Gatling alternative: a worked migration guide

Gatling is one of the few load testing tools where the scenario DSL itself is genuinely well-designed. The reason teams move off it is rarely "the DSL is bad" — it's that JVM operations, Scala maintenance, and the inability to share results with non-engineers add up. This guide translates a real Gatling simulation to LoadTester, then is honest about where Gatling wins.

Quick verdict

Treat this as an operating-model comparison. Gatling is strong when simulations are software artifacts owned by performance engineers; LoadTester is stronger when common HTTP/API checks need to be created, rerun, and understood by a wider delivery team.

Starting point: a Gatling simulation

Real-world Gatling Scala simulation — login, browse, checkout, with a feeder for parameterization:

// CheckoutSimulation.scala
import io.gatling.core.Predef._
import io.gatling.http.Predef._
import scala.concurrent.duration._

class CheckoutSimulation extends Simulation {

  val httpProtocol = http
    .baseUrl("https://staging.example.com")
    .acceptHeader("application/json")
    .contentTypeHeader("application/json")

  val skuFeeder = csv("skus.csv").random

  val checkoutScenario = scenario("Checkout")
    .exec(http("login")
      .post("/api/auth")
      .body(StringBody("""{"email":"test@example.com","password":"test"}"""))
      .check(jsonPath("$.token").saveAs("token"))
    )
    .pause(1, 3)
    .exec(http("browse")
      .get("/api/products?category=shoes")
      .header("Authorization", "Bearer ${token}")
    )
    .pause(1, 3)
    .feed(skuFeeder)
    .exec(http("checkout")
      .post("/api/checkout")
      .header("Authorization", "Bearer ${token}")
      .body(StringBody("""{"sku":"${sku}","qty":1}"""))
      .check(status.is(200))
    )

  setUp(
    checkoutScenario.inject(
      rampUsersPerSec(10).to(200).during(30.seconds),
      constantUsersPerSec(200).during(60.seconds),
    )
  ).protocols(httpProtocol)
   .assertions(
     global.responseTime.percentile3.lt(400),
     global.failedRequests.percent.lt(2),
   )
}

Run with ./mvnw gatling:test after configuring the Gatling Maven plugin. The simulation has four moving parts: HTTP protocol config, scenario chain, injection profile (ramp + plateau), and global assertions. Each translates to a different LoadTester concept.

The translation

Protocol config → test settings

Base URL, default headers, content type — all set once on the LoadTester test definition. No code, just fields.

Scenario chain → step sequence

Gatling's .exec(...).pause(...).exec(...) chain becomes an ordered list of steps in the scenario:

Scenario "Checkout"
  Step 1: login
    POST /api/auth
    Body:    {"email":"test@example.com","password":"test"}
    Capture: $.token -> token
  Pause: 1–3s

  Step 2: browse
    GET /api/products?category=shoes
    Header: Authorization: Bearer {{token}}
  Pause: 1–3s

  Step 3: checkout
    POST /api/checkout
    Header: Authorization: Bearer {{token}}
    Body:   {"sku":"{{sku}}","qty":1}
    Assert: status == 200

Feeder → CSV data source

Gatling's csv("skus.csv").random maps directly to LoadTester's CSV data source. Upload the same skus.csv, set the strategy to "random per iteration." Same behavior.

Injection profile → load shape

This is where Gatling has more expressive power. Its injection DSL — rampUsersPerSec, constantUsersPerSec, heavisideUsers, incrementUsersPerSec — covers many shapes. LoadTester offers presets (constant, ramp, ramp + hold + decline, spike) plus custom multi-stage shapes. The 90% case is covered; the bespoke 10% (e.g., heavisideUsers mathematical injection) is not.

Global assertions → thresholds

global.responseTime.percentile3.lt(400) becomes a p95 threshold of 400ms. global.failedRequests.percent.lt(2) becomes an error-rate threshold of 2%. Same semantics, different syntax.

Feature parity matrix

Where Gatling is genuinely the better tool

Gatling earns its reputation. Don't migrate if any of these apply:

• Your scenarios use complex conditional flow. Gatling's doIf, doIfOrElse, tryMax, asLongAs let you express realistic user behavior with branching that depends on response content. LoadTester's scenarios are linear with simple assertions; arbitrary control flow isn't supported.
• You're already deep in Scala/JVM. If your engineering org is JVM-shaped and Maven/Gradle is the standard build, Gatling fits the existing toolchain. Migrating means stepping outside that.
• Bespoke injection profiles matter. If you genuinely need heavisideUsers or custom mathematical injection that doesn't fit ramp/hold patterns, Gatling's DSL is the right tool.
• You need WebSocket, SSE, or gRPC support. Gatling has modules for all three. LoadTester is HTTP-focused.
• You operate at a scale where the cost of cloud workers exceeds your existing JVM infrastructure cost. Self-hosted Gatling on owned hardware is free at any volume.

Where LoadTester tends to win

• The team isn't all engineers. Gatling reports are HTML files. LoadTester runs are URLs you can paste into Slack and have a PM read.
• Run history matters. Gatling produces a report per run with no built-in comparison; you build a history yourself. LoadTester stores every run and compares them automatically.
• You don't want a JVM in your test infrastructure. Gatling needs a JVM, Maven or Gradle, and Scala compilation. LoadTester is a managed service.
• Tests need to be edited by people who don't write Scala. Adding a header or changing a payload in Gatling means editing Scala and rerunning the build. In LoadTester it's a UI edit.

Migration checklist

1. Inventory existing simulations. Flag any that use doIf, asLongAs, custom injection math, or non-HTTP protocols — these may not migrate.
2. Pick the highest-pain Gatling test (the one nobody likes editing) and translate it first using the steps above.
3. Run both versions against the same target. Confirm the numbers agree within noise.
4. Wire one CI release gate to the LoadTester test instead of the Gatling Maven plugin.
5. For the rest: migrate as you'd otherwise edit. When a simulation needs significant rework, recreate it in LoadTester instead of patching the Scala.
6. Keep Gatling for the simulations flagged in step 1.

How this comparison was evaluated

For this Gatling alternative page, we evaluated the tradeoff between code-first control and operational accessibility. We looked at simulation authoring, developer skill requirements, CI ownership, reporting, run history, team handoff, and whether the scenario is complex enough to justify a specialist DSL.

Gatling remains a strong choice for teams already invested in its simulation model. LoadTester is stronger when the workload is a common HTTP/API release check and the organization wants more people to participate in interpreting results.

When you should stay on Gatling

• Your team writes Scala, Kotlin, or Java and treats performance test design as software engineering, not configuration.
• You need Gatling Enterprise's specific governance, RBAC, or compliance features for an existing procurement-bound deployment.
• You're benefiting from Gatling's per-node scalability (3,000–5,000 VUs/instance) and have the operational maturity to run it.

FAQ

What problem should the alternative actually solve?

Be specific. If the answer is "we don't share results well" — many tools fix that. If the answer is "we need WebSocket support without a JVM" — fewer tools fix that. The right alternative depends on which constraint dominates.

What's the realistic timeline?

For a team with 5–10 simulations and most of them HTTP-only: 2–4 weeks for full migration including CI integration. For teams with 50+ simulations using Gatling's full DSL: plan a quarter and expect to keep some in Gatling permanently.

Should I migrate everything?

No. The right outcome is a hybrid where Gatling stays for what it's specifically good at (complex flow, custom injection, non-HTTP protocols) and LoadTester takes the routine HTTP and API release-gate workload.