diff --git a/build.sbt b/build.sbt
index 9c60ff7fa5..1397effc47 100644
--- a/build.sbt
+++ b/build.sbt
@@ -504,7 +504,6 @@ lazy val perfServerJavaOptions = List(
   "-Xmx16g",
   "-XX:+AlwaysPreTouch"
 )
-lazy val flightRecordingJavaOpts = "-XX:StartFlightRecording=filename=recording.jfr,dumponexit=true,duration=120s"
 
 lazy val perfTests: ProjectMatrix = (projectMatrix in file("perf-tests"))
   .enablePlugins(GatlingPlugin)
@@ -535,8 +534,8 @@ lazy val perfTests: ProjectMatrix = (projectMatrix in file("perf-tests"))
   .settings(
     fork := true,
     connectInput := true,
-    Compile / run / javaOptions ++= flightRecordingJavaOpts :: perfServerJavaOptions,
-    Test / run / javaOptions --= flightRecordingJavaOpts :: perfServerJavaOptions
+    Compile / run / javaOptions ++= perfServerJavaOptions,
+    Test / run / javaOptions --= perfServerJavaOptions
   )
   .jvmPlatform(scalaVersions = List(scala2_13))
   .dependsOn(core, pekkoHttpServer, http4sServer, nettyServer, nettyServerCats, playServer, vertxServer, vertxServerCats)
diff --git a/perf-tests/README.md b/perf-tests/README.md
index 11da164988..4d58194842 100644
--- a/perf-tests/README.md
+++ b/perf-tests/README.md
@@ -34,8 +34,25 @@ If you want to run a test server separately from simulations, use a separate sbt
 perfTests/runMain sttp.tapir.perf.apis.ServerRunner http4s.TapirMulti
 ```
 
-This is useful when profiling, as `perfTests/runMain` will be a forked JVM isolated from the JVM that runs Gatling, configured with additional options like `"-XX:StartFlightRecording=filename=recording.jfr,...`
-After the simulations, you can open `recording.jfr` in Java Mission Control to analyze performance metrics like heap and CPU usage.
+This is useful when profiling, as `perfTests/runMain` will be a forked JVM isolated from the JVM that runs Gatling.
+
+## Profiling 
+
+To atach the profiler to a running server, it is recommended to use [async-profiler](https://github.com/async-profiler/async-profiler).
+Start the profiler by calling:
+```
+asprof -e cpu,alloc,lock -f profile.jfr <PID>
+```
+
+After the simulations, you can open `recording.jfr` in IntelliJ IDEA or Java Mission Control to analyze performance metrics like heap and CPU usage.
+It's also useful to build CPU flamegraphs with the [async-profiler converter](https://github.com/async-profiler/async-profiler?tab=readme-ov-file#download):
+```
+java -cp ./converter.jar jfr2flame ./profile.jfr flamegraph.html
+```
+
+After opening the flamegraph in your browser, use the spyglass icon to search for regular expressions and find the total % of registered samples matching the query. Searching for `tapir` will show you what's the Tapir's total share of the load. This can be a useful metric to compare before and after implementing performance improvements.
+
+Note that profiling noticeably affects performance, so it's recommended to measure throughput/latency without the profiler attached.
 
 ## Examples