What is Synthetic Tests?

Quick Definition

Synthetic Tests are scripted, automated checks that simulate user or system interactions against applications, services, or infrastructure on a scheduled basis to verify availability, functionality, latency, and correctness before real users are impacted.

Analogy: Synthetic Tests are like test-driving a car on a closed track on a schedule to confirm brakes, steering, and gauges work before passengers get in.

Formal technical line: Synthetic Tests are deterministic, scheduled or triggered probes that execute predefined transactions or queries against production-like endpoints and generate telemetry for observability and alerting.

Other common meanings:

Synthetic monitoring — commonly used interchangeably to mean external uptime/transaction checks.
Synthetic data generation — different domain; creating data for training ML models.
Synthetic transactions — specific scripted user journeys within synthetic monitoring.

What it is

Synthetic Tests are scripted probes that execute deterministic interactions with systems to validate behavior, latency, and correctness from predefined locations or environments.
They run without real users and typically repeat on a schedule or are triggered by CI/CD pipelines, incident investigations, or deployment events.

What it is NOT

Not a replacement for real user monitoring (RUM); it complements RUM by providing predictable baselines.
Not purely load testing; while they can be used at scale, their primary focus is correctness and availability.
Not synthetic data generation, which is about creating datasets for training ML.

Key properties and constraints

Deterministic: same script yields same actions unless environment changes.
Observable: emits structured telemetry (success/failure, latency, errors).
Location-aware: tests may run from edge, regional, or internal vantage points.
Security-aware: requires authentication, secrets management, and least privilege.
Cost-sensitive: frequent tests across many locations can increase bills.
Privacy-aware: must avoid exposing PII in assertions or logs.

Where it fits in modern cloud/SRE workflows

Pre-deployment gating in CI/CD pipelines.
Post-deploy smoke checks and canary verification.
Production synthetic monitoring for SLIs feeding SLOs and error budgets.
Incident response for automated health checks and runbook triggers.
Service-level capacity and integration validation for multi-cloud and hybrid systems.

Diagram description (text-only)

A scheduler triggers agents or cloud probes across locations. Each probe loads a script that performs authentication, executes transaction steps against single or multiple endpoints, records telemetry, and sends results to an observability platform. Alerts evaluate SLOs and notify on-call systems. CI/CD can pause deployment if synthetic checks fail.

Synthetic Tests in one sentence

Synthetic Tests are predictable, scripted probes that continuously validate availability and critical user journeys so teams can detect regressions before real users are affected.

Synthetic Tests vs related terms (TABLE REQUIRED)

ID	Term	How it differs from Synthetic Tests	Common confusion
T1	Real User Monitoring	Passive capture of actual user traffic	People think it duplicates synthetic coverage
T2	Load Testing	Focus on high-volume stress and capacity	Assumed to verify functional correctness
T3	Smoke Tests	Broad basic checks post-deploy	Often conflated with full synthetic journeys
T4	Canary Releases	Deployment strategy for gradual rollout	Mistaken for a monitoring approach
T5	Chaos Engineering	Introduces failures to test resilience	Sometimes confused as continuous probes
T6	Synthetic Data Generation	Creates datasets for ML training	Confused due to the word synthetic
T7	API Contract Testing	Verifies API schemas and responses in CI	Assumed to replace runtime synthetic checks
T8	Endpoint Health Checks	Basic liveness or readiness probes	Believed to be sufficient for user journeys

Row Details

T2: Load Testing details:
Load testing targets throughput, concurrency, and resource limits.
Synthetic Tests typically run lightweight journeys repeatedly.
Use load tests for capacity planning; use synthetic for correctness and availability.

Why does Synthetic Tests matter?

Business impact

Revenue protection: Synthetic Tests often catch regressions in checkout or auth flows before user drop-off reduces conversion.
Trust and churn: Frequent detections of service degradation typically reduce customer churn by enabling faster remediation.
Risk reduction: Synthetic baselines provide evidence for contractual SLAs and support legal or compliance audits.

Engineering impact

Incident reduction: Synthetic checks commonly detect regressions earlier, reducing mean time to detect (MTTD).
Faster velocity: CI-integrated synthetic tests let teams merge with confidence by auto-verifying critical flows post-merge.
Reduced blast radius: Canary verification with synthetic gating prevents bad releases from reaching all users.

SRE framing

SLIs/SLOs: Synthetic Tests commonly provide SLIs like “critical-journey success rate” and latency percentiles that feed SLOs.
Error budgets: Synthetic-derived error budgets make release decisions data-driven.
Toil: Automating synthetic tests reduces manual smoke testing toil for on-call teams.
On-call: Synthetic failures provide actionable alerts that can be tied to runbooks and automated mitigations.

What commonly breaks in production (realistic examples)

Auth token expiry causes silent failures in downstream API calls, often not caught by liveness probes.
CDN or edge misconfiguration that serves stale or 404 content for specific regions.
Third-party payment gateway changes returning unexpected error codes on checkout.
Database schema rollout causing a subset of queries to return nulls for certain payloads.
Network path changes causing elevated latency for a critical microservice route.

Where is Synthetic Tests used? (TABLE REQUIRED)

ID	Layer/Area	How Synthetic Tests appears	Typical telemetry	Common tools
L1	Edge and CDN	Regional content and routing checks	200/304 codes, time to first byte, region	Synthetic probes, CDN logs
L2	Network	ICMP/TCP/HTTP probes from locations	RTT, packet loss, traceroute hops	Network probes, observability agents
L3	Service/API	Transaction scripts for critical APIs	Request latency, status codes, payload checks	API probes, contract checks
L4	Application UI	Browser-based user journeys	Load times, JS errors, DOM assertions	Browser automation probes
L5	Data layer	Query correctness and latency checks	Query result shape, latency, error rates	DB probes, integration scripts
L6	Cloud infra	VM/service provisioning validations	Provision times, instance health	Cloud provider checks, infra tests
L7	Kubernetes	Pod readiness from internal probes	Pod response times, DNS resolution	K8s probes, internal synthetic agents
L8	Serverless/PaaS	Cold start and function correctness tests	Cold start latency, invocation errors	Function invocation probes
L9	CI/CD	Pre/post-deploy gates and smoke scripts	Build/deploy status, test pass rates	Pipeline jobs, synthetic stages
L10	Security	Auth and endpoint access checks	Failed auth rates, policy denials	Auth probes, canaries

Row Details

L1: Use regional vantage points to validate CDN rules and edge cached responses.
L7: Kubernetes synthetic tests often need cluster-internal probes to test service DNS and network policies.
L8: Serverless checks should include cold-start profiling and per-region invocation validation.

When should you use Synthetic Tests?

When it’s necessary

Critical user journeys that affect revenue or compliance (checkout, login, data export).
Post-deploy verification of production changes.
SLA-backed services that require continuous availability guarantees.

When it’s optional

Low-impact internal tooling where occasional manual checks suffice.
Development environments where RUM or unit/integration tests already cover behavior.

When NOT to use / overuse it

Excessive frequency across many locations causing cost spikes.
As the only monitoring source; RUM and logs are necessary complements.
For exploratory testing or coverage that requires randomized user input — use dedicated fuzzing or user testing.

Decision checklist

If feature impacts revenue AND affects many users -> run synthetic tests in multiple regions and in CI gates.
If service is internal and low criticality AND cost is constrained -> run a few regional probes at lower frequency.
If you need capacity testing -> use load testing, not synthetic smoke tests.

Maturity ladder

Beginner: One or two synthetic checks for critical endpoints with basic alerting.
Intermediate: Multi-region probes, scripted multi-step transactions, CI post-deploy checks, SLI/SLO integration.
Advanced: Adaptive frequency based on error budget burn rate, synthetic-driven canaries, automated remediation and chaos integration.

Example decisions

Small team: Run 3 synthetic journeys (login, search, checkout) from one regional public probe, run every 5 minutes, alert on 3 failures in 15 minutes.
Large enterprise: Run multi-region browser-based journeys, internal cluster probes, integrate with SLOs and automated rollback pipelines, run canary verification per deploy.

How does Synthetic Tests work?

Components and workflow

Script authoring: Define explicit steps, assertions, and teardown.
Execution engine: Agents, cloud probes, or headless browsers run scripts.
Scheduler: Orchestrates timing, frequency, and distribution.
Telemetry collector: Aggregates success/failure, latency, logs, and traces.
Evaluation layer: Computes SLIs/SLOs, alerts, and dashboards; triggers runbooks or automated rollbacks.
Storage & analysis: Historical results for trends, postmortem, and anomaly detection.

Data flow and lifecycle

Author script -> store in repo or platform -> scheduler triggers agent -> agent runs script -> results emitted to telemetry pipeline -> evaluator computes metrics -> alerts/visualization -> archive.

Edge cases and failure modes

Flaky steps due to external third-party variability.
Authentication token rotation causing silent failures.
DNS caching differences between probe locations and users.
Tests masking real user problems when run only from a limited set of vantage points.

Practical example (pseudocode style)

Authenticate -> GET /api/cart -> POST /api/cart/items -> POST /api/checkout -> Assert 200 and JSON schema match -> Emit latency and success.

Typical architecture patterns for Synthetic Tests

External global probes: Use multiple public locations for customer-facing applications.
When to use: Public-facing sites, CDN validation.
Internal in-cluster probes: Agents inside Kubernetes clusters for internal service checks.
When to use: Microservice meshes, DNS, internal APIs.
CI-integrated smoke stage: Lightweight synthetic checks run after deployment.
When to use: Pre-release gating and quick rollback triggers.
Browser-based full-journey probes: Headless browsers that validate frontend behavior.
When to use: Complex UI interactions and SPA routes.
Distributed agent mesh: Hybrid approach with both edge and internal probes and centralized telemetry.
When to use: Global services with mixed public and private dependencies.

Failure modes & mitigation (TABLE REQUIRED)

ID	Failure mode	Symptom	Likely cause	Mitigation	Observability signal
F1	Flaky assertions	Intermittent test failures	Third-party variability or timing	Add retries and tolerance windows	Increased failure variance
F2	Auth token expiry	Sudden auth failures	Secrets rotation not updated	Use vault, automated secret refresh	401 spikes in telemetry
F3	DNS inconsistency	Different results by region	DNS caching or split-horizon DNS	Use internal resolvers and probing	Varying resolved IPs
F4	Rate limiting	Throttled responses	Too frequent probes or API limits	Reduce frequency, centralize probes	429s and elevated latency
F5	Cost overrun	Unexpected billing surge	High frequency and many locations	Optimize schedule and sampling	Spike in probe-related costs
F6	Environment drift	Tests passing but users failing	Test environment diverges from prod	Keep scripts running against prod-like endpoints	Discrepancy between RUM and synthetic
F7	Noise / alert fatigue	Too many alerts	Low signal-to-noise in rules	Group alerts and tune thresholds	High alert counts per shift
F8	Probe agent outage	Missing telemetry from probes	Agent health or network partition	Self-healing agents and fallback probes	Missing datapoints from locations

Row Details

F1: Flaky assertions details:
Introduce deterministic waits rather than blind sleeps.
Capture and log full response payloads for debugging.
Use adaptive thresholds for transient third-party APIs.

Key Concepts, Keywords & Terminology for Synthetic Tests

Term — Definition — Why it matters — Common pitfall

SLI — Service Level Indicator representing a measurable characteristic of service quality — Basis for SLOs — Confusing SLI with SLA targets.
SLO — Service Level Objective, a target for an SLI over time — Guides error budgets and release decisions — Too many SLOs dilute focus.
Error budget — Allowable rate of SLO breaches used for release control — Balances reliability vs velocity — Overly conservative budgets stall deploys.
Synthetic probe — Agent or process that runs a synthetic test — Central runtime component — Not architected for high-traffic load testing.
Vantage point — Physical or cloud location where probes run — Reveals region-specific issues — Limited vantage points mask regional failures.
Headless browser — Browser used without UI for automated UI tests — Validates client-side behavior — Heavy resource cost if used too frequently.
Transaction test — Multi-step user journey simulated by a script — Validates end-to-end flows — Fragile if downstream services change frequently.
Canary check — Synthetic test validating a canary release subset — Enables safe rollouts — Poor canary coverage misses regressions.
Probe scheduler — Component that schedules test runs — Ensures coverage and cadence — Single scheduler can be a single point of failure.
Assertion — Condition checked in synthetic step (status code, DOM node) — Determines pass/fail — Overly strict assertions cause false positives.
Latency percentile — P95, P99 values of response time from synthetic runs — Captures tail latency — Short sampling can mislead percentile values.
Availability check — Binary pass/fail probe of endpoint health — Simple and actionable — Cannot verify complex user journeys alone.
Maintenance window suppression — Temporarily silences alerts during known work — Prevents noise — Failure to schedule leads to missed failures.
Secret rotation — Updating auth credentials used by probes — Keeps tests secure — Hardcoding secrets causes outages on rotation.
Probe throttling — Limits frequency of tests to avoid rate limits — Controls cost and avoids throttling — Aggressive throttling delays detection.
CI gate — Synthetic checks run as part of CI/CD pipeline stage — Prevents bad merges to prod — Slow checks lengthen pipelines.
Synthetic dashboard — Visual summary of synthetic test results and trends — Aids triage — Cluttered dashboards hinder actionability.
Runbook — Step-by-step incident guidance tied to alerts — Speeds resolution — Outdated runbooks cause confusion.
Playbook — Higher-level procedural guidance for teams — Guides decision-making — Too generic to action in high-pressure incidents.
False positive — Alert when system is actually healthy — Causes trust erosion — Tune thresholds and refine checks.
False negative — Missing an actual user-impacting issue — Leads to undetected outages — Expand coverage and vary vantage points.
Canary deployment — Progressive rollout method combined with synthetic gating — Limits blast radius — Poor telemetry integration weakens protection.
Recovery automation — Auto-rollback or self-heal actions triggered by synthetic failures — Reduces toil — Dangerous without proper guards.
Observability pipeline — Telemetry ingestion and processing backend — Enables analysis and alert evaluation — High cardinality can increase costs.
Probe mesh — Distributed agents across networks running tests — Improves coverage — Management complexity increases.
Headless browser replay — Re-run failed UI tests with screenshots and traces — Aids debugging — Storing artifacts may increase storage costs.
Synthetic baseline — Expected metric profiles used to detect anomalies — Helps detect regression — Stale baselines create false alerts.
Canary SLI — SLI evaluated specifically for canary traffic — Critical for safe rollouts — Misconfigured canary SLI can block deploys wrongly.
Multi-step transaction — Scripted set of dependent calls in synthetic tests — Simulates realistic user flow — Single-point step failure invalidates entire test.
Assertion timeout — Max wait time for an assertion to become true — Prevents indefinite waits — Too long hides failures; too short causes flakiness.
Probe isolation — Running probes in sandboxed environments for safety — Prevents contamination of production — May differ from production behavior.
Synthetic cost model — Calculated cost of running synthetic probes — Important for budgeting — Ignoring cost can cause unexpected bills.
Regional health check — Synthetic tests focused on geographic impact — Detects region-specific outages — Requires sufficient regional sampling.
API schema check — Validates response schema against contract — Prevents integration regressions — Schema churn increases maintenance.
Progressive sampling — Varying frequency by importance or error budget — Balances cost vs detection speed — Complex to implement initially.
Trace correlation — Attaching distributed traces to synthetic runs — Speeds root cause analysis — Missing context reduces value.
Service mesh probe — Internal testing for mesh-powered traffic policies — Verifies policy correctness — Mesh config drift breaks probes.
Failure injection — Deliberate faults to validate probe and system resiliency — Ensures robustness — Must be gated to avoid impact.
Response fingerprint — Hashing response attributes to detect change — Identifies unexpected content changes — Fragile with dynamic content.

How to Measure Synthetic Tests (Metrics, SLIs, SLOs) (TABLE REQUIRED)

ID	Metric/SLI	What it tells you	How to measure	Starting target	Gotchas
M1	Synthetic success rate	Percent of runs passing	SuccessCount / TotalRuns	99.5% for critical journeys	Not same as user success
M2	Median latency (P50)	Typical response speed	Collect latencies, compute median	Varies by app, start with 300ms	Masked by outliers
M3	Tail latency (P95/P99)	User-facing worst-case delays	Compute percentile over window	P95 target 1s P99 2s	Needs sufficient samples
M4	Time to first byte	Edge responsiveness	Measure TTFB from probe	< 300ms typical start	CDN caching distorts numbers
M5	Mean time to detect (MTTD)	How long to detect regressions	Time from failure to alert	Aim under 5m for critical flows	Alerting pipelines add variance
M6	Error types distribution	Categorize failure causes	Aggregate status codes and errors	Monitor top 3 error codes	Sparse errors complicate stats
M7	Availability by region	Regional health differences	SuccessRate grouped by location	Same as global SLO but per region	Requires good regional sampling
M8	Canary verification SLI	Stability of canary release	SLI on canary traffic passes	Same as SLO for prod-critical	Canary traffic must be representative
M9	Probe coverage	Percent of critical journeys monitored	MonitoredJourneys / CriticalJourneys	90% min for critical systems	Defining critical is social
M10	Synthetic cost per month	Budget impact of synthetic runs	Sum probe billing	Set budget cap per team	Billing attribution complexity

Row Details

M1: Synthetic success rate details:
Decide what constitutes a pass (status code, payload check, end-to-end assertion).
Exclude known maintenance windows from SLI computation.
M3: Tail latency details:
Ensure sample size is sufficient for percentile calculations (hundreds of samples).
Use sliding windows to detect regressions.

Best tools to measure Synthetic Tests

(Each below follows exact structure)

Tool — Open-source probe frameworks (example frameworks)

What it measures for Synthetic Tests:
Transaction success, latency, and response assertions.
Best-fit environment:
Self-managed environments and on-prem clusters.
Setup outline:
Install agent or runner on nodes.
Write scripts in supported language.
Configure scheduler and telemetry exporter.
Integrate with metrics backend.
Set alerting rules and dashboards.
Strengths:
Full control and no per-run vendor costs.
Flexible scripting and integration.
Limitations:
Requires maintenance and scaling effort.
Observability and storage require own infra.

Tool — Commercial synthetic monitoring platforms

What it measures for Synthetic Tests:
Multi-region probes, browser journeys, and API checks with dashboards.
Best-fit environment:
Organizations wanting managed probes and SLA guarantees.
Setup outline:
Define journeys in UI or code.
Configure locations and frequency.
Connect to alerting and SLO systems.
Set up access control and secrets.
Strengths:
Quick time-to-value and global vantage points.
Built-in reporting and alerting.
Limitations:
Cost grows with coverage and frequency.
Less control over agent behavior.

Tool — Browser automation platforms

What it measures for Synthetic Tests:
Client-side rendering, DOM interactions, and JS errors.
Best-fit environment:
SPAs and complex UIs needing end-to-end verification.
Setup outline:
Create headless browser scripts.
Configure capture of screenshots, logs, traces.
Schedule runs across regions.
Store artifacts for debugging.
Strengths:
High-fidelity reproduction of user journeys.
Visual evidence for failures.
Limitations:
Resource intensive and more flaky.
Higher maintenance as UI changes.

Tool — CI/CD integrated probes

What it measures for Synthetic Tests:
Post-deploy smoke checks and integration verification.
Best-fit environment:
Teams with established CI/CD pipelines.
Setup outline:
Add synthetic stage to pipeline.
Use lightweight scripts to validate deploy.
Fail pipeline on critical failures.
Automate rollback or approval gates.
Strengths:
Immediate feedback during release.
Low-latency protection for deploys.
Limitations:
Adds time to pipelines if not optimized.
Requires test stability to avoid blocking.

Tool — Internal cluster agents (Kubernetes)

What it measures for Synthetic Tests:
Internal service connectivity, DNS, mesh policies.
Best-fit environment:
Kubernetes deployments and microservice clusters.
Setup outline:
Deploy probes as cronjobs or sidecar agents.
Configure service identities and RBAC.
Collect telemetry to cluster metrics.
Integrate with internal alerting.
Strengths:
Tests internal topologies that external probes miss.
Low-latency and accurate for cluster issues.
Limitations:
Must avoid polluting production resources.
Needs secure secret handling.

Recommended dashboards & alerts for Synthetic Tests

Executive dashboard

Panels:
Global success rate trend and SLO burn-down.
Top failing journeys by severity.
Monthly synthetic cost and coverage.
Business impact mapping (revenue-linked journeys).
Why:
Provides leadership visibility into customer-impacting reliability.

On-call dashboard

Panels:
Live failing synthetic checks with error counts.
Per-region failure heatmap.
Recent deploys correlation.
Runbook links and last successful run artifacts.
Why:
Rapid triage and remediation for on-call engineers.

Debug dashboard

Panels:
Last N run traces and raw HTTP responses.
Step-by-step timeline for failed transactions.
Related logs, traces, and metrics for implicated services.
Historical trend for the failed step.
Why:
Enables deep root-cause analysis without bounce between tools.

Alerting guidance

Page vs ticket:
Page (urgent): Critical SLO breach, repeated failures across multiple regions, or canary failure during rollout.
Ticket (non-urgent): Single-region transient failures or degraded non-critical journeys.
Burn-rate guidance:
Use error-budget burn rate to escalate: low burn -> ticket; high sustained burn -> page and rollback.
Noise reduction tactics:
Deduplicate by fingerprinting identical failures.
Group alerts by service or release.
Suppress alerts during scheduled maintenance windows.

Implementation Guide (Step-by-step)

1) Prerequisites – Identify critical user journeys and corresponding owners. – Establish telemetry backends and SLI storage. – Ensure secrets management and access control are available.

2) Instrumentation plan – Define what constitutes a pass for each journey. – Select vantage points and frequency per journey. – Choose assertion types (status codes, schema, DOM, latency).

3) Data collection – Configure probes to send structured telemetry to observability pipelines. – Attach trace and log context to each synthetic run. – Ensure timestamps and location metadata are included.

4) SLO design – Map SLIs to business goals and set realistic SLOs per journey. – Partition SLOs by region or customer tier when needed. – Define error budgets and escalation thresholds.

5) Dashboards – Build executive, on-call, and debug dashboards. – Include links to runbooks and artifacts. – Add trend analysis panels with rolling windows.

6) Alerts & routing – Define alert rules tied to SLO burn and raw failure signals. – Configure routing to the correct on-call teams. – Include context and run artifacts in alert payloads.

7) Runbooks & automation – Create step-by-step runbooks for common failures. – Automate remediation actions where safe (circuit breaker open, rollback). – Keep runbooks versioned and reproducible.

8) Validation (load/chaos/game days) – Run load tests to verify synthetic infrastructure scales. – Run chaos experiments to validate resilience and probe reliability. – Conduct game days to validate runbook effectiveness.

9) Continuous improvement – Review synthetic coverage monthly and add or retire checks. – Tune thresholds to balance noise with detection speed. – Correlate synthetic failures with RUM and logs for coverage gaps.

Checklists

Pre-production checklist

Script runs against staging environment and succeeds.
Secrets used by probes are stored in vault and referenced.
Telemetry pipeline configured with test namespaces.
Runbook drafted for expected failures.

Production readiness checklist

Probes validated against production endpoints.
SLOs and alerting rules deployed.
On-call rotations and routing verified.
Cost estimation reviewed and approved.

Incident checklist specific to Synthetic Tests

Confirm failure is reproducible from multiple vantage points.
Retrieve last successful run and diff responses.
Correlate with recent deploys and change events.
Execute runbook steps, escalate if thresholds exceeded.
After mitigation, run validation probes and close incident.

Examples

Kubernetes example:
Deploy a synthetic probe as a Kubernetes CronJob that runs every 5 minutes, authenticates with service account, executes internal service calls, and posts telemetry to cluster metrics. Verify RBAC and resource limits. Good looks like 100% successful runs for 24 hours pre-deploy.
Managed cloud service example:
Configure cloud provider function to invoke an external payment API endpoint in a test mode once per minute from two regions. Store secrets in the cloud KMS and ensure IAM least privilege. Good looks like latency and success rate within SLO and artifacts retained for 7 days.

Use Cases of Synthetic Tests

1) Checkout flow validation (e-commerce) – Context: High-value checkout path with multiple third-party payments. – Problem: Payment gateway regressions cause lost revenue. – Why Synthetic Tests helps: Detects payment errors immediately after deploy or provider changes. – What to measure: Success rate, payment response codes, latency. – Typical tools: API probes + browser journey.

2) Login and SSO verification (enterprise app) – Context: Single sign-on integration across regions. – Problem: Token misconfiguration breaks enterprise access. – Why Synthetic Tests helps: Simulates SSO handshake and token refresh. – What to measure: Auth success, token expiry handling, redirects. – Typical tools: API and OAuth probe scripts.

3) DNS and CDN regional routing (global site) – Context: Multi-region CDN and DNS routing. – Problem: Edge misconfig results in 404s in a region. – Why Synthetic Tests helps: Regional probes detect content and routing errors. – What to measure: Status codes, TTFB, cache hit rates. – Typical tools: Edge probes and traceroute telemetry.

4) Internal microservice contract check (microservices) – Context: Multiple teams deploy shared service APIs. – Problem: Schema changes break consumers. – Why Synthetic Tests helps: Contract checks validate response shape and critical fields. – What to measure: Schema validation pass rate, latency. – Typical tools: API contract probes and CI-integrated checks.

5) Database migration verification (data layer) – Context: Rolling DB schema changes. – Problem: Missing column or migrated data causes nulls. – Why Synthetic Tests helps: Query-based checks validate data integrity post-migration. – What to measure: Query results, error rates, response times. – Typical tools: DB probes and integration scripts.

6) Serverless cold-start monitoring (serverless) – Context: Functions invoked sporadically. – Problem: High cold-start latency affecting UX. – Why Synthetic Tests helps: Repeated invocations measure cold-start profiles. – What to measure: Cold-start latency, failure rates. – Typical tools: Function invocation probes and telemetry.

7) Canary deployment verification (release engineering) – Context: Progressive rollout of new service version. – Problem: Regressions not caught until full rollout. – Why Synthetic Tests helps: Canary SLI assesses new version stability before full release. – What to measure: Canary failure rate, latency, error distribution. – Typical tools: CI gates and canary probes.

8) API gateway routing checks (infra) – Context: Multi-tenant gateway routing rules. – Problem: Misroutes causing tenant impact. – Why Synthetic Tests helps: Simulates tenant requests to validate routing and rate limits. – What to measure: Route correctness and status codes. – Typical tools: Gateway probes.

9) Backup restore validation (operational) – Context: Periodic backups for compliance. – Problem: Restores fail or are incomplete. – Why Synthetic Tests helps: Regular restore validation scripts ensure backups are usable. – What to measure: Restore success, data integrity checks. – Typical tools: Orchestration scripts invoking restore workflows.

10) Payment processor change (third-party integration) – Context: Provider changes API contract. – Problem: Unexpected error codes or field changes. – Why Synthetic Tests helps: Detects contract deviations before customers notice. – What to measure: Field presence and response codes. – Typical tools: API probes and schema checks.

Scenario Examples (Realistic, End-to-End)

Scenario #1 — Kubernetes internal service mesh validation

Context: Microservices communicate via service mesh in Kubernetes cluster.
Goal: Ensure internal routing and DNS resolve for critical payment service.
Why Synthetic Tests matters here: External probes can’t access internal cluster; internal probes validate service-to-service paths.
Architecture / workflow: CronJob probe runs in namespace, authenticates with service account, calls internal API endpoints, emits traces to observability.
Step-by-step implementation:

Write probe script to call service A -> service B -> DB read.
Deploy as Kubernetes CronJob with resource limits.
Attach RBAC role allowing pod exec to retrieve certs.
Configure telemetry exporter to cluster metrics.
Create alert for 5 failures in 15 minutes.
What to measure: Success rate, P95 latency, trace spans for service B.
Tools to use and why: K8s CronJob, Prometheus metrics, internal tracing because they integrate with cluster.
Common pitfalls: Hardcoded service endpoints causing failures on blue-green deploys.
Validation: Run local force-deployment and verify synthetic runs succeed.
Outcome: Faster detection of mesh misconfig and reduced MTTD.

Scenario #2 — Serverless cold-start and correctness check

Context: Serverless function handles invoice generation in multiple regions.
Goal: Detect cold-start regressions and validate response schema.
Why Synthetic Tests matters here: Serverless cold-starts are intermittent and can impact CPU-bound flows.
Architecture / workflow: Function invocations scheduled from cloud-managed probes across regions, responses schema validated, artifacts stored.
Step-by-step implementation:

Create invocation script that passes sample payload.
Schedule per-region invocations every 2 minutes.
Collect latency and response schema validation logs.
Alert when median cold-start > threshold or schema fails.
What to measure: Cold-start latency, success rate, schema pass.
Tools to use and why: Cloud function scheduler and provider telemetry for integrated metrics.
Common pitfalls: Running in test mode that bypasses upstream auth leading to false confidence.
Validation: Execute burst invocations and correlate with resource changes.
Outcome: Identified provider change causing cold-start increase and triggered configuration fixes.

Scenario #3 — Incident-response postmortem verification

Context: A production incident caused checkout failures for 30 minutes.
Goal: Use synthetic historical runs to explain failure propagation and detection latency.
Why Synthetic Tests matters here: Historical synthetic logs provide deterministic timeline for diagnosis.
Architecture / workflow: Telemetry store with run artifacts, correlation with deploy events and logs.
Step-by-step implementation:

Retrieve synthetic runs around incident timeframe.
Compare last successful run to first failing run payloads.
Correlate with deploy and infra events.
Document root cause and remediation in postmortem.
What to measure: Time of first failure, number of impacted runs, affected regions.
Tools to use and why: Observability backend with trace and artifact storage.
Common pitfalls: Missing synthetic artifacts due to short retention.
Validation: Confirm remediation actions and run synthetic tests to ensure stability.
Outcome: Root cause identified as a misapplied config and automated rollback added.

Scenario #4 — Cost vs performance trade-off analysis

Context: Team wants more coverage but cost of probes is rising.
Goal: Optimize frequency and locations without compromising SLOs.
Why Synthetic Tests matters here: Balancing detection speed and budget constraints.
Architecture / workflow: Analyze failure patterns by region and frequency, implement progressive sampling.
Step-by-step implementation:

Audit probe costs per journey and region.
Identify low-value high-cost probes.
Implement tiered sampling: critical journeys full coverage; non-critical reduced frequency.
Monitor for missed regressions and adjust.
What to measure: Cost per alert, detection latency, SLO compliance.
Tools to use and why: Billing export analysis and probe telemetry.
Common pitfalls: Over-pruning probes that detect rare but critical failures.
Validation: Run simulated regressions in pruned regions and confirm detection.
Outcome: Reduced monthly cost while maintaining SLOs.

Common Mistakes, Anti-patterns, and Troubleshooting

(List of concise items with symptom -> root cause -> fix)

Symptom: Frequent false positives. -> Root cause: Overly strict assertions or short timeouts. -> Fix: Relax assertions, add retries, increase timeouts.
Symptom: Missing regional failures. -> Root cause: Probes run only from one location. -> Fix: Add at least two to three regional vantage points.
Symptom: High probe cost. -> Root cause: Excessive frequency and global coverage for low-value journeys. -> Fix: Tier journeys and implement progressive sampling.
Symptom: Alerts during maintenance. -> Root cause: No maintenance suppression. -> Fix: Implement scheduled suppression in alerting rules.
Symptom: CI pipelines fail intermittently. -> Root cause: Long-running synthetic checks in pipeline. -> Fix: Move heavy browser checks to post-deploy or reduce scope.
Symptom: Authentication failures after secret rotation. -> Root cause: Hardcoded credentials in scripts. -> Fix: Use vault/KMS and automate secret refresh.
Symptom: No context in alerts. -> Root cause: Missing run artifacts and response payloads. -> Fix: Attach last response and trace link to alerts.
Symptom: Synthetic tests pass but users complain. -> Root cause: Probes run from different network path than users. -> Fix: Add RUM correlation and probes from user-similar networks.
Symptom: Flaky UI tests. -> Root cause: DOM timing and dynamic content. -> Fix: Use stable selectors and explicit waits for elements.
Symptom: SLI mismatch with business KPIs. -> Root cause: Incorrect SLI definition. -> Fix: Revisit SLI mapping to business outcome.
Symptom: Alert fatigue. -> Root cause: Too many low-signal alerts. -> Fix: Aggregate, dedupe, and set higher thresholds.
Symptom: Long MTTD. -> Root cause: Low probe frequency. -> Fix: Increase cadence for critical journeys.
Symptom: Probe agent crashes. -> Root cause: Resource exhaustion or dependency mismatch. -> Fix: Enforce resource quotas and health checks.
Symptom: Missing long-term trends. -> Root cause: Short telemetry retention. -> Fix: Increase retention for critical synthetic metrics.
Symptom: Broken canary gating. -> Root cause: Canary SLI not representative. -> Fix: Generate synthetic traffic that mimics production traffic mix.
Symptom: False confidence in third-party APIs. -> Root cause: Tests use cached or mocked endpoints. -> Fix: Run tests against live provider test environments or staging.
Symptom: Excessive artifact storage. -> Root cause: Storing full screenshots and logs for every run. -> Fix: Store artifacts only on failures or sample them.
Symptom: Probe network calls blocked by firewall. -> Root cause: Missing egress permissions. -> Fix: Update network rules to allow probe egress.
Symptom: Synthetic probes masked by CDN caches. -> Root cause: Tests hit cached content only. -> Fix: Add cache-busting headers or test uncached endpoints.
Symptom: Observability gaps for synthetic runs. -> Root cause: No trace propagation. -> Fix: Instrument probes to inject trace context into requests.
Symptom: Too many SLOs. -> Root cause: Siloed teams defining per-metric SLOs. -> Fix: Rationalize to critical customer-facing SLOs.
Symptom: Probe results inconsistent with RUM. -> Root cause: Sampling differences and different user populations. -> Fix: Correlate and align sampling strategies.
Symptom: Security exposure from test payloads. -> Root cause: PII in test data. -> Fix: Use anonymized test data and mask logs.
Symptom: Alerts trigger on deploys only. -> Root cause: No staging verification. -> Fix: Add pre-deploy synthetic checks and intermediate canaries.
Symptom: Probe throttled by API provider. -> Root cause: Too many probe requests to public APIs. -> Fix: Coordinate with provider or reduce frequency.

Best Practices & Operating Model

Ownership and on-call

Assign ownership of synthetic tests to feature teams owning the journeys.
On-call rotations should include synthetic test failures in pager duties.
Central SRE should govern global SLOs and provide guardrails.

Runbooks vs playbooks

Runbooks: Step-by-step instructions for specific synthetic failures (e.g., auth token expired).
Playbooks: Higher-level escalation guidance and decision-making (e.g., rollback criteria).
Keep runbooks versioned with code and test artifacts.

Safe deployments

Use canary with synthetic gating to validate new releases on a subset of traffic.
Automate rollback on critical SLO breaches detected by synthetic checks.
Implement traffic shaping to minimize blast radius.

Toil reduction and automation

Automate test refreshes on schema or API changes via CI.
Auto-annotate alerts with last successful run artifacts.
Automate secret rotation and probe reconfiguration.

Security basics

Store probe credentials in KMS or vault and grant least privilege.
Mask sensitive response data before storing artifacts.
Limit probe access to read-only operations when possible.

Weekly/monthly routines

Weekly: Review failing synthetic checks and adjust flaky ones.
Monthly: Review coverage gaps, SLO status, and probe costs.
Quarterly: Review SLI validity and SLO targets with stakeholders.

Postmortem review items specific to Synthetic Tests

Was synthetic coverage sufficient to detect the issue earlier?
Were artifacts available and useful for diagnosis?
Did synthetic tests cause false positives or noisy alerts?
Action items: add coverage, extend retention, adjust thresholds.

What to automate first

Alert-to-runbook linking.
Artifact capture on failures.
CI post-deploy synthetic gating for critical journeys.

Tooling & Integration Map for Synthetic Tests (TABLE REQUIRED)

ID	Category	What it does	Key integrations	Notes
I1	Probe runner	Executes scripts and journeys	Metrics backend, tracing, storage	Use for agent-based probes
I2	Headless browser	High-fidelity UI validation	Screenshots, logs, traces	Resource heavier than API probes
I3	CI/CD pipeline	Runs post-deploy synthetic gates	VCS, deploy system, alerting	Prevents bad releases
I4	Observability backend	Stores metrics, logs, traces	Dashboards, alerting, SLO engine	Central evaluation point
I5	Secrets manager	Securely stores auth credentials	Probe runners, CI systems	Must support rotation
I6	Canary orchestrator	Routes subset traffic for canaries	Load balancer, CI, probes	Ties canary traffic to probes
I7	Cost analysis	Tracks probe billing and spend	Billing exports, dashboards	Helps optimize sampling
I8	Internal agent mesh	Distributed in-cluster probes	K8s, service mesh, tracing	Validates internal routes
I9	Incident management	Creates pages/tickets on alerts	Alerting, on-call, runbook links	Critical for operations
I10	Schema validator	Validates API response shapes	CI, probes, contract repo	Prevents consumer regressions

Row Details

I1: Probe runner details:
Can be self-hosted agent, cloud-managed runtime, or containerized cronjobs.
Must support secure configuration and telemetry export.
I6: Canary orchestrator details:
Works with service mesh or load balancers to split traffic.
Integrate canary SLI evaluation from probes before promoting.

Frequently Asked Questions (FAQs)

How do I choose which journeys to synthetic test?

Select journeys with highest user or revenue impact and those that are brittle or depend on third parties; prioritize by business impact and failure cost.

How often should synthetic tests run?

Typically every 1–5 minutes for critical journeys; less frequent for low-risk endpoints. Balance detection latency with cost.

How many regions should I run probes from?

Start with at least two regions representative of user bases; expand if users or incidents indicate region-specific issues.

What’s the difference between synthetic monitoring and RUM?

Synthetic is active, scripted, and deterministic; RUM is passive, capturing actual user behavior and uncontrolled variability.

What’s the difference between synthetic tests and load testing?

Load testing stresses capacity with high volume; synthetic tests verify correctness and availability at small scale routinely.

What’s the difference between canary verification and synthetic canary checks?

Canary verification focuses on behavioral metrics for the new version; synthetic canary checks specifically run scripted journeys against canary endpoints.

How do I measure synthetic SLIs?

Define clear pass/fail criteria for each test, collect success counts and latencies, and compute rates over rolling windows.

How do I handle secrets in synthetic scripts?

Use a centralized secrets manager with short-lived credentials and avoid embedding secrets in scripts or repos.

How do I reduce alert noise from synthetic checks?

Aggregate failures, adjust thresholds, group related alerts, and suppress during maintenance windows.

How do I validate synthetic tests are reliable?

Run once-per-minute checks in staging and production for a week, then compare to RUM and logs for correlation.

How do I test internal Kubernetes services with synthetic tests?

Deploy probes as CronJobs or sidecars with service account access and export metrics to cluster monitoring.

How do I convince stakeholders to fund synthetic monitoring?

Present cost vs risk: show potential revenue loss avoided by early detection and reduced incident MTTR.

How do I ensure synthetic tests scale?

Use distributed agents, tier sampling, and central telemetry pipelines with efficient encoding and retention policies.

How do I detect regressions caused by third-party changes?

Add schema validation and compare response fingerprints; alert on deviation from baseline.

How do I set initial SLOs from synthetic tests?

Start with conservative but realistic targets informed by historical synthetic latency and success rates.

How do I ensure privacy when storing artifacts?

Mask PII in responses, use redaction, and keep artifacts access-restricted.

How do I integrate synthetic tests with incident management?

Attach run artifacts and traces to alerts and ensure routing rules map to correct on-call teams.

Conclusion

Synthetic Tests are a pragmatic, high-value approach to proactively validate critical application, infrastructure, and integration behavior. When implemented thoughtfully, they reduce detection latency, provide deterministic baselines for SLOs, and enable safer deployments.

Next 7 days plan

Day 1: Inventory top 5 critical user journeys and owners.
Day 2: Implement 3 basic synthetic checks for those journeys.
Day 3: Integrate checks into CI/CD post-deploy stage.
Day 4: Configure SLI collection and a basic dashboard.
Day 5: Define SLOs and alerting thresholds; create runbooks for failures.

Appendix — Synthetic Tests Keyword Cluster (SEO)

Primary keywords

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Related terminology

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Synthetic test SSL validation
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Synthetic test KMS usage
Synthetic test AWS Lambda probes
Synthetic test GCP Cloud Functions probes
Synthetic test Azure Functions probes
Synthetic test Kubernetes CronJob probes
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Synthetic test flaky assertion handling
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Synthetic test dedupe strategies
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Synthetic test DNS split-horizon detection
Synthetic test cache-busting techniques
Synthetic test artifact sampling
Synthetic test screenshot capture on fail
Synthetic test trace correlation id
Synthetic test observability pipeline tuning
Synthetic test retention policy best practices
Synthetic test collaboration with SRE teams
Synthetic test SLA evidence
Synthetic test regulatory compliance checks