GATLING  VS  BLAZEMETER

BlazeMeter is a Swiss Army knife. Gatling is a scalpel

Breadth is BlazeMeter's pitch: JMeter, Selenium, Locust, even Gatling scripts, one dashboard over all of it. Gatling Enterprise only runs Gatling, so its AI analysis, SLOs, and scoring are built around one engine's data, not bolted on top of five.

From there, it's what happens after the run: how you script it, own it, and read what it tells you.

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BlazeMeter is the generalist: any engine, one dashboard. Gatling is is the specialist: one engine, built end to end.

If your org already has scripts spread across JMeter, Selenium, and Locust, or a Gatling script or two running through Taurus, BlazeMeter's one-dashboard cloud is a genuinely reasonable choice. But running Gatling through a generic layer built for five engines isn't the same as running it natively, with AI, SLOs, and scoring tuned to it specifically. That's the gap this comparison is about.

BlazeMeter

Cloud-hosted JMeter, Selenium, Locust, and Gatling via Taurus. One dashboard, any engine.

Gatling Community + Gatling Enterprise

Open-source core, enterprise platform, built only for performance.

Runs whatever you've already scripted: JMeter and Selenium natively, Locust and Gatling via Taurus.

Protocol comes coverage from JMeter, Selenium and Locust's. Broad, but bolted together from several engines.

Thread-per-virtual-user engine under the hood. Scale comes from adding more cloud machines, not from doing more per machine.

GUI-first authoring, a Chrome recorder, and no-code test builders. Approachable without a coding background.

Test-as-code in Java, JavaScript, TypeScript, Kotlin, and Scala. The same repo, build, and review flow as your app.

60,000 virtual users or 300,000 requests/second per load generator. 5M+ concurrent users on a 20-generator fleet.

HTTP/S, WebSockets, SSE, gRPC, MQTT, JMS natively, plus Kafka, AMQP, and JDBC databases via plugins.

After the run: AI analysis, SLO compliance, trends, comparisons, and a campaign score that moves over time.

An engine that doesn't waste a single CPU cycle

A single machine caps out around 64,000 concurrent sockets per target. Gatling’s engine is built to use nearly all of them.

60,000 VUs300,000 req/s per generator

Netty-based non-blocking I/O saturates the socket ceiling instead of stalling on threads.

0 threadsper virtual user

Fully async virtual users use a fraction of the CPU and memory of thread-per-user tools. Fewer machines, same traffic.

Full TLSthroughput under bursts

BoringSSL and persistent connection reuse, no TIME_WAIT stalls, no handshake collapse on HTTPS-heavy traffic.

5M+ VUson 20 load generators

Linear fleet scaling, Gatling-managed on AWS, or self-managed on AWS, Azure, GCP, Kubernetes, OpenShift, and on-prem.

BEYOND THE RUN

Stop reading charts.
‍Start reading results.

Gatling Enterprise Edition is built for everything that happens after the loadhits: understanding what broke, whether it's getting worse, and proving to the business that reliability is improving.

AI analysis

One click after every run: what broke, what slowed down, what to fix first. Across runs, the AI tells you whether a regression is new or chronic, with a confidence level, not a shrug.

Service Level Objectives

Pick a percentile (50th to 99.9999th) or an error ratio, set the target, stack asmany as you need. Every run comes back compliant or not.

Campaigns and scores

A composite score per test and per campaign for multiple tests (Methodology, Objectives, Confidence) tracked over time with deltas.

Injection profile builder

Ramps, plateaus, staircases built visually, previewed live, without touching your scenario code. Change the load shape, not the script.

MCP server and AI skills

Your coding agent gets live access to your Gatling Enterprise account: itconfigures, deploys, and starts tests with the right settings, from your IDE.

Migration agents

AI converts your JMeter and LoadRunner scripts into Gatling tests. Years of test assets move with you, nobody rewrites them by hand.

Gatling vs Blazemeter
Feature-by-feature comparison

Gatling

Blazemeter

Edge

Architecture & Protocols

Concurrency & capacity

Native protocol support

Extended protocol support

Netty engine; up to 60,000 VUs or 300,000 req/s per generator
HTTP, HTTP/2, WebSockets, Server-Sent Events, gRPC, JMS, MQTT
Kafka, AMQP, JDBC, FTP/SFTP via plugins
Thread-per-VU JMeter engine; scale reached by adding cloud machines
HTTP(S), SOAP/REST, WebSocket, JDBC, JMS, MQTT via JMeter core
Very broad, inherited from JMeter, Selenium, and Locust
Gatling
Tied
Blazemeter

Developer experience

Scripting languages

Build tool integration

No-code / low-code option

Injection profile builder

CLI-driven workflow

Runs Gatling scripts

Java, JavaScript, TypeScript, Scala, Kotlin
Native Maven, Gradle, sbt plugins
Gatling Studio web recorder + no-code test builder (Enterprise)
Visual builder: ramps, plateaus, staircases, live timeline preview
CLI available; build-tool oriented
CLI available; build-tool oriented
JMeter GUI/XML, Groovy/BeanShell, Taurus YAML, Selenium
Taurus CLI, native CI plugins for Jenkins, GitLab, Azure DevOps
Chrome recorder, scriptless functional authoring, GUI test editor
Load shape defined in the JMeter thread group GUI or Taurus config
Taurus: config-driven CLI runner and automation layer
Taurus: config-driven CLI runner and automation layer
Gatling
Gatling
Blazemeter
Gatling
Tied
Tied

AI CAPABILITIES

AI-assisted test authoring

AI-powered results analysis

Agent-to-platform deployment

JMeter migration assistant

LoadRunner migration assistant

In-IDE AI Assistant generates, explains, and improves test;
MCP server + AI skills
AI Run Summary, Trend Analysis, Run Comparison: narrative verdicts
MCP + skills: validate, generate, and run tests via coding agent
AI-powered migration assistant
AI-powered migration assistant
AI-assisted script and test-data generation
AI log analysis surfaces errors across run output
No equivalent MCP-style agent integration published
Native. JMeter is already a first-class engine
AI-powered script conversion from LoadRunner into BlazeMeter
Gatling
Tied
Gatling
Gatling
Tied

Open Source Core

License


Metrics collection

Apache License 2.0 (permissive)
Full-resolution capture: every request and response recorded as an individual event
Closed-source SaaS; Taurus automation layer is Apache 2.0
Aggregate and granular metrics collected per engine, exportable
Gatling

Tied

METRICS, REPORTS & DASHBOARD

Metrics catalog breadth

Observability integrations

SLO management

Single-run scoring


Campaign-level scoring & multi-test tracking


Run trends over time


Multi-run comparison

25+ built-in metrics: network, TCP, TLS, DNS, GC, CPU/memory
Datadog, Dynatrace, New Relic, InfluxDB, OpenTelemetry
Visual no-code SLO builder: percentile or error ratio, stack multiple
Composite score per test (Methodology, Objectives, Confidence)

Campaign score aggregates multiple tests, tracked over time
Trends view over last 10 runs

Compare up to 5 runs, 11 metrics, one click from Run History
~18 built-in metrics: HTTP, VU, iteration, data transfer
Deep, long-standing APM integrations across the same major vendors
Pass/fail thresholds configured per test plan
No unified composite score across engines

Projects group tests, but carry no score or aggregated tracking
Historical trend analysis across runs and projects
Compare runs across engines and environments
Gatling
Tied
Gatling
Gatling
Tied
Blazemeter
Tied

Enterprise & Deployment

Self-hosted load generators

Managed cloud load generators

Shift-left / CI-native testing

Maintenance overhead

Private locations on AWS, Azure, GCP, Kubernetes, and on-premises
Gatling-managed AWS regions worldwide; optional dedicated IPs
Runs in the build pipeline via Maven, Gradle, or sbt
Test-as-code: versionable and refactorable across five languages
Private Location agents for on-prem or behind-firewall execution
Advertised across 50+ managed cloud locations, turnkey scale-out
Runs via Taurus or native plugins in most major CI systems
JMX/XML and GUI test plans are harder to diff and code-review
Tied
Tied
Gatling
Gatling

Products & licensing

Organization

Guarantees

Gatling Corp - performance testing is the entire product
‍
Enterprise support, SLAs, advisory services, feature co-development
Perforce, one product in a broad testing and DevOps portfolio
Enterprise support and SLAs across the full BlazeMeter suite
Gatling
Tied

Teams that switched
and what changed.

30M
concurrent users
JioStar
5M+ VUs
on 20 load generators
100,000 req/s
sustained in production
Bouygues Telecom
0 errors
at 100k RPS per service
Attentive
G2 Leader
Load Testing category

This year we rewrote our simulations in JavaScript so the whole team could really take ownership. It’s the most accessible language for our developers; and in e-commerce, we use it every day.

Loïc Chero
IT Leader, Tikamoon

Compared to other options, Gatling Enterprise Edition has helped us streamline performance testing in JS. It gave us visibility into key metrics we can now measure and improve, which helps us enhance our product for our customers.

Kundan Singh
Director of Product and Engineering, LoginRadius

Before Gatling, going live felt like crossing our fingers and hoping it held. Now we can simulate, measure, and move forward with clarity.

Nordine El Mojahid
Head of Digital IT, Tape à l'œil

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test with Gatling?

See how Gatling's engine, platform, AI features, and reporting tools can help you go beyond load testing.

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