Microservices load testing

Microservices performance should be tested around the business workflow that crosses the service graph, not just around isolated service endpoints. The important question is whether the path through the relevant services still completed on time and correctly under load.

LoadStrike is designed for that transaction view. It can keep APIs, queues or streams, browser actions, and downstream services in one self-hosted runtime so teams can inspect the full workflow instead of stitching per-service evidence later.

Who this is for

Platform, service, QA, and performance teams working on service graphs where completion depends on multiple internal systems participating in one transaction.

Why endpoint-only testing breaks down here

Per-endpoint tests rarely show where the overall workflow slowed first when latency is introduced by fan-out, asynchronous work, retries, or downstream service dependencies that appear after the first edge call.

How LoadStrike fits

LoadStrike keeps the transaction intact across service boundaries, uses grouped reporting and correlation to surface uneven outcomes, and supports clustered execution when microservices programs need broader self-hosted coverage.

Transaction concept

Define the service graph as one workflow first.

Quick start

Build a minimal transaction before expanding to a larger service path.

Reports overview

See how service-path failures surface in the run artifact.

Distributed load testing

Review the clustered execution model for larger service programs.

Why is microservices load testing hard to reduce to single endpoints?

Because many user-visible outcomes depend on more than one service participating in the transaction. A fast ingress endpoint does not prove the service graph stayed healthy end to end under load.

Can LoadStrike include queues or streams inside a microservices scenario?

Yes. The public site documents APIs, queues, streams, browser journeys, and downstream services as part of the same transaction-aware workflow model.

What should a microservices team read after this page?

Start with the transaction concept, quick start, reports overview, and distributed load testing pages so the workflow model, first scenario, diagnostics, and cluster options are all clear before rollout expands.

What Is A Transaction?

A transaction in LoadStrike is the full workflow you care about, not just one request. Read this page first if your workload crosses systems.

Quick Start

Build one simple transaction, attach correlation, and run it. Use this page when you want the shortest path to a working LoadStrike test.

Report Overview

This page explains how to read a LoadStrike report. Use it when you want to know what each section means and where to look first.

LoadStrike vs Gatling

Compare LoadStrike and Gatling across scenario discipline, request modeling, downstream visibility, transport breadth, reporting depth, and self-hosted operations.

LoadStrike vs Apache JMeter

Compare LoadStrike and Apache JMeter across scenario design, protocol coverage, downstream correlation, browser workflows, reporting, and self-hosted operations.

LoadStrike and OTEL Collector

See how the LoadStrike OTEL Collector sink fits into OpenTelemetry-oriented reporting workflows.

LoadStrike and TimescaleDB

See how the LoadStrike TimescaleDB sink fits into transaction-aware reporting workflows and public Grafana starter assets.

End-to-end load testing

See the broader full-workflow page.

Event-driven load testing

Drill into the async half of many microservices systems.

OTEL Collector integration

Connect run data to an existing observability pipeline.