What is SLI? Meaning, Architecture, Examples, Use Cases, and How to Measure It (2026 Guide)

Terminology
Posted on | by Rajesh Kumar

Quick Definition (30–60 words)

An SLI (Service Level Indicator) is a measurable quantitative metric representing user-perceived service quality. Analogy: SLI is the speedometer showing how a car performs for a trip. Formal: An SLI is a defined telemetry-derived ratio or value used to evaluate compliance with an SLO over a measurement window.

What is SLI?

What it is / what it is NOT

  • An SLI is a precise metric tied to user experience or system health.
  • It is NOT an SLA, an SLO, or an incident report; those are derived artifacts or contracts.
  • It is NOT raw unbounded telemetry; it is a curated measurement with defined numerator, denominator, and window.

Key properties and constraints

  • Objective and measurable: has exact computation.
  • User-centric: ideally maps to end-user experience.
  • Time-bounded: evaluated over fixed windows (e.g., 7d, 30d).
  • Aggregation-aware: must define how to aggregate (avg, percentile, ratio).
  • Sampling and cardinality constraints: must account for sampling bias and high-cardinality dimensions.
  • Privacy and security constraints: telemetry must be collected under privacy and compliance rules.

Where it fits in modern cloud/SRE workflows

  • Observability layer: computed from logs, traces, metrics, events.
  • SLO governance: feeds SLOs and error budgets.
  • CI/CD and deployment gating: used to validate releases and can block rollouts.
  • Incident response: triggers alerts and informs postmortems.
  • Capacity and cost decisions: guides trade-offs between cost and customer experience.

A text-only “diagram description” readers can visualize

  • Users make requests -> Requests pass through edge and load balancer -> Requests routed to services or serverless functions -> Backend services query databases and caches -> Observability agents collect metrics, logs, and traces -> Metrics pipeline aggregates and computes SLIs -> SLO evaluation and alerting engines consume SLIs -> Dashboards and on-call systems present results.

SLI in one sentence

An SLI is a defined, reproducible metric that quantifies a critical aspect of user experience or system reliability for use in SLO evaluation and operational decisions.

SLI vs related terms (TABLE REQUIRED)

ID Term How it differs from SLI Common confusion
T1 SLO SLO is a target based on SLIs Confused as raw metric
T2 SLA SLA is a contractual promise with penalties Confused as identical to SLO
T3 Error Budget Budget derived from SLO using SLIs Mistaken for alert rule
T4 Metric Raw telemetry point not always user-centric Thought to equal SLI always
T5 Alert Operational signal triggered by thresholds Considered same as SLI
T6 KPI Business metric often broader than SLI Overlaps without precision
T7 Trace Request-level path data, not aggregated SLI Mistaken as SLI source only
T8 Log Entry of events used to compute SLI Treated as SLI itself
T9 Observability Entire practice including SLIs Misread as only tooling
T10 Telemetry All collected signals from systems Used interchangeably with SLI

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

  • None

Why does SLI matter?

Business impact (revenue, trust, risk)

  • Revenue protection: Better SLIs reduce customer-facing failures that directly harm revenue.
  • Trust and churn: Transparent SLI-based targets help retain customers by setting expectations.
  • Contractual and legal risk: SLIs feed SLOs and SLAs, which can have financial implications.

Engineering impact (incident reduction, velocity)

  • Focused troubleshooting: SLIs narrow down what user-facing quality changed.
  • Prioritization: Error budgets enable pragmatic trade-offs between reliability work and features.
  • Reduced toil: Automated SLI measurement helps prevent repetitive manual status checks.

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

  • SLI -> SLO: SLIs define the measurement; SLOs define what is acceptable.
  • Error budget: The allowance of unreliability calculated from SLO and observed SLI.
  • Toil reduction: Use SLIs to identify and automate repetitive operational work.
  • On-call: SLIs influence paging rules and runbooks.

3–5 realistic “what breaks in production” examples

  • Authentication latency spikes cause user logins to fail, reducing successful logins per minute SLI.
  • Cache eviction bug increases backend DB queries, drop in request success SLI.
  • Deployment misconfiguration causes 503s at edge, triggering availability SLI degradation.
  • Provider outage increases storage read errors, impacting data-retrieval SLI.
  • CI pipeline change introduces a regression that increases error rates for a key endpoint SLI.

Where is SLI used? (TABLE REQUIRED)

ID Layer/Area How SLI appears Typical telemetry Common tools
L1 Edge/Network Request success ratio at ingress Status codes latency Metrics exporter tracing
L2 Service/API API availability and latency Request duration counts APM metrics traces
L3 Application Feature response correctness Business event counts logs Instrumentation SDKs
L4 Data/Storage Read consistency and latency DB ops metrics errors DB telemetry exporters
L5 Kubernetes Pod readiness and request success Pod metrics events K8s metrics server
L6 Serverless/PaaS Invocation success and duration Invocation counts errors Platform metrics
L7 CI/CD Deployment success rate Build duration statuses Pipeline metrics
L8 Security Auth success and integrity checks Audit logs alerts SIEM metrics
L9 Observability Telemetry completeness Telemetry ingestion rates Observability platforms

Row Details (only if needed)

  • None

When should you use SLI?

When it’s necessary

  • Customer-facing services where user experience matters.
  • When you have an SLO or contractual SLA to measure.
  • When teams need objective criteria for incidents and releases.

When it’s optional

  • Internal tooling with low business impact.
  • Early prototypes where feature validation precedes reliability investment.

When NOT to use / overuse it

  • For every internal metric without user impact; over-instrumentation causes noise.
  • As a manager’s vanity metric; SLI must map to user value.
  • Using SLIs to micro-manage engineers rather than to enable decisions.

Decision checklist

  • If user transactions impact revenue AND are repeatable -> instrument SLIs.
  • If metric directly reflects user experience AND is automatable -> convert to SLI.
  • If metric is noisy and not actionable -> do not make it an SLI.

Maturity ladder: Beginner -> Intermediate -> Advanced

  • Beginner: Measure a small set of availability and latency SLIs for core APIs.
  • Intermediate: Add business SLIs, error budgets, and automated alerts.
  • Advanced: Multi-dimensional SLIs with cardinality slicing, adaptive alerting, and CI/CD gating.

How does SLI work?

Explain step-by-step

  • Define user journeys and select candidate signals.
  • Specify exact SLI computation: numerator, denominator, window, aggregation.
  • Instrument code and infrastructure to emit consistent telemetry.
  • Ingest telemetry into a pipeline that normalizes and computes SLIs.
  • Store SLI time series and evaluate against SLO windows and error budgets.
  • Trigger alerts, dashboards, and automation when thresholds are crossed.
  • Feed results into postmortems, runbooks, and release criteria.

Components and workflow

  • Instrumentation SDKs and agents.
  • Telemetry collector and metrics pipeline.
  • SLI computation engine (aggregation, filters).
  • Storage for raw and aggregated data.
  • Alerting and notification systems.
  • Dashboards and reporting.
  • Governance and review processes.

Data flow and lifecycle

  • Generation: Service emits telemetry.
  • Collection: Agents gather metrics/logs/traces.
  • Transport: Buffered and sent to backend.
  • Aggregation: Compute raw metrics and SLI ratios.
  • Retention: Store for evaluation and compliance.
  • Consumption: Alerts, dashboards, and reports.

Edge cases and failure modes

  • Sampling bias leading to incorrect SLI calculation.
  • Clock skew causing window misalignment.
  • Partitioned telemetry ingestion where some events are lost.
  • High-cardinality labels exploding storage and skewing aggregates.

Typical architecture patterns for SLI

  • Inline SLI instrumentation: Services emit precomputed SLI counters (useful when telemetry ingestion is unreliable).
  • Centralized aggregation: Collect raw telemetry centrally and compute SLIs in the backend (best for consistency and complex slicing).
  • Hybrid: Pre-aggregate simple counters at the edge and compute complex SLIs centrally.
  • Trace-derived SLIs: Compute SLIs from distributed traces for request-level accuracy; use when latency components matter.
  • Sampling-aware SLIs: Apply calibrated sampling with inverse weighting for high-throughput services.

Failure modes & mitigation (TABLE REQUIRED)

ID Failure mode Symptom Likely cause Mitigation Observability signal
F1 Missing telemetry Sudden SLI gap Agent failure or pipeline outage Fallback counters and retry Drop in ingestion rate
F2 Skewed sampling SLI differs from reality Sampling bias in agents Use stratified sampling Discrepancy between logs and metrics
F3 High cardinality Metric ingestion cost spike Unbounded labels used Limit labels and rollups Increased cardinality metric
F4 Clock drift Misaligned windows NTP failure or container drift Use server-side timestamps Time offset alerts
F5 Aggregation errors Incorrect SLI values Incorrect query logic Test queries and unit tests Unexpected baseline shifts
F6 Provider quota Incomplete data set Rate limiting by backend Throttle and buffer metrics Throttling counters rise
F7 Data loss Lower denominator or numerator Network drops or storage full Retry and buffering Packet loss and retry logs

Row Details (only if needed)

  • None

Key Concepts, Keywords & Terminology for SLI

Glossary of 40+ terms. Each line: Term — 1–2 line definition — why it matters — common pitfall

  • SLI — Quantitative indicator for service quality — Basis of SLOs — Mistaking raw metrics for SLIs
  • SLO — Target goal using SLIs over a window — Drives operational decisions — Set unrealistic targets
  • SLA — Contractual agreement often with penalties — Legal and commercial obligations — Confusing SLA with SLO
  • Error budget — Allowance for unreliability (1 – SLO) — Enables trade-offs — Burning without governance
  • Availability — Fraction of successful requests — Directly impacts users — Counting healthy checks not real traffic
  • Latency — Time for request to complete — Affects perceived performance — Using mean instead of p95/p99
  • Throughput — Requests per second or transactions — Capacity planning input — Ignoring burst behavior
  • Reliability — Ability to perform under expected conditions — Business continuity measure — Undefined per user impact
  • Observability — Practice of instrumenting for debugging — Enables SLI computation — Collecting data without context
  • Telemetry — Logs metrics traces and events — Raw inputs for SLIs — Unstructured logs used as sole SLI source
  • Metric — Numeric measurement over time — Common SLI source — Not always user-centric
  • Trace — End-to-end recorded request path — Helps root cause analysis — High storage cost
  • Log — Event records for systems and apps — Useful for deriving SLIs — Unindexed logs are unusable
  • Cardinality — Count of unique label values — Affects storage and query perf — Unbounded labels cause explosion
  • Aggregation window — Time period for SLI evaluation — Defines responsiveness — Too short causes noise
  • Rolling window — Continuous window over recent time — Smoothens short spikes — Misconfigured leads to missed regressions
  • Quantile — p50 p95 p99 latency percentiles — Captures tail behavior — Misinterpreting quantiles as averages
  • Histogram — Buckets of latency or value frequency — Enables quantiles — Requires correct bucketing
  • Sample rate — Fraction of events collected — Reduces cost — Uncompensated sampling biases SLIs
  • Instrumentation — Adding telemetry to code — Enables accurate SLIs — Ad-hoc instrumentation causes inconsistency
  • Service level — User-visible capability metric — Aligns engineering with business — Too many service levels dilute focus
  • Burn rate — Speed at which error budget is consumed — Drives paging policies — Overreacting to short bursts
  • Canary — Gradual rollout approach — Limits blast radius — Poor canary criteria can miss issues
  • Rollback — Revert deployment on failure — Limits user impact — Manual rollback delays mitigation
  • On-call — Responsible responder for incidents — Ensures fast reaction — Over-notification causing fatigue
  • Runbook — Playbook for common incidents — Reduces time to mitigate — Stale runbooks create confusion
  • Playbook — Structured operational actions for events — Guides responders — Too generic to be actionable
  • Root cause — Primary factor causing incident — Enables fixes — Symptom-focused analysis
  • Postmortem — Blameless incident analysis — Drives learning — Skips action items
  • Noise — Non-actionable alerts and metrics — Reduces signal-to-noise — Poor thresholds and filters
  • Deduplication — Grouping similar alerts — Reduces overload — Over-deduping hides unique issues
  • SLA credit — Compensation for breach of SLA — Protects customers — Misalignment with SLOs
  • Drift — Deviation from expected behavior — Early indicator of regression — Often ignored until severe
  • Regression — New change causing degradation — Deployment guardrails detect it — Fixing without root cause
  • Synthetic monitoring — Simulated user requests — Early detection of outages — Can be unrepresentative
  • Real-user monitoring — Actual user experience capture — True SLI source — Privacy constraints can limit collection
  • Adaptive alerting — Alerts based on learned baselines — Reduces false positives — Requires training data
  • Post-deployment validation — Tests after releases to validate SLI — Prevents regressions — Often skipped under time pressure

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

ID Metric/SLI What it tells you How to measure Starting target Gotchas
M1 Request success ratio Availability as experienced by users Successful responses divided by total requests 99.9% for critical APIs Need stable denominator
M2 P95 latency Tail latency experienced by most users 95th percentile of request durations 200ms for UI APIs P95 hides p99 issues
M3 Error rate Fraction of requests failing Failed requests divided by total 0.1% for core services Define what counts as failure
M4 End-to-end success Transaction completion rate Successful workflows divided by attempts 99% for checkout flows Complex workflows need composition
M5 Time to first byte Perceived page load start TTFB measurement from real users 100ms for edge CDN CDN caching changes semantics
M6 Cache hit ratio Read request off-cache vs origin Hits divided by total lookups 95% for read-heavy services Warm-up periods skew results
M7 DB query latency DB response time affecting apps p95 of DB query durations 50ms for primary indices Index changes shift baselines
M8 Job success rate Background job completion Successful jobs divided by queued jobs 99% for critical jobs Idempotency affects retries
M9 Telemetry completeness Health of monitoring pipeline Received telemetry divided by expected 99% ingestion rate Sampling hides missing segments
M10 Synthetic availability External synthetic UX success Synthetic checks succeeded divided by total 99.95% for global pages Synthetic differs from real users

Row Details (only if needed)

  • None

Best tools to measure SLI

Provide 5–10 tools.

Tool — Prometheus + Thanos

  • What it measures for SLI: Time series metrics and aggregations for SLIs.
  • Best-fit environment: Kubernetes and cloud-native clusters.
  • Setup outline:
  • Instrument services with client libraries.
  • Deploy Prometheus for scraping.
  • Configure recording rules for SLI computations.
  • Use Thanos for long-term storage and global queries.
  • Expose metrics to alerting and dashboards.
  • Strengths:
  • Open and flexible.
  • Strong ecosystem for K8s.
  • Limitations:
  • High cardinality costs.
  • Long-term storage needs separate stack.

Tool — OpenTelemetry + Observability backends

  • What it measures for SLI: Traces metrics and logs for composite SLIs.
  • Best-fit environment: Polyglot microservices and distributed systems.
  • Setup outline:
  • Instrument using OpenTelemetry SDKs.
  • Configure collectors and exporters.
  • Define metrics from spans and logs.
  • Use backends for SLI queries.
  • Strengths:
  • Unified telemetry model.
  • Vendor-agnostic.
  • Limitations:
  • Maturity differences across languages.
  • Requires backend capabilities for SLI queries.

Tool — Cloud provider managed metrics (e.g., cloud metrics platforms)

  • What it measures for SLI: Platform-level metrics like invocation counts and errors.
  • Best-fit environment: Serverless and managed PaaS.
  • Setup outline:
  • Enable platform metrics and logging.
  • Define metric filters and dashboards.
  • Export or compute SLIs in provider console or external system.
  • Strengths:
  • Easy startup with minimal instrumentation.
  • Integrated with provider features.
  • Limitations:
  • Limited customization and sampling controls.

Tool — APM platforms (application performance monitoring)

  • What it measures for SLI: Request-level latency, errors, and traces.
  • Best-fit environment: Web applications and services needing deep traces.
  • Setup outline:
  • Install APM agents.
  • Configure transactions and error grouping.
  • Create SLI computations using APM metrics.
  • Strengths:
  • Rich UI for traces and correlations.
  • Helpful for root cause analysis.
  • Limitations:
  • Cost at scale.
  • Vendor lock-in concerns.

Tool — Logging and analytics (ELK, ClickHouse)

  • What it measures for SLI: Derive business SLIs from event logs and outcomes.
  • Best-fit environment: Event-driven and batch systems.
  • Setup outline:
  • Structure logs with consistent fields.
  • Configure ingestion and indices.
  • Create queries that compute numerators and denominators.
  • Strengths:
  • Flexible queries for complex business SLIs.
  • Good for ad-hoc analysis.
  • Limitations:
  • Retention and query cost.
  • Latency for real-time SLIs.

Recommended dashboards & alerts for SLI

Executive dashboard

  • Panels:
  • Overall SLO compliance percentage across services.
  • Top error budget burners.
  • Business transaction SLIs (e.g., checkout success).
  • Trend lines for 7d and 30d windows.
  • Why: Provides leadership with high-level reliability posture.

On-call dashboard

  • Panels:
  • Current alerting SLI violations.
  • Error budget burn rate.
  • Recent incidents list and status.
  • Real-time traces for failing requests.
  • Why: Focuses responders on actionable items.

Debug dashboard

  • Panels:
  • Request breakdown by endpoint and latency bucket.
  • Top root cause traces and error logs.
  • Resource utilization correlated with SLI degradation.
  • Telemetry ingestion health.
  • Why: Enables rapid diagnosis and remediation.

Alerting guidance

  • What should page vs ticket:
  • Page: High severity SLI breach impacting many users or critical flows and rapid burn rate.
  • Ticket: Non-critical SLI degradation or slow burn not requiring immediate human action.
  • Burn-rate guidance (if applicable):
  • Page if burn rate > 4x expected and projected to exhaust budget in the next 24 hours.
  • Use multi-window burn-rate checks (e.g., 1h and 24h) to avoid flapping.
  • Noise reduction tactics:
  • Deduplicate similar alerts by service and root cause.
  • Group alerts by namespace, region, or feature.
  • Temporarily suppress alerts during validated maintenance windows.

Implementation Guide (Step-by-step)

1) Prerequisites – Defined user journeys and ownership. – Baseline observability stack and access controls. – Team agreement on SLO targets and governance.

2) Instrumentation plan – Identify candidate SLIs per journey. – Define exact numerator and denominator and labels. – Choose sampling rate and labels to include. – Add instrumentation to code and libraries.

3) Data collection – Deploy collectors and configure exporters. – Ensure buffering, retries, and quotas are handled. – Validate ingestion and retention policies.

4) SLO design – Select SLO windows and targets (e.g., 7d/30d). – Create error budgets and burn-rate rules. – Define alerting thresholds tied to error budgets.

5) Dashboards – Build executive, on-call, and debug dashboards. – Create SLI time-series and slices for dimensions.

6) Alerts & routing – Define paging rules and escalation policies. – Integrate with incident management and runbooks.

7) Runbooks & automation – Author playbooks for common SLI violations. – Automate rollbacks, canary promotion, and throttling where safe.

8) Validation (load/chaos/game days) – Run load tests and game days against SLOs. – Simulate telemetry outages and validate fallback counters.

9) Continuous improvement – Review SLI performance in weekly reliability reviews. – Iterate on SLI definitions and thresholds.

Include checklists

Pre-production checklist

  • Defined SLI numerator and denominator for each critical journey.
  • Instrumentation added and tested in staging.
  • Telemetry ingestion validated and alerts configured.
  • Runbook created for immediate page scenarios.
  • Team ownership assigned.

Production readiness checklist

  • SLI computed in production for 7d baseline.
  • Dashboards accessible to stakeholders.
  • Alert thresholds validated under load.
  • Error budget workflows enabled.
  • Access control and data retention reviewed.

Incident checklist specific to SLI

  • Verify SLI computation correctness immediately.
  • Check telemetry ingestion health.
  • Identify whether breach is due to code, infra, or provider.
  • Use playbooks to mitigate and create tickets for fixes.
  • Capture timeline and root cause for postmortem.

Use Cases of SLI

Provide 8–12 use cases.

1) Public API availability – Context: External customers depend on API endpoints. – Problem: Frequent transient errors reduce trust. – Why SLI helps: Quantifies availability and tracks trends. – What to measure: Request success ratio and p99 latency. – Typical tools: Prometheus, APM, API gateway metrics.

2) Checkout flow reliability – Context: E‑commerce critical business flow. – Problem: Partial failures reduce conversion. – Why SLI helps: Measures end-to-end business success. – What to measure: Checkout completion rate and payment success. – Typical tools: Event logs, transaction tracing, analytics DB.

3) Search latency for UI – Context: Search must be responsive for adoption. – Problem: Slow searches degrade UX. – Why SLI helps: Guides caching and indexing priorities. – What to measure: p95 search response time and empty-result rate. – Typical tools: APM, CDN metrics, search analytics.

4) Background job processing – Context: Jobs transform data and must complete within SLA. – Problem: Backlog growth and missed deadlines. – Why SLI helps: Measures job success rate and latency. – What to measure: Job success ratio and queue time p95. – Typical tools: Queue monitoring, metrics exporters.

5) Database read consistency – Context: Multi-region replicas with eventual consistency. – Problem: Stale reads affect business logic. – Why SLI helps: Quantify inconsistency incidents. – What to measure: Freshness window success ratio. – Typical tools: DB metrics, synthetic reads.

6) CDN cache health – Context: Global static content delivery. – Problem: Cache misses increase origin load and cost. – Why SLI helps: Balances cost vs performance. – What to measure: Cache hit ratio and origin load. – Typical tools: CDN metrics and edge logs.

7) Serverless function latency – Context: Scale-to-zero functions with cold start impacts. – Problem: Cold starts cause latency spikes. – Why SLI helps: Measure user impact and cost trade-off. – What to measure: Invocation p95 latency and cold start rate. – Typical tools: Provider metrics and OpenTelemetry.

8) Telemetry pipeline health – Context: Observability depends on reliable telemetry. – Problem: Missing telemetry reduces confidence in SLIs. – Why SLI helps: Ensures monitoring is trustworthy. – What to measure: Telemetry ingestion completeness and tail latency. – Typical tools: Monitoring platform internal metrics.

9) Security authentication flow – Context: SSO and auth checks for all users. – Problem: Auth failures block all activity. – Why SLI helps: Detects systemic auth regressions quickly. – What to measure: Auth success ratio and latency. – Typical tools: SIEM, auth provider metrics.

10) Feature rollout gating – Context: New features deployed via feature flags. – Problem: New release causes performance regressions. – Why SLI helps: Gate promotion using SLI thresholds. – What to measure: Feature-specific error rate and latency. – Typical tools: Telemetry with labels, feature flag platform.

Scenario Examples (Realistic, End-to-End)

Scenario #1 — Kubernetes API Latency Regression

Context: Microservices on Kubernetes serving user-facing APIs.
Goal: Detect and limit API latency regressions post-deploy.
Why SLI matters here: A latency increase degrades UX across services.
Architecture / workflow: Ingress -> Service -> Pods -> DB; Prometheus scrapes pods; Thanos stores metrics.
Step-by-step implementation:

  • Define SLI: p95 request duration per API path.
  • Instrument HTTP handlers to expose duration histogram.
  • Configure Prometheus recording rule for p95.
  • Add an SLO: p95 < 200ms over 7d at 99.5%.
  • Set burn-rate alerts and canary gating in CI/CD. What to measure: p95 per path, error rate, pod CPU/memory.
    Tools to use and why: Prometheus for metrics, Grafana for dashboards, CI to block promotion.
    Common pitfalls: High cardinality labels on user id; sampling of traces hide tail.
    Validation: Run load tests and canaries to confirm SLI stable.
    Outcome: Automated rollback on canary when p95 breach predicted.

Scenario #2 — Serverless Checkout Cold-Start Impact (Serverless/PaaS)

Context: Checkout flow implemented as serverless functions with low baseline traffic.
Goal: Ensure checkout latency remains acceptable while minimizing cost.
Why SLI matters here: Cold starts can break checkout conversion.
Architecture / workflow: CDN -> API Gateway -> Serverless funcs -> Payment provider; provider metrics and OpenTelemetry traces collected.
Step-by-step implementation:

  • Define SLI: p95 checkout invocation duration and success ratio.
  • Instrument function to emit invocation type cold/warm and duration.
  • Use provider metrics for invocation counts and cold start tag.
  • Set SLOs: p95 < 500ms and success ratio > 99% over 30d.
  • Implement warmers or provisioned concurrency based on SLI. What to measure: Cold start rate, p95 latency, success ratio.
    Tools to use and why: Provider metrics and OpenTelemetry for detailed traces.
    Common pitfalls: Warmers add cost and mask concurrency issues.
    Validation: Simulate traffic patterns and measure SLI over 7d.
    Outcome: Balanced provisioned concurrency for peak windows reducing SLI breaches.

Scenario #3 — Postmortem Driven Improvement (Incident-Response)

Context: Major outage caused by dependency timeout causing 503s.
Goal: Prevent recurrence and improve SLI instrumentation.
Why SLI matters here: Objective measurement clarifies when incident began and impact.
Architecture / workflow: Service calls external API; observability captured errors and traces.
Step-by-step implementation:

  • Reconstruct incident timeline using SLI time series.
  • Identify that request success ratio dropped below SLO at 03:12.
  • Add additional SLI: dependency success ratio and latency.
  • Update runbook to include dependency circuit breaker activation.
  • Re-run chaos test to validate improvements. What to measure: Service success ratio, dependency success ratio.
    Tools to use and why: Tracing for root cause and metrics for SLI.
    Common pitfalls: Postmortems blame symptoms rather than adding coverage.
    Validation: Game day simulating dependency timeout and confirming SLI warns early.
    Outcome: Faster mitigation and reduced recurrence probability.

Scenario #4 — Cost vs Performance Trade-off (Cost/Performance)

Context: High-volume read service with expensive high-memory nodes.
Goal: Reduce infra cost while keeping user latency within SLO.
Why SLI matters here: Quantifies user impact of cost optimizations and informs trade-offs.
Architecture / workflow: Reads served via cache then DB; cache hit ratio SLI available.
Step-by-step implementation:

  • Define SLIs: cache hit ratio and p95 read latency.
  • Model cost for various cache sizes and eviction policies.
  • Run experiments lowering cache sizes incrementally in staging.
  • Observe SLI drift and select configuration where SLO still met but cost reduced. What to measure: Cache hit ratio, p95 read latency, infra cost delta.
    Tools to use and why: Monitoring stack and cost reports from cloud billing.
    Common pitfalls: Not accounting for cold start of cache after change.
    Validation: Controlled A/B tests and monitoring SLI over 14 days.
    Outcome: Savings achieved with accepted latency increase within SLO.

Common Mistakes, Anti-patterns, and Troubleshooting

List 15–25 mistakes with Symptom -> Root cause -> Fix

1) Symptom: Sudden SLI gap. Root cause: Telemetry pipeline outage. Fix: Verify ingestion, enable local fallback counters, alert on ingestion health. 2) Symptom: Alerts fire but users unaffected. Root cause: SLI computed on vanity metric unrelated to UX. Fix: Re-evaluate SLI mapping to user journeys. 3) Symptom: SLO missed but no incidents. Root cause: Measurement aggregation error. Fix: Audit calculation and test with synthetic data. 4) Symptom: On-call fatigue. Root cause: Overly aggressive alert thresholds and noisy telemetry. Fix: Adjust thresholds, add suppression and dedupe. 5) Symptom: High metric cost. Root cause: High cardinality labels. Fix: Reduce label cardinality, rollup labels, use histograms wisely. 6) Symptom: SLIs fluctuate wildly. Root cause: Short evaluation windows. Fix: Increase window duration and smooth using rolling averages. 7) Symptom: Wrong SLI values after deployment. Root cause: Instrumentation mismatch or versioned labels. Fix: Rollback and standardize instrumentation releases. 8) Symptom: SLI differs between regions. Root cause: Inconsistent telemetry configuration per region. Fix: Standardize exporters and sampling across regions. 9) Symptom: Synthetic checks green but users complain. Root cause: Synthetic not matching real user path. Fix: Add real-user monitoring SLIs and diversify synthetics. 10) Symptom: Error budget exhausted unexpectedly. Root cause: Quiet degradation over time unnoticed. Fix: Add burn-rate alerts and weekly reviews. 11) Symptom: Missing root cause in postmortem. Root cause: Insufficient trace retention. Fix: Increase retention for key services and add sampling for traces. 12) Symptom: Long alert dedup windows hide new incidents. Root cause: Over-aggressive dedupe rules. Fix: Use dedupe by fingerprint and short dedupe windows. 13) Symptom: Alerts for telemetry completeness during launches. Root cause: Expected traffic patterns not accounted. Fix: Add planned maintenance windows and suppress alerts during rollout. 14) Symptom: SLIs show regression after migration. Root cause: Config mismatch or environment differences. Fix: Run canaries and parallel runs before cutover. 15) Symptom: Security data not included in SLI. Root cause: Privacy constraints misapplied. Fix: Define privacy-safe aggregations and retain minimal identifiers. 16) Symptom: Late night SLI spikes. Root cause: Batch jobs overlapping peak windows. Fix: Reschedule heavy jobs or throttle them. 17) Symptom: Tooling query timeouts. Root cause: Inefficient SLI queries or huge cardinality. Fix: Use recording rules and pre-aggregations. 18) Symptom: Multiple teams disagree on SLI definition. Root cause: No governance or ownership. Fix: Establish SLI owner and review board. 19) Symptom: SLI computation expensive. Root cause: Real-time complex joins on large data. Fix: Precompute and store counters near source. 20) Symptom: Observability blind spots after scaling. Root cause: Agent sampling increased without compensation. Fix: Re-evaluate sampling strategy and compensate in calculations. 21) Symptom: Alerts duplicated across teams. Root cause: Overlapping alerting rules. Fix: Centralize SLO alert definitions and routing.

Observability-specific pitfalls (at least 5 included above)

  • Missing traces due to sampling.
  • High-cardinality causing query timeouts.
  • Telemetry pipeline drops causing SLI blind spots.
  • Synthetic checks misrepresenting real traffic.
  • Poor retention limits postmortem analysis.

Best Practices & Operating Model

Ownership and on-call

  • Assign SLI owners per service and per user journey.
  • Rotate on-call teams with clear escalation and SLI-focused responsibilities.

Runbooks vs playbooks

  • Runbook: Highly prescriptive steps for common SLI breaches.
  • Playbook: Higher-level decision guide when automation or human judgement necessary.
  • Keep runbooks versioned and tested.

Safe deployments (canary/rollback)

  • Gate canaries with SLI checks on short windows.
  • Automate rollback if canary SLI deviates beyond thresholds.

Toil reduction and automation

  • Automate instrumentation in frameworks.
  • Use auto-remediation for common degradations when safe.
  • Schedule maintenance windows to avoid paging for expected events.

Security basics

  • Ensure telemetry does not contain PII.
  • Apply least privilege to observability systems.
  • Encrypt metrics and logs at rest and in transit.

Weekly/monthly routines

  • Weekly: Review error budget consumption and short-term burn rates.
  • Monthly: Review SLI definitions, ownership, and major changes.

What to review in postmortems related to SLI

  • Verify SLI accuracy during incident.
  • Evaluate whether SLI would have warned earlier.
  • Update SLI definitions or thresholds if needed.
  • Track follow-up items into backlog with owners.

Tooling & Integration Map for SLI (TABLE REQUIRED)

ID Category What it does Key integrations Notes
I1 Metrics store Stores time series metrics and runs queries Dashboards alerting exporters Use recording rules for SLIs
I2 Tracing Captures distributed traces for request flows Metrics APM and logs Useful for root cause of SLI tail behavior
I3 Logging Stores structured logs for event-derived SLIs Analytics DB and alerts Good for business SLIs
I4 Alerting Sends pages tickets and notifications PagerDuty chat ICS Tied to burn rate and SLO rules
I5 Dashboard Visualizes SLIs and trends Data sources and auth Executive and debug views
I6 Telemetry collector Buffers and transports telemetry Exporters and security layers Resilient buffering is essential
I7 CI/CD Runs canaries and gating checks Monitoring and rollback hooks Enforce SLI checks before promotion
I8 Feature flags Controls rollout and metrics labeling Metrics and A/B testing Tie feature-specific SLIs to flags
I9 Cost tools Associates cost with service usage Billing APIs and tags Useful for cost-performance trade-offs
I10 Security SIEM Correlates security telemetry with SLIs Logs and alerting Adds security context to SLI incidents

Row Details (only if needed)

  • None

Frequently Asked Questions (FAQs)

What is the difference between SLI and SLO?

SLI is the metric; SLO is the target for that metric over a window.

Can SLIs be derived from logs only?

Yes, but it requires structured logs and reliable ingestion to compute numerators and denominators.

How many SLIs should a service have?

Focus on 3–5 core SLIs per user journey; too many dilutes focus.

Should business metrics be SLIs?

Yes, business SLIs for critical flows are recommended when they reflect user experience.

How to handle high-cardinality labels in SLI metrics?

Avoid using unbounded identifiers as labels; pre-aggregate or use rollups.

What SLI window is best?

Use multiple windows like 7d and 30d; short windows for immediate detection and long windows for trend.

Are synthetic checks sufficient for SLIs?

No, synthetics help but should be supplemented by real-user SLIs for accuracy.

How to set SLO targets?

Start conservative based on historical data and business tolerance; iterate with stakeholders.

What alerts should trigger paging?

Severe SLI breaches that risk exhausting error budgets quickly or affect core user flows.

How to test SLI correctness?

Use synthetic events and replay historical data to validate computation.

How to manage SLIs during maintenance?

Suppress alerts with scheduled maintenance windows and document the change in SLO reporting.

Do SLIs differ for multi-tenant systems?

Yes, consider tenant-specific SLIs where tenant impact differs and cardinality is manageable.

How to avoid alert fatigue with SLI alerts?

Use tiered alerts, deduplication, and burn-rate based paging rules.

Can SLI compute from sampled traces?

Yes if sampling strategy is known and compensated; prefer consistent sampling schemes.

How long should telemetry be retained for SLI analysis?

Varies by compliance; keep enough history to understand regressions and perform postmortems — often 30–90 days for metrics, longer for aggregated summaries.

What to do when SLOs are constantly missed?

Investigate root cause, adjust SLOs with business, add capacity or reliability fixes, and reduce risk by gated rollouts.

How to include security events in SLIs?

Define privacy-preserving aggregates and include security-relevant failure ratios as SLIs.

How often should SLI definitions be reviewed?

Quarterly or after major architecture changes and incidents.

Conclusion

SLIs are the foundation for measuring and managing user-facing reliability in cloud-native systems. They enable objective SLOs, drive incident response, and inform infrastructure and product trade-offs. A pragmatic SLI program balances precision with operational cost and supports automation, governance, and continuous improvement.

Next 7 days plan (5 bullets)

  • Day 1: Identify top 3 user journeys and propose candidate SLIs.
  • Day 2: Define exact numerator denominator aggregation and windows.
  • Day 3: Instrument one service and validate telemetry in staging.
  • Day 4: Implement recording rules and a basic dashboard for SLI.
  • Day 5–7: Run a short load test and validate alerting and runbook actions.

Appendix — SLI Keyword Cluster (SEO)

Primary keywords

  • Service Level Indicator
  • SLI definition
  • SLI SLO SLA difference
  • measuring SLI
  • SLI architecture

Secondary keywords

  • error budget
  • SLO best practices
  • observability for SLIs
  • SLI monitoring tools
  • SLI in Kubernetes

Long-tail questions

  • how to define an SLI for an api
  • what is the difference between sli and slo
  • how to compute request success ratio sli
  • best tools to measure sli in kubernetes
  • how to set an slo from an sli
  • should business metrics be slis
  • how to avoid alert fatigue with sli alerts
  • how to test sli calculations
  • measuring sli from traces vs metrics
  • how to include security in sli measurements

Related terminology

  • error budget burn rate
  • p95 p99 latency sli
  • synthetic monitoring for slis
  • real user monitoring sli
  • telemetry ingestion completeness
  • sampling strategy for sli
  • cardinality management metrics
  • recording rules for sli
  • canary deployments and slis
  • rollback automation
  • runbooks for sli incidents
  • observability pipeline resilience
  • prometheus sli patterns
  • opentelemetry for slis
  • apm for sli analysis
  • serverless cold start sli
  • cache hit ratio sli
  • db latency sli
  • job success rate sli
  • feature flag gated slis
  • sla vs slo vs sli
  • postmortem and sli analysis
  • sli governance
  • sli ownership model
  • telemetry privacy for slis
  • adaptive alerting for slis
  • cost performance tradeoff sli
  • telemetry collectors buffering
  • long term storage for slis
  • sli dashboards for executives
  • oncall dashboard sli panels
  • debug dashboard sli panels
  • ingest throttling impact on slis
  • sli calculation validation
  • sli aggregation window choice
  • sli approximation techniques
  • sli failure modes
  • sli mitigation strategies
  • sli runbook templates
  • sli maturity model
  • sli decision checklist
  • sli instrumentation plan
  • sli implementation guide