Preparation Checklist

About

Before executing a load test, proper preparation ensures the test is meaningful, accurate, and reliable. This page outlines all the required inputs, resources, and decisions that must be made prior to testing, grouped into general and type-specific categories.

General Preparation (Applicable to All Types)

Before any load test whether targeting an API, UI flow, backend service, or database we must establish a solid foundation. General preparation ensures our tests are purpose-driven, repeatable, and technically valid, and helps avoid wasted time, skewed results, or production risk.

Below are the core areas of general preparation, applicable across all types of load testing targets:

1. Clearly Define the Testing Objective

Every load test must have a well-defined, measurable goal. This prevents the test from being just an exercise in sending traffic, and instead makes it an intentional performance validation.

Typical objectives include:

• Establishing baseline performance under expected load

• Identifying the system’s breaking point

• Measuring response time and throughput against service-level targets

• Observing resource utilization trends over time (soak testing)

• Evaluating resilience under failure or saturation conditions

Why it matters: Without a clear objective, it’s impossible to know whether the test passed, failed, or provided any useful insight.

2. Quantify the Expected Load

We must know how much and what kind of load to simulate. This includes not just user counts, but also the volume, frequency, and types of operations they perform.

Key dimensions:

• Concurrent users or sessions

• Transactions per second (TPS) or requests per second (RPS)

• Peak load windows (e.g., login bursts every morning)

• Session duration and think times

Sources:

• Production access logs

• Application monitoring dashboards

• Business SLAs or capacity planning targets

Why it matters: Arbitrary numbers produce unrealistic pressure or underwhelming results, which make tests unrepresentative of real-world conditions.

3. Identify Key Scenarios and User Flows

Determine which user interactions are critical to simulate under load. This may be:

• End-to-end user journeys (e.g., login → dashboard → transaction)

• Isolated business-critical functions (e.g., payment processing)

• Background processes (e.g., job scheduling, data syncs)

Design flows based on:

• Actual usage frequency

• Risk or business importance

• Historical incidents or known hot paths

Why it matters: Not all requests are equal. High-frequency or mission-critical scenarios should be prioritized in the load model.

4. Validate Test Environment Suitability

Load testing should be conducted in a realistic environment. Testing on our laptop or in a dev sandbox can skew results.

Essential environment considerations:

• Infrastructure parity with production (CPU, memory, autoscaling)

• Identical configurations (app settings, JVM flags, DB indexing)

• Presence of all integrations and middleware

• No background jobs, test noise, or deployments during load tests

Also confirm:

• Network paths are similar to real users (latency, firewalls)

• Caching layers and queues are not bypassed

Why it matters: If the environment doesn’t match production behavior, the results won’t generalize. Bottlenecks may go undetected or be falsely flagged.

5. Prepare Reliable Test Data

Most scenarios rely on valid input data such as user accounts, authentication tokens, or domain-specific objects (e.g., orders, products).

Checklist:

• Sufficient quantity of valid, reusable data

• Clean data states (e.g., no partially processed entities)

• Dynamic data generation or parameterization support

• Preloading for dependent entities (e.g., valid product catalog)

Also prepare invalid or boundary-case data for failure-path testing.

Why it matters: Bad or insufficient data can result in test errors, retries, or skewed performance — making the test unreliable.

6. Define Performance Benchmarks or SLAs

Before measuring anything, we need to know what success looks like.

Benchmarks should cover:

• Response time thresholds (e.g., median < 500 ms, 95th percentile < 2s)

• Error rate limits (e.g., no more than 1% 5xx errors)

• Resource usage expectations (e.g., CPU < 75%, memory < 80%)

• Throughput targets (e.g., 1000 requests/minute sustained)

We can use historical data, business SLAs, or engineering guidelines to define thresholds.

Why it matters: Results are only meaningful when compared to a clearly defined standard.

7. Prepare and Validate Tooling

Choose and configure the tools we'll use for test scripting, execution, and result collection.

Essential tools may include:

• Load generators (e.g., JMeter, k6, Gatling)

• Parameterization and data feeders (e.g., CSV, JSON, APIs)

• Result reporters (HTML reports, InfluxDB + Grafana)

• Test orchestration (e.g., Jenkins, GitHub Actions)

Ensure:

• Scripts run cleanly at small scale

• Assertions and response validations are included

• Tooling doesn’t itself become a bottleneck

Why it matters: Misconfigured tools may create test errors or distort metrics. Tooling must be tested before actual execution.

8. Enable Monitoring and Logging

Observability is essential. Our load test must be monitored at multiple layers, in real time.

Enable collection of:

• Application metrics (response times, active threads, GC activity)

• Infrastructure metrics (CPU, memory, disk, network)

• Database metrics (connection pool, query latency, IOPS)

• Logs (error logs, timeouts, retries)

Integrate dashboards like Grafana, Prometheus, CloudWatch, or ELK for visual monitoring during and after the test.

Why it matters: Without visibility into system internals, interpreting test results is guesswork.

1. API Load Testing Preparation

• Items marked "Yes" are considered mandatory for all API load tests.

• Items marked "Conditional" depend on test type, API nature, and organization-specific policies.

Category	Item	Description / Details	Mandatory
1. Objective Definition	Test Objective	Define what you are testing: performance, scalability, stability, or regression.	Yes
	Success Criteria	Define SLAs or benchmarks for response time, error rate, and throughput.	Yes
	Expected Traffic Pattern	Choose traffic shape: steady, spike, ramp-up, soak, or burst load.	Yes
2. API Specifications	List of Endpoints	List of API URLs and HTTP methods (GET, POST, PUT, etc.) to be tested.	Yes
	Request/Response Schema	Sample request bodies and expected response formats for each API.	Yes
	API Documentation	OpenAPI/Swagger spec or Postman collection to understand APIs clearly.	Yes
	Dependency Map	Note any downstream systems or services each endpoint interacts with.	Conditional
3. Authentication	Auth Mechanism	Identify if the API uses Basic Auth, API keys, JWT, or OAuth2.	Yes
	Token Generation	Understand how to generate or acquire tokens (e.g., login, service call).	Yes
	Token Expiry Handling	Account for token expiry and renewal if tests run long.	Conditional
	Role/Access Levels	Determine whether different endpoints require different user roles.	Conditional
4. Test Data	Valid Data Samples	Collect payloads, query parameters, and path variables required by APIs.	Yes
	Parameterization Strategy	Plan for dynamic test data (e.g., user IDs, timestamps, random values).	Yes
	Number of Unique Users	Estimate how many distinct users are needed to simulate concurrency.	Conditional
	Preloaded Entities	Ensure any related objects (products, orders) are created before the test.	Conditional
	Data Cleanup Plan	Plan for test data cleanup, especially for write-heavy operations.	Conditional
5. Load Design	Concurrent Users	Set the number of virtual users to simulate parallel requests.	Yes
	Requests per Second (RPS)	Define the target throughput or request volume over time.	Yes
	Ramp-up Time	Specify how quickly the test load will increase to simulate real usage patterns.	Yes
	Think Time / Pacing	Set user delay between steps to mimic real user behavior.	Conditional
	Test Duration	Decide how long the load will be applied (short bursts or endurance).	Yes
6. Environment	Target Environment	Ensure you know the base URL or hostname of the API environment to test.	Yes
	Production Parity	Confirm how closely the test environment matches production in infra and config.	Conditional
	Isolation	Prevent other tests or deployments from running during your test.	Yes
	Monitoring Access	Ensure visibility into system metrics during test execution.	Yes
7. Tooling	Load Testing Tool	Decide which tool will be used (e.g., JMeter, k6, Gatling, Locust).	Yes
	Tool Installation and Setup	Verify installation and configuration of the tool and supporting dependencies.	Yes
	Script Parameterization	Incorporate dynamic values (e.g., CSV, feeders) in test scripts.	Yes
	Response Validation	Include logic to check HTTP status codes and content correctness.	Yes
	Script Dry Run	Run the script with minimal load to ensure correctness before applying full load.	Yes
8. Monitoring	Application Metrics	Setup dashboards for response time, error count, latency, and request distribution.	Yes
	System Metrics	Track CPU, memory, disk I/O, and network activity.	Yes
	Database Metrics	Monitor query time, connection pool size, and slow query logs.	Conditional
	Logging System	Enable access to logs for application, exceptions, and infrastructure.	Yes
	Dashboards	Ensure dashboards (e.g., Grafana, CloudWatch) are active to observe live trends.	Conditional
9. Governance	Rate Limits or API Quotas	Be aware of any platform-level or gateway-level limits (e.g., 1000 RPS).	Conditional
	Throttling and Retry Logic	Know how the system handles overload, retries, or 429/503 errors.	Conditional
	Notification to Stakeholders	Inform DevOps, Infra, and QA teams about test timing to avoid conflicts.	Conditional
	Rollback Plan in Case of Side Effects	Plan how to undo test changes (e.g., mass test user creation or transactions).

2. UI Load Testing Preparation

• Mandatory = Yes: Must be present in any serious UI load test.

• Mandatory = Conditional: Depends on the application structure, scope of testing, or infrastructure setup.

Category	Item	Description / Details	Mandatory
1. Objective Definition	Test Objective	Define whether you are testing performance, UX under load, or frontend/backend interaction latency.	Yes
	Success Criteria	Target page load times, acceptable UX delays, error thresholds, or component rendering completion.	Yes
	Load Profile	Number of simulated users, load duration, and flow repetition frequency.	Yes
2. Scenario Planning	User Journeys / Click Paths	Define key flows to simulate (e.g., login → dashboard → search → edit profile).	Yes
	Pages or Screens Under Test	Identify which screens will be opened and interacted with during the test.	Yes
	Think Time / User Delay	Define wait times between user actions to simulate real behavior.	Yes
	Data Variation	Plan dynamic user data (e.g., usernames, product IDs) for different sessions.	Conditional
3. Authentication	Auth Mechanism	How users log in (form-based, SSO, token injection), and how it’s scripted in the tool.	Yes
	Session Management	Handle cookies, session expiry, and CSRF tokens between steps.	Yes
	User Pool	Pool of test user accounts to be used in parallel (unique or shared users).	Yes
4. Element Handling	DOM Element Locators	Accurate selectors for buttons, input fields, and links (IDs, class names, XPath, etc.).	Yes
	Dynamic Element Readiness	Use appropriate waits for JS-loaded content (explicit waits, condition checks).	Yes
	Page Load Synchronization	Define when a page is considered "fully loaded" (DOM ready, AJAX complete, etc.).	Yes
5. Tooling	Testing Tool	Tool choice (e.g., Selenium Grid, Playwright, Cypress with parallelization plugins).	Yes
	Execution Mode	Choose headless vs. headed browsers based on test scope and infra limitations.	Yes
	Scripting Language	Confirm scripting language used (Java, Python, JS, etc.) and framework readiness.	Yes
	Parallel Execution Setup	Ability to run multiple browser instances or distribute across nodes.	Yes
	Load Injection Controller	Use a controller to schedule and manage browser sessions and user flows.	Conditional
6. Test Data	Input Data for Forms/Actions	Prepare email IDs, names, addresses, or any form inputs needed.	Yes
	Visual Assets (if needed)	Images, files, or attachments to simulate file uploads or form completions.	Conditional
	State Preparation	Ensure required backend state (e.g., existing orders, empty carts) is setup before test.	Conditional
7. Infrastructure Setup	Execution Nodes	Sufficient and scalable VMs/containers to run browser instances in parallel.	Yes
	Browser Drivers / Dependencies	Install and configure browser drivers (e.g., ChromeDriver, GeckoDriver).	Yes
	Tool Agent Setup	Tools like Selenium Grid or Playwright Test Runner must be ready and registered with nodes.	Yes
8. Environment Readiness	Test Environment URL	Provide base URL of the UI environment to be tested (e.g., staging, UAT).	Yes
	Env Isolation / Stability	No deployments or parallel load on the environment during testing.	Yes
	Responsive / Device Target	Define if testing desktop only or also mobile/tablet via viewport resizing.	Conditional
9. Monitoring	Page Load Time Tracking	Use browser timers or frontend logs to capture load/render times.	Yes
	JavaScript Error Logging	Collect any frontend errors/exceptions that occur during test.	Yes
	Backend/API Metrics	Complement UI test with backend metrics to correlate slowdowns.	Conditional
	Video or Screenshot Capture	Capture videos or screenshots for failed flows or checkpoints.	Conditional
10. Governance	Test Duration Limits	Ensure browser tests don’t run excessively long to avoid memory leaks or browser crashes.	Yes
	License Limits (if using commercial tool)	Be aware of any license or concurrency caps.	Conditional
	Stakeholder Notification	Notify QA, Dev, and Infra teams of the test schedule and expectations.	Conditional
	Result Reporting	Plan to generate readable reports (HTML, dashboards, CI uploads) post test.

3. Backend/Messaging Load Testing Preparation

• Mandatory = Yes: Must be present in any serious UI load test.

• Mandatory = Conditional: Depends on the application structure, scope of testing, or infrastructure setup.

Category	Item	Description / Details	Mandatory
1. Objective Definition	Test Objective	Define the purpose: throughput testing, latency measurement, queue depth handling, or failure resilience.	Yes
	Success Criteria	Define SLAs for processing time, message loss, retries, dead-letter thresholds, etc.	Yes
	Load Pattern	Constant, ramp-up, burst, or soak — define load profile over time.	Yes
2. Entry Point & Triggers	Type of Backend Component	Identify whether it’s an event listener, scheduled batch job, queue consumer, webhook handler, etc.	Yes
	Trigger Mechanism	How is the process triggered? (e.g., Kafka topic, JMS queue, cron, webhook, internal poller)	Yes
	Trigger Source Simulation	Simulate or mock producers/publishers to generate the load.	Yes
	Queue/Topic Name	Exact name of the queue/topic/endpoint to which load will be published.	Yes
	Message Protocol and Format	Know the protocol (e.g., AMQP, JMS, Kafka) and message format (JSON, XML, binary).	Yes
3. Message Content	Message Payload Sample	Prepare valid, realistic payloads to simulate production behavior.	Yes
	Field Variation Strategy	Parameterize fields like IDs, timestamps, values for randomness.	Yes
	Message Size Estimation	Understand average vs max payload size to test serialization, bandwidth, and memory use.	Yes
	Message Rate	Define messages per second (MPS), total messages, batch size, or scheduling interval.	Yes
4. Queue/System Behavior	Consumer Configuration	Confirm consumer app settings: concurrency, batch size, thread pool, ack strategy.	Yes
	Retry and Error Handling	Understand retry policy, delay, and handling logic in case of failures.	Yes
	Dead-Letter Queue Behavior	Know where failed messages go, and how they’re processed or alerted.	Conditional
	Ordering & Deduplication Strategy	If ordering matters, confirm how it is handled (e.g., Kafka partitions, idempotency keys).	Conditional
5. Test Data & Dependencies	Data Initialization	Required setup data in DB or external services for the processing logic to function.	Conditional
	External Calls	Any dependent APIs or systems the backend connects to — check availability or stub them.	Yes
	Test Isolation	Ensure no cross-contamination of production queues or shared infrastructure.	Yes
6. Infrastructure Setup	Queue Broker Setup	Kafka, RabbitMQ, ActiveMQ, etc. — ensure correct version, cluster size, and topic/queue configuration.	Yes
	Monitoring Integration	Hook into metrics (e.g., consumer lag, queue depth, processing latency, error count).	Yes
	Resource Scaling & Limits	Ensure backend service, DB, and broker nodes have proper CPU, memory, and disk allocation.	Yes
7. Tooling	Load Publisher Tool	Choose tools like Kafka CLI, custom Java/Python publisher, JMeter with JMS plugin, or Testcontainers.	Yes
	Automation Script	Scripted mechanism to generate load in volume — through CLI, CI job, or test harness.	Yes
	Replay of Historical Messages	Optional: ability to replay previously captured messages for realistic test.	Conditional
	Metrics Collection Setup	Ensure metrics from consumer, broker, and infra are captured in tools like Prometheus, Grafana, or ELK.	Yes
8. Observability & Alerts	Consumer Lag Metrics	Monitor time or count lag between publish and consume (esp. for Kafka).	Yes
	Error Logs & Exception Tracing	Ensure logs are tailing correctly for runtime issues or deserialization failures.	Yes
	Broker Health	Monitor queue/topic depth, partition lag, dropped messages, and delivery latency.	Yes
	Alert Suppression or Notification	Disable or mute alerts if test volume will trigger false positives.	Conditional
9. Cleanup Plan	Post-Test Message Purge	Strategy to clean up test messages from queues, logs, or DB.	Yes
	Test Data Reset	Rollback or truncate any changes to maintain clean state.	Conditional
	Resource Teardown	Shut down any temporary services or test infrastructure after testing completes.	Conditional
10. Stakeholder Coordination	Test Schedule Announcement	Inform stakeholders (DevOps, QA, Infra, Observability) of timing and scope.	Conditional
	SLA Violation Notification	Know who to contact in case load test reveals service breakdowns.	Conditional
	Result Reporting Strategy	Define how test results will be published, shared, and reviewed.	Yes

4. Database Load Testing Preparation

• Mandatory = Yes: Must be present in any serious UI load test.

• Mandatory = Conditional: Depends on the application structure, scope of testing, or infrastructure setup.

Category	Item	Description / Details	Mandatory
1. Objective Definition	Test Objective	Define goal: max throughput, query latency, concurrent connections, connection pool testing, write capacity, etc.	Yes
	Success Criteria	Set benchmarks for query response times, TPS, CPU usage, IOPS, replication delay, etc.	Yes
	Load Profile	Define traffic pattern: steady, burst, ramp-up, long-duration (soak), etc.	Yes
2. Scope Definition	Query Types	Specify which types of queries will be tested (SELECT, INSERT, UPDATE, DELETE, complex JOINs).	Yes
	Load Entry Point	Will queries hit DB directly (e.g., via JDBC) or through backend services/APIs?	Yes
	Table Coverage	List which tables, collections, or partitions are involved in the test.	Yes
3. Data Strategy	Test Dataset	Prepare test data for query execution — realistic and of meaningful volume.	Yes
	Dataset Size	Ensure dataset size reflects production-like distribution for indexes, partitions, joins, etc.	Yes
	Data Loading Method	Script or mechanism to pre-load DB with required data.	Yes
	Data Refresh Plan	Strategy to reload/reset DB before each test iteration.	Conditional
	Data Consistency	Ensure referential integrity and constraints are honored if applicable.	Yes
4. Query Execution Setup	Query Frequency	Define how many queries per second, per thread, or per session are to be fired.	Yes
	Parameterization Strategy	Use variable input values for dynamic queries to avoid result caching.	Yes
	Transaction Control	Define if queries are inside transactions (BEGIN/COMMIT), and how isolation levels affect performance.	Conditional
	Query Caching Strategy	Understand and configure caching (app-side or DB-side) for realistic simulation.	Conditional
5. Connection Management	Number of Connections	Define total active connections during load test.	Yes
	Connection Pool Size	Configure pool size in app/server (e.g., HikariCP, Tomcat JDBC).	Yes
	Pool Exhaustion Strategy	Define behavior when pool is full — queue, fail, grow.	Conditional
	Idle and Timeout Settings	Check timeout, eviction, and idle thresholds for connection reuse.	Conditional
6. Indexing & Optimization	Index Coverage	Ensure queries have indexes or deliberately test index-less cases.	Yes
	Query Plan Review	Use EXPLAIN PLAN or profiling to verify execution paths.	Yes
	Partitioning/Sharding Awareness	If DB is partitioned, validate how load is distributed and whether skew occurs.	Conditional
7. Infrastructure Setup	Database Instance Size	Instance type, CPU/memory limits, IOPS provisioning must be clearly defined.	Yes
	Disk Configuration	Disk type (SSD, network-attached), write IOPS, buffer pool size, redo/undo configuration.	Conditional
	Replication Setup	If replication is enabled, test replication lag or sync pressure under load.	Conditional
	Monitoring Agent Setup	Ensure metrics collection tools are ready — Prometheus exporters, APM agents, CloudWatch, etc.	Yes
8. Metrics & Observability	Query Latency	Capture metrics on min/avg/max response times.	Yes
	Error Rate	Track query failures, connection errors, constraint violations, etc.	Yes
	Resource Metrics	Monitor CPU, memory, disk I/O, buffer pool hits, table scans.	Yes
	Connection Usage	Observe active/idle connections, failed handshakes, connection churn.	Yes
	Locking/Deadlocks	Track contention — row locks, table locks, blocking queries.	Conditional
9. Tooling	Load Test Tool	Choose load generator (e.g., JMeter JDBC plugin, custom Java app, BenchmarkSQL).	Yes
	Script/Query Execution Framework	Implement repeatable, parameterized query execution.	Yes
	Data Generator Tool	Optionally use tools like dbForge, pgbench, or Flyway to prepare data.	Conditional
	Logging/Profiling Tool	Enable slow query logs, AWR (Oracle), pg_stat_statements (PostgreSQL), etc.	Yes
10. Governance	Access Control	Validate user roles used in test — read-only, DML, admin users.	Yes
	Isolation from Production	Never load test production DB. Always use a replica or test-specific environment.	Yes
	Maintenance & Backup Window Awareness	Avoid running during DB maintenance jobs, backup windows, or migration schedules.	Yes
	Alert Mute Strategy	Temporarily mute alerts for CPU, locks, or slow queries during test execution.	Conditional
	Test Timing Approval	Coordinate with DBA and infra teams on test schedule and capacity impact.	Yes
11. Post-Test Cleanup	Data Cleanup Plan	Truncate tables or restore DB snapshot after test completion.	Yes
	Resource Release	Shut down test instances or scale down cloud infra if scaled up temporarily.	Conditional
	Result Storage	Save logs, metrics, and query plans for review.	Yes

5. Third-Party API Testing Preparation

• Mandatory = Yes: Must be present in any serious UI load test.

• Mandatory = Conditional: Depends on the application structure, scope of testing, or infrastructure setup.

Category	Item	Description / Details	Mandatory
1. Objective Definition	Test Objective	Define what you are validating — integration resilience, API SLA adherence, fallback logic, or proxy behavior.	Yes
	Scope of Load	Clarify if full-scale load will hit the third-party directly or only through stubs/mocks.	Yes
	Success Criteria	Response times, status codes, retry handling, rate limits, and fallback performance.	Yes
	Legal/Policy Compliance	Confirm load test is allowed under the provider’s usage policy and contract.	Yes
2. Access & Authentication	Auth Type	API key, OAuth2, JWT, HMAC signatures — know the auth mechanism and how to simulate it.	Yes
	Token Validity	Understand token expiry, refresh logic, and how your script will handle it.	Yes
	Test Environment URL	Use sandbox/test environments provided by third-party APIs (never test against production).	Yes
	Credential Restrictions	Ensure test users have correct roles or access boundaries.	Yes
3. Test Scope Definition	Endpoint Coverage	List of endpoints to be tested (e.g., GET /user, POST /transaction).	Yes
	Supported Methods	HTTP methods used (GET, POST, PUT, DELETE), content types (JSON, XML).	Yes
	Payload Preparation	Prepare realistic sample requests — valid and invalid ones.	Yes
	Dependency Impact	Know how third-party API affects downstream flows (e.g., OTP triggers, invoice creation).	Conditional
4. Rate Limiting & Quotas	Published Rate Limits	Understand daily/monthly quotas, burst capacity, and penalties for overuse.	Yes
	Throttling Mechanism	Identify how rate-limiting is enforced (HTTP 429, delayed response, token bucket).	Yes
	Retry Strategy	Know how your client retries, what backoff is used, and how retries are tracked.	Yes
	Quota Management	Track usage vs. quota during testing to avoid unintentional overrun.	Yes
5. Error & Edge Case Handling	Known Error Codes	Collect all expected error responses (403, 429, 500) and how they are triggered.	Yes
	Error Injection Scenarios	Optionally simulate network delays, timeouts, or 5xx responses to test your system's resilience.	Conditional
	Fallback Logic	Ensure your application has circuit breaker or fallback mechanisms enabled for failures.	Yes
6. Test Design	Load Profile	Users, requests per second, ramp-up time, total duration — define your load design.	Yes
	Data Variation	Randomize IDs, names, payloads to avoid duplication or caching issues.	Yes
	Think Time	Simulate real usage with pauses between calls.	Yes
	Isolation Plan	Avoid running parallel tests that might interfere with quota or behavior.	Yes
7. Tooling & Execution	Load Tool Selection	Choose a tool (e.g., JMeter, Postman CLI, k6, Locust) that supports required auth and response checks.	Yes
	Script Parameterization	Support for variable input and dynamic payloads.	Yes
	Logging and Debugging	Capture logs for request/response payloads and headers for validation.	Yes
	Sandbox Limitations	Understand what’s simulated vs. live in the test environment (e.g., fake payments, email delivery mock).	Yes
8. Observability	Response Time Monitoring	Track and log latency of each call.	Yes
	Status Code Distribution	Monitor status codes to detect error spikes.	Yes
	SLA Compliance	Compare observed latency to contractual SLAs if available.	Conditional
	Alerting Suppression	Prevent automated alerting for expected failures during testing (both your side and theirs).	Conditional
9. Stakeholder Communication	Notify Provider (if required)	Some vendors require prior notice or approval for load testing (e.g., Stripe, Twilio).	Conditional
	Internal Coordination	Inform internal teams (Infra, DevOps, QA, Observability) about test window.	Yes
	Legal / Compliance Approval	Get internal clearance if the provider or your app handles regulated data (e.g., payments, healthcare).	Conditional
10. Post-Test Activities	Result Validation	Review latency, error trends, and retry success rate after the test.	Yes
	Quota Reset / Replenishment	Request additional quota or wait for reset if limits were hit.	Conditional
	Test Data Cleanup	If any real data was created (e.g., test users), ensure it is cleaned up.	Yes
	Documentation	Capture the entire test configuration and results for future audits or optimization.	Yes