Use case

Self-hosted load testing

Learn how LoadStrike approaches self-hosted load testing for transaction-aware workflows across distributed systems.

Self-hosted load testing diagram
Explain the self-hosted deployment model and connect it to pricing, licensing, and cluster docs.
Direct answer

Why does self-hosted matter here?

Self-hosted load testing matters when the team wants the runtime, reports, and distributed execution to live on infrastructure it controls. That is especially relevant for workloads that touch internal systems, private queues or streams, regulated environments, or browser and service flows that should not leave the network boundary.

LoadStrike is explicitly positioned as self-hosted. The site ties that model to its transaction-aware runtime, plan structure, runner-key access, and coordinator-and-agent execution rather than to a cloud load-consumption service.

The user problem

The workload touches internal systems or operational constraints that should stay on infrastructure your team controls.

Why it matters

Private queues, regulated environments, browser dependencies, and cluster placement often make it more important to control the runtime than to rent more external traffic generation.

Best-fit workloads

Where this workload usually needs transaction visibility

Internal service and broker paths

Run transaction-aware tests close to systems that should not leave the network boundary.

Regulated or controlled environments

Keep runtime access, reports, and observability exports inside your own infrastructure.

Self-hosted cluster programs

Scale past one node without switching to a cloud usage-metered model.

Who this is for

Teams that need to run on their own infrastructure while still testing full workflows across APIs, queues or streams, browser journeys, and downstream services.

Why endpoint-only testing breaks down here

Cloud-style endpoint load generation can miss the operational need to keep transport access, browser dependencies, cluster placement, and report artifacts inside the environment the team already controls.

How LoadStrike fits

LoadStrike keeps the product self-hosted, publishes pricing and runner-key expectations for that model, and documents local plus distributed cluster behavior for teams that want one runtime across the systems they already operate.

What to expect

Verified LoadStrike fit points

  • Self-hosted product positioning is visible across the site and pricing.
  • Pricing and security pages explain the plan model and runtime access at a buyer-facing level.
  • Cluster docs cover local and distributed execution patterns.
  • The same runtime can push final run data into supported sinks when the plan includes them.
Resources

Docs and examples

Use these pages to connect the self-hosted model to runtime access and operations.

Pricing

See the self-hosted plan structure and trial path already published on the site.

Cluster overview

See how local and distributed execution are documented publicly.

Common questions

Common questions

Is LoadStrike a self-hosted product?

Yes. The site positions LoadStrike as self-hosted and ties pricing, runtime access, and clustered execution to that deployment model.

Can self-hosted teams still use reporting sinks?

Yes. The docs cover built-in reporting sinks for InfluxDB, TimescaleDB, Grafana Loki, Datadog, Splunk HEC, and OTEL Collector on Business and Enterprise.

What should a self-hosted team read after this page?

Start with pricing, security and data handling, and the cluster overview so the commercial model, runtime access, and execution footprint are all clear before the rollout starts.

Related

Related documentation

Start with the implementation details that match this page.

Cluster Overview

Cluster mode lets one LoadStrike run spread across multiple nodes. Use it when a single machine is not enough or when topology matters.

Related

Related comparisons

Use these comparison pages if you still need a tool-level decision.

LoadStrike vs k6

Compare LoadStrike and k6 across code ergonomics, protocol scope, downstream correlation, reporting depth, browser workflows, and distributed self-hosted execution.

LoadStrike vs Apache JMeter

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

Related

Related integrations

Connect the run output to the observability backend your team already uses.

LoadStrike and Datadog

See how the LoadStrike Datadog sink fits into transaction-aware, self-hosted load testing workflows.

Related

Next steps

Keep moving with the most relevant follow-up pages.

Pricing

Review the public trial and plan details.

Next step

Next step

Review pricing first, then connect the self-hosted rollout back to the cluster model your team plans to operate.