	Drops to null or flatline
F2	Sampling bias	Underreported errors	High sampling rate or wrong sampling	Lower sample rate for errors	Error traces ratio mismatch
F3	Time skew	Off-by-window reports	Clock drift on collectors	NTP, use ingest timestamps	Misaligned peaks across regions
F4	Aggregation error	Incorrect SLI values	Wrong aggregation logic	Validate formula, unit tests	Discrepancies in counts vs raw logs
F5	Label cardinality explosion	Slow queries, high costs	Unbounded labels from user input	Limit labels, use bucketing	High series count metric
F6	Alert storm	Many alerts for one incident	No dedupe or grouping	Grouping, suppress during maintenance	High alert volume, repeated alerts

Row Details (only if needed)

None

Key Concepts, Keywords & Terminology for SLI

Glossary (40+ terms). Each entry: Term — definition — why it matters — common pitfall

SLI — Measured indicator of user-facing behavior — Core signal for SLOs — Confused with SLO.
SLO — Target chosen for an SLI over time — Guides error budget — Treated as an SLA mistakenly.
SLA — Contractual service promise often with penalties — Legal commitment — Mistaken for internal SLO.
Error budget — Allowable failure rate derived from SLO — Enables risk-managed releases — Treated as buffer to ignore issues.
Availability — Portion of successful requests — Directly impacts user access — Measured without context can be misleading.
Latency — Time to respond to a request — Affects UX and throughput — Percentiles can hide tail latency.
Throughput — Requests per second or similar — Capacity planning input — Misused as a reliability metric.
P99/P95 — Latency percentile metrics — Highlight tail behavior — Requires sufficient sample size.
Request success rate — Ratio of successful responses — Simpler availability SLI — Needs correct status code mapping.
Quantile — A statistical measure for distribution — Useful for p95/p99 — Not additive across windows.
Time window — Aggregation period for SLI computation — Impacts responsiveness of alerts — Too long delays detection.
Aggregation method — How raw data is summarized — Affects SLI semantics — Wrong aggregation yields incorrect SLI.
Sampling — Selecting subset of telemetry — Reduces cost — Can bias SLIs if not done carefully.
Traces — Distributed trace data showing request paths — Root cause analysis — High overhead at scale.
Logs — Event records from systems — Rich context for incidents — Hard to compute SLIs directly at scale.
Metrics — Numeric time-series values — Primary SLI input — Needs consistent labels.
Labels / Tags — Key-value contextual metadata — Enables sliceable SLIs — High cardinality risk.
Cardinality — Number of distinct label combinations — Scalability concern — Can spike storage costs.
Synthetic testing — Scripted checks from controlled locations — Validates availability — Not same as real-user SLI.
RUM — Real User Monitoring from browsers/devices — Measures real UX — Privacy and sampling concerns.
Canary — Small subset deployment for testing — Reduces blast radius — Requires SLI comparison with baseline.
Deployment pipeline — CI/CD flow for releases — Integration point for SLI-driven gating — Complexity increases with policy.
Auto-remediation — Automated fixes triggered by SLI signals — Reduces toil — Risk of incorrect automation loops.
Burn rate — Speed at which error budget is consumed — Guides emergency actions — Miscalculated burn can mislead.
Dedupe — Aggregating similar alerts into one — Reduces noise — Over-dedupe may hide distinct incidents.
On-call — Team rotation for incidents — SLI alerts drive paging — Poor SLI design increases paging load.
Runbook — Step-by-step incident guide — Speeds recovery — Outdated runbooks are harmful.
Playbook — Higher-level incident strategy — Guides decision-making — Too generic to be actionable.
Postmortem — Analysis after incident — Shared learning — Blame culture reduces value.
Toil — Repetitive manual work — SLI automation reduces toil — Misidentifying toil wastes effort.
Observability — Ability to understand system state — Essential to compute reliable SLIs — Observability gaps cause blind spots.
APM — Application Performance Monitoring — Measures service metrics and traces — Can be cost-prohibitive at scale.
Throttling — Rate-limit behavior — Affects available capacity — Needs to be reflected in SLIs.
Retries — Client or proxy retries — Can mask underlying errors — SLI must consider upstream and downstream retries.
Circuit breaker — Fail fast pattern — Protects systems — Can influence SLI calculus if it hides errors.
SLI burn policy — Rules when error budget burns trigger actions — Enforces discipline — Too rigid policies can block necessary work.
Service level indicator definition — The formal SLI spec — Removes ambiguity — Vague definitions lead to misalignment.
Composite SLI — Aggregation across multiple SLIs — Provides holistic view — Weighting choices affect meaning.
Baseline — Reference behavior for canaries — Required for comparison — Bad baselines mislead rollouts.
False positive alert — Alert for non-issue — Interrupts engineers — Root cause often instrumentation.
False negative — Missing alert for real issue — Causes customer impact — Often due to sampling or thresholds.
Retention — How long telemetry is stored — Impacts postmortem depth — Short retention hinders root cause analysis.
Instrumentation drift — Metrics change meaning over time — Causes SLI misinterpretation — Version-controlled definitions needed.
SLA credits — Financial remedy for SLA breach — Affects business risk — Not automatic without contract terms.
Thundering herd — Many retries during failure causing overload — Worsens SLIs — Requires backoff and jitter.

How to Measure SLI (Metrics, SLIs, SLOs) (TABLE REQUIRED)

ID	Metric/SLI	What it tells you	How to measure	Starting target	Gotchas
M1	Availability	Fraction of successful user requests	success_count / total_count per window	99.9% for critical	Status mapping matters
M2	p95 latency	Typical upper latency users see	95th percentile of request durations	p95 < 300ms	Requires full sampling
M3	p99 latency	Tail latency affecting worst users	99th percentile of durations	p99 < 1s	High variance, needs smoothing
M4	Error rate by code	Frequency of specific failures	count(code==5xx)/total	<0.1% for critical paths	Retries mask errors
M5	Time to recovery (MTTR)	Mean time to restore functionality	avg(recovery durations)	Depends on business	Needs clear incident start/end
M6	Success rate per region	Regional availability differences	region_success/region_total	Match global SLO or region SLO	Low traffic regions noisy
M7	Cold start rate	Serverless cold start frequency	count(cold_start)/invocations	<5% for latency-sensitive	Instrumentation in platform needed
M8	Data correctness	Proportion of correct responses	correctness_count/total	99.99% for critical data	Hard to assert automatically
M9	Job completion SLI	Batch job success and on-time	ontime_success/total	99% for scheduled jobs	Schedule jitter complicates window
M10	Composite SLI	End-to-end user journey health	weighted aggregation of SLIs	99% weighted score	Weighting decisions subjective

Row Details (only if needed)

None

Best tools to measure SLI

Tool — Prometheus

What it measures for SLI: Time-series metrics, counters, histograms for latency and success rates.
Best-fit environment: Kubernetes, cloud-native microservices.
Setup outline:
Instrument services with client libraries.
Expose /metrics endpoints.
Use histograms or summaries for latency.
Deploy Prometheus with service discovery.
Record rules for SLI computations.
Strengths:
Open-source and flexible.
Powerful query language (PromQL).
Limitations:
Scaling and long-term storage require components.
Histograms need careful configuration.

Tool — OpenTelemetry

What it measures for SLI: Traces, metrics, and logs for building SLIs.
Best-fit environment: Heterogeneous systems across clouds.
Setup outline:
Add SDK to services.
Configure exporters to backend.
Standardize semantic conventions.
Ensure sampling strategy for traces.
Strengths:
Vendor neutral, rich context.
Unifies telemetry types.
Limitations:
Implementation complexity.
Sampling and cost trade-offs.

Tool — Datadog

What it measures for SLI: APM traces, metrics, RUM for frontend SLIs.
Best-fit environment: Enterprises seeking managed observability.
Setup outline:
Install agents or SDKs.
Configure APM and RUM.
Build monitors based on SLI queries.
Strengths:
Integrated dashboards and alerts.
Easy onboarding.
Limitations:
Cost can scale quickly.
Black-box agents for some workloads.

Tool — Grafana + Loki + Tempo

What it measures for SLI: Visualization of metrics, logs, traces for SLI computation.
Best-fit environment: Open-source observability stacks.
Setup outline:
Feed metrics to Prometheus or Grafana Cloud.
Use Loki for logs, Tempo for traces.
Build SLI dashboards with Grafana panels.
Strengths:
Modular and flexible.
Strong visualization.
Limitations:
Integration and maintenance overhead.
Requires operational expertise.

Tool — Cloud provider metrics (AWS/GCP/Azure)

What it measures for SLI: Platform-level metrics (API Gateway, Lambda, ALB).
Best-fit environment: Serverless or managed services.
Setup outline:
Enable provider metrics and enhanced logs.
Create metric filters for SLIs.
Export to provider monitoring or external tools.
Strengths:
Low friction for managed services.
Integrated with billing and lifecycle.
Limitations:
Metric granularity and retention vary.
Less control than self-hosted.

Recommended dashboards & alerts for SLI

Executive dashboard

Panels:
Global composite SLI and trend over 30/90 days to show business health.
Error budget remaining per critical product.
Region/tenant breakdown for major customers.
High-level incident status and MTTR trend.
Why: Quick view for product and executive stakeholders on reliability posture.

On-call dashboard

Panels:
Real-time SLI values with last 5–15 minute windows.
Active incidents and pages with links to runbooks.
Top contributing errors by service and error code.
Recent deploys and canary results.
Why: Rapid context for responders to triage and act.

Debug dashboard

Panels:
Raw request traces filtered by error or slow latency.
Per-endpoint histograms and heatmaps.
Dependency call graphs and database queue lengths.
Logs correlated with traces for implicated timeframes.
Why: Deep-dive diagnostics for engineers during incident resolution.

Alerting guidance

What should page vs ticket:
Page: SLI breach that risks violating SLO or rapid burn rate above threshold with customer impact.
Ticket: Non-urgent SLI trends that show gradually deteriorating behavior.
Burn-rate guidance:
1–3x burn rate: Investigate and apply mitigations.
5x burn rate: Escalate and implement emergency release pause.
Noise reduction tactics:
Deduplicate alerts by grouping by incident ID or trace.
Use alert suppression during planned maintenance.
Implement dynamic thresholds with historical baselines.
Use suppression windows for transient flapping conditions.

Implementation Guide (Step-by-step)

1) Prerequisites – Define stakeholders and owners. – Inventory critical user journeys and dependencies. – Ensure telemetry collectors and retention policy exist.

2) Instrumentation plan – Identify events and labels required for SLI. – Add client libraries (metrics/tracing) to services. – Define histogram buckets and status code mappings.

3) Data collection – Configure collectors, sampling, and exporters. – Verify telemetry ingestion and storage. – Implement health metrics for the telemetry pipeline.

4) SLO design – Choose SLI(s) per customer journey. – Define time windows and targets. – Map SLOs to error budget policies.

5) Dashboards – Build executive, on-call, and debug dashboards. – Provide drilldowns by region, customer, and endpoint.

6) Alerts & routing – Create SLI-based monitors and burn-rate alerts. – Route pages to on-call with escalation policy. – Separate informational tickets for trending issues.

7) Runbooks & automation – Create runbooks for common SLI breaches. – Implement automated actions (rollback, scaledown) where safe. – Ensure runbooks link to dashboards and telemetry.

8) Validation (load/chaos/game days) – Run load tests to validate SLI under expected load. – Execute chaos experiments to verify automation and alerting. – Schedule game days to rehearse incident workflows.

9) Continuous improvement – Review SLIs monthly against business changes. – Update instrumentation and refine targets. – Use postmortems to close gaps in SLI coverage.

Checklists

Pre-production checklist

Instrumentation implemented and verified.
Baseline SLI values computed with synthetic and real traffic.
Dashboards and alerts configured.
Runbooks in place for core SLIs.
Stakeholders notified of SLO targets.

Production readiness checklist

Telemetry pipeline stable and monitored.
Error budget policy defined and automated actions configured.
On-call rotation and escalation tested.
Rollback and canary tooling integrated with SLI checks.

Incident checklist specific to SLI

Confirm SLI breach and scope.
Identify impacted customer segments.
Apply runbook steps and mitigations.
Communicate status and expected recovery time.
Record timeline for postmortem and SLI evaluation.

Use Cases of SLI

Provide 8–12 use cases

Web storefront availability – Context: E-commerce platform. – Problem: Customers cannot checkout during partial outages. – Why SLI helps: Measures checkout success to prioritize fixes. – What to measure: Checkout success rate and p99 payment latency. – Typical tools: APM, RUM, payment gateway metrics.
API gateway latency – Context: Public API for third-party developers. – Problem: High tail latency causes developer complaints. – Why SLI helps: Pinpoints gateway-induced delays pre-backend. – What to measure: p95/p99 gateway latency and 5xx rate. – Typical tools: Prometheus, tracing, API gateway metrics.
Mobile app startup time – Context: Mobile consumer app. – Problem: Slow cold start increases churn. – Why SLI helps: Quantifies startup experience for releases. – What to measure: Median cold start time and crash rate on startup. – Typical tools: RUM SDK, mobile analytics.
Serverless function success – Context: Serverless backend for event processing. – Problem: Occasional cold starts and timeouts. – Why SLI helps: Monitors invocation success and cold starts. – What to measure: Invocation success rate and cold start ratio. – Typical tools: Cloud provider metrics, OpenTelemetry.
Multi-region failover – Context: Global service with active-active regions. – Problem: Region-specific outages degrade SLA for users. – Why SLI helps: Region SLIs detect imbalance and trigger failover. – What to measure: Region availability and cross-region latency. – Typical tools: Synthetic checks, global load balancer metrics.
Data pipeline timeliness – Context: Analytics ETL delivering dashboards. – Problem: Late data causes business reporting errors. – Why SLI helps: Measures job completion within SLA window. – What to measure: On-time completion percentage and lag distribution. – Typical tools: Job metrics, workflow managers.
Database query correctness – Context: Financial ledger service. – Problem: Incorrect balances due to data corruption. – Why SLI helps: Detects and quantifies incorrect responses. – What to measure: Correctness checks per transaction and reconciliation failures. – Typical tools: DB monitoring, integrity checks, custom tests.
CI/CD deployment health – Context: Frequent deployments across services. – Problem: Deployments that degrade production without detection. – Why SLI helps: Canary SLIs ensure safe promotion. – What to measure: Post-deploy error rate delta and canary vs baseline SLI. – Typical tools: CI pipelines, canary analysis tools.

Scenario Examples (Realistic, End-to-End)

Scenario #1 — Kubernetes API backend latency

Context: A microservice running on Kubernetes handles user queries.
Goal: Ensure p95 latency under 300ms for critical endpoint.
Why SLI matters here: User-facing searches must stay snappy to reduce churn.
Architecture / workflow: Ingress -> API pods (k8s) -> Redis cache -> Postgres. Prometheus and OpenTelemetry collect metrics and traces.
Step-by-step implementation:

Instrument endpoint with latency histograms and status codes.
Export metrics to Prometheus; traces to Tempo.
Define SLI: p95(latency) over 5m windows.
Set SLO: p95 <300ms monthly target 99%.
Create canary checks; gate deployments if canary SLI degrades >2x baseline.
Add alerting: page at SLI breach >5% error budget burn in 1h. What to measure: p95, request rate, cache hit rate, DB p95.
Tools to use and why: Prometheus for metrics, Grafana dashboards, Tempo for traces.
Common pitfalls: Histogram buckets misconfigured; high-cardinality labels.
Validation: Load test and simulate DB slow queries during canary.
Outcome: Reduced tail latency and faster rollback decisions.

Scenario #2 — Serverless image processing pipeline (serverless/PaaS)

Context: Image resizing pipeline on managed serverless functions.
Goal: Keep invocation success rate above 99.9 and cold start ratio under 5%.
Why SLI matters here: Customers expect timely asset availability; delays impact page loads.
Architecture / workflow: Upload -> Event triggers Lambda -> Resize -> Store in object storage; Cloud metrics and custom tracing.
Step-by-step implementation:

Instrument functions for success/failure and cold start.
Use cloud provider metrics and custom metrics exported to a monitoring platform.
Define SLI: success_rate over 30m; cold_start_rate per function.
SLOs: success_rate 99.9% monthly; cold_start_rate <5% daily.
Alert if success_rate drops or cold starts spike for sustained periods. What to measure: Invocation counts, failures, duration, cold starts.
Tools to use and why: Provider metrics, OpenTelemetry, managed dashboards.
Common pitfalls: Provider metric granularity and retention; hidden retries.
Validation: Simulated burst loads and observe cold start behavior.
Outcome: Improved latency and predictable scaling behavior.

Scenario #3 — Incident response and postmortem SLI use

Context: Service experienced degraded availability for 30 minutes across EU region.
Goal: Determine impact and improve runbooks to prevent recurrence.
Why SLI matters here: Quantifies customer impact and supports remediation prioritization.
Architecture / workflow: SLI computations from edge logs show region drops; error budget consumed.
Step-by-step implementation:

Fetch SLI series and error budget burn during incident.
Correlate with deploys and infra alerts.
Execute runbook steps to failover traffic to another region.
Postmortem: compute customer-minutes lost and corrective actions. What to measure: Regional availability, failover time, MTTR.
Tools to use and why: Dashboards, incident management, logs and traces.
Common pitfalls: Missing telemetry in region; unclear incident start time.
Validation: Tabletop exercises and scheduled failover drills.
Outcome: Clear remediation, improved runbook, and adjusted SLO.

Scenario #4 — Cost vs performance trade-off in caching

Context: High read traffic service considers reducing cache TTL to lower cache costs.
Goal: Balance cache hit rate decrease with acceptable latency impact.
Why SLI matters here: Shows customer impact of cost-saving measure and informs decision.
Architecture / workflow: Requests -> CDN -> App cache -> DB. Telemetry for cache hits, p95 latency.
Step-by-step implementation:

Baseline SLIs for p95 latency and hit rate.
Run controlled experiment reducing TTL in canary.
Measure SLI delta and error budget impact.
If SLI stays within SLO, scale change; else roll back. What to measure: Cache hit rate SLI, p95 latency, DB CPU usage.
Tools to use and why: APM, CDN metrics, Prometheus.
Common pitfalls: Not segmenting by payload size; ignoring tail users.
Validation: Real-user monitoring and synthetic tests.
Outcome: Informed decision balancing cost and UX.

Common Mistakes, Anti-patterns, and Troubleshooting

List of 20 common mistakes with symptom -> root cause -> fix

Symptom: SLI flatlines to null. Root cause: Telemetry pipeline outage. Fix: Implement telemetry health checks and alert on pipeline gaps.
Symptom: Alert storm on single incident. Root cause: No dedupe/grouping. Fix: Group alerts by incident and dedupe identical errors.
Symptom: SLIs show no errors despite complaints. Root cause: Instrumentation not covering user path. Fix: Add client-side RUM or edge instrumentation.
Symptom: SLI improves after adding retries. Root cause: Retries masking upstream errors. Fix: Measure upstream error rate before retries.
Symptom: High cost for metrics retention. Root cause: Unbounded cardinality or too-high resolution. Fix: Reduce cardinality and downsample older data.
Symptom: SLO repeatedly missed but teams ignore it. Root cause: Lack of error budget policy. Fix: Define actions at burn thresholds and enforce them.
Symptom: False positives in alerts. Root cause: Thresholds too tight vs baseline. Fix: Use statistical baselines and anomaly detection.
Symptom: False negatives — missed incidents. Root cause: Too much sampling. Fix: Increase sampling for error paths.
Symptom: Different SLI values across tools. Root cause: Inconsistent aggregation windows or time alignment. Fix: Standardize windowing and timestamp semantics.
Symptom: High-cardinality metrics blow quota. Root cause: Using user IDs as labels. Fix: Hash or bucket user IDs or use label values sparingly.
Symptom: Postmortem lacks SLI data. Root cause: Short retention. Fix: Extend retention for critical SLI series or export to long-term storage.
Symptom: Canary passes but production degrades. Root cause: Canary not representative. Fix: Improve canary traffic selection and baseline matching.
Symptom: SLI computed differently by teams. Root cause: No central SLI definition registry. Fix: Create and enforce centralized SLI definitions.
Symptom: Alerts during maintenance windows. Root cause: No suppression in CI/CD. Fix: Automate suppression and schedule maintenance windows.
Symptom: SLI target set arbitrarily. Root cause: No stakeholder alignment. Fix: Align SLOs with business stakeholders and data.
Symptom: Slow alerts for urgent issues. Root cause: Long aggregation window. Fix: Use multiple windows for detection and confirmation.
Symptom: Overuse of SLIs for internal metrics. Root cause: Equating operational metrics with user impact. Fix: Prioritize user-facing SLIs.
Symptom: High tail latency unnoticed. Root cause: Only monitoring medians. Fix: Add p95/p99 SLIs and track tail behavior.
Symptom: Instrumentation changes break SLI continuity. Root cause: Metric renaming without migration. Fix: Maintain compatibility and map old metrics.
Symptom: Security incidents not captured. Root cause: No integrity SLIs. Fix: Define SLIs for auth success rates and audit integrity.

Observability pitfalls (at least 5 included above)

Missing instrumentation, sampling bias, retention limits, high cardinality, inconsistent aggregation.

Best Practices & Operating Model

Ownership and on-call

Team owning the service also owns SLIs, SLOs, and error budgets.
On-call rotations should include a reliability role that monitors error budget consumption.

Runbooks vs playbooks

Runbooks: Step-by-step procedures for known failure modes.
Playbooks: Higher-level decision guides (e.g., when to failover).
Keep runbooks version controlled and runnable by on-call.

Safe deployments (canary/rollback)

Use canaries with SLI comparison against baseline.
Automate rollback if canary SLI deviates beyond threshold.
Gradual rollout with automatic pause at SLI risk thresholds.

Toil reduction and automation

Automate remediation for known and safe fixes.
Reduce repetitive incident tasks with runbook automation and chatops.
Monitor automation actions to avoid cascading failures.

Security basics

Protect telemetry pipelines and ensure integrity of SLI data.
Audit access to SLI dashboards and SLO definitions.
Mask or aggregate sensitive labels to meet privacy requirements.

Weekly/monthly routines

Weekly: Review active error budget consumption and recent anomalies.
Monthly: Review SLO health, adjust targets, and validate SLIs against business needs.

What to review in postmortems related to SLI

Confirm SLI definition correctness for incident.
Verify telemetry completeness and gaps.
Calculate customer impact using SLIs and error budget consumption.
Action items to instrument gaps, change SLOs, or adjust alerting.

Tooling & Integration Map for SLI (TABLE REQUIRED)

ID	Category	What it does	Key integrations	Notes
I1	Metrics store	Stores time-series metrics	Prometheus, Grafana, Cortex	Central SLI storage
I2	Tracing	Distributed request tracing	OpenTelemetry, Tempo	Correlate errors to traces
I3	Logging	Central logs for context	Loki, ELK	Useful for root cause
I4	RUM / Synthetic	Real-user and synthetic checks	RUM SDKs, synthetic runners	Critical for frontend SLIs
I5	Alerting / Pager	Pages on SLI breaches	Opsgenie, PagerDuty	Integrate burn-rate alerts
I6	CI/CD	Integrates canary gating	Jenkins, GitHub Actions	Automate SLI checks pre-promotion
I7	Incident mgmt	Track incidents and postmortems	Jira, PagerDuty incidents	Link to SLI metrics
I8	Policy engine	Enforce error budget rules	Custom or policy services	Automate release block
I9	Cloud provider metrics	Native platform metrics	CloudWatch, Stackdriver	Easy access for managed services
I10	Long-term storage	Archive SLI series	Object storage, remote TSDB	For postmortem audits

Row Details (only if needed)

None

Frequently Asked Questions (FAQs)

What is the difference between an SLI and an SLO?

An SLI is the measured indicator; an SLO is the performance target applied to that indicator over time.

How many SLIs should a service have?

Start with 2–3 primary SLIs focused on availability, latency, or correctness; expand only as needed.

How long should SLI retention be?

Depends on business needs; critical services often keep SLI series for months to years for postmortems.

Should we use client-side or server-side SLIs?

Use both: client-side RUM for real-user experience, server-side for backend behavior and reproducibility.

Can SLIs be computed from logs?

Yes, but ensure parsers and pipelines are reliable and performant.

How do we avoid high cardinality in SLI labels?

Limit label keys, bucket values, and hash or aggregate identifiers.

How to set SLO targets?

Align with business stakeholders using historical SLI data and customer impact trade-offs.

What granularity should SLI windows use?

Use multiple windows: short (5–15m) for paging and long (30d) for SLO evaluation.

How to handle noisy SLIs?

Apply smoothing, require confirmation windows, and tune alert thresholds.

Are composite SLIs useful?

Yes for end-to-end journeys but require careful weighting and interpretation.

How to incorporate SLIs into CI/CD?

Gate promotions with canary SLI checks and block releases if error budget burn is high.

How to measure correctness SLIs?

Use periodic probes, checksum comparisons, or reconciliation jobs to assert correctness.

What role does sampling play?

Sampling reduces cost but must preserve error paths and tail latency for SLIs.

How to report SLI breaches to customers?

Use transparent incident reports with quantified SLI impact and remediation steps.

When should SLOs change?

When product behavior, user expectations, or business priorities change; adjust with stakeholder approval.

How to handle low-traffic SLIs?

Use longer aggregation windows and synthetic tests to reduce noise.

Do SLIs apply to internal tooling?

Only if user experience or business processes depend on that tooling; otherwise use lightweight monitoring.

How to secure SLI telemetry?

Encrypt in transit, restrict access, and audit SLI definition changes.

Conclusion

SLIs are the measurable bridge between engineering telemetry and customer experience. Properly defined SLIs enable reliable SLOs, enforceable error budgets, and safer delivery processes. They require good instrumentation, consistent computation, and an operating model that ties metrics to decisions.

Next 7 days plan (5 bullets)

Day 1: Inventory critical user journeys and map potential SLIs.
Day 2: Implement basic instrumentation for 1–2 critical endpoints.
Day 3: Configure metric collection and build an on-call dashboard.
Day 4: Define SLOs and error budget policies with stakeholders.
Day 5–7: Run a canary deployment and validate SLI-driven gating and alerts.

Appendix — SLI Keyword Cluster (SEO)

Primary keywords

SLI
Service Level Indicator
SLI definition
SLI vs SLO
SLI measurement

Secondary keywords

service reliability indicators
SLI examples
SLIs for microservices
SLI in Kubernetes
SLI for serverless

Long-tail questions

what is an SLI in SRE
how to calculate an SLI
difference between SLI and SLO and SLA
best tools for measuring SLIs in 2026
how to set SLO targets based on SLIs
how to measure SLI for a web application
how to create SLI dashboards for on-call
how to define SLI for multi-region services
how to avoid high-cardinality in SLI labels
how to compute p99 latency SLI accurately
are SLIs required for internal services
how to use SLIs in CI CD pipelines
how to automate rollbacks based on SLIs
how to measure SLI for serverless functions
how to set error budgets using SLIs
how to correlate SLIs with business metrics
how to validate SLI instrumentation
how to choose SLI aggregation windows
how to use SLI burn rate thresholds
how to troubleshoot SLI discrepancies

Related terminology

SLO
SLA
error budget
availability SLI
latency SLI
p95 SLI
p99 SLI
success rate SLI
composite SLI
synthetic monitoring
RUM
OpenTelemetry
Prometheus
Grafana
canary deployment
burn rate
runbook
postmortem
observability
telemetry pipeline
metrics retention
cardinality
sampling
tracing
logging
APM
CI/CD gating
automated rollback
chaos engineering
game days
MTTR
mean time to detect
error budget policy
incident response
SLA credits
platform metrics
managed services
serverless cold start
deployment canary
synthetic checks
anomaly detection
alert deduplication
data correctness SLI
retention policy
instrumentation drift
SLIs for security
SLI governance
SLI registry
SLI formula standardization