12 performance testing myths that lead to production failures

Most production performance failures don't start with a bad tool. They start with a bad assumption. Load testing can wait until the release candidate. Autoscaling will absorb the spike. The average response time is green, so users must be fine.

Then real traffic arrives. Ticketmaster's Eras Tour presale in 2022 saw 3.5 billion system requests, 4x its previous peak, and about 15% of interactions hit errors. Amazon's Prime Day 2018 opened with roughly an hour of error pages. Aircall, a cloud phone platform, signed a large client with 1,000 simultaneous users and crashed under the load. It lost deals it had already won.

These myths aren't limited to small teams or legacy shops. They show up in organizations with mature engineering practices, often because each one contains a grain of truth that used to be the whole truth. Here are the 12 most common, and what to do instead.

Myth 1: You need a specialized expert to run performance tests

This myth was true for a long time. Legacy tools meant GUI-driven scripts, proprietary formats and a central performance team that every other team queued behind. At Intuit, that model produced late feedback, regressions reaching production and a waiting line for every team that needed a test.

Intuit rebuilt load testing as a platform capability instead. Performance test scaffolding now ships with every new service repository. Gatling usage grew from fewer than 100 developers in 2018 to more than 3,000, with 80,000+ load tests run each year.

"If you wanted to drive hundreds of thousands of transactions per second, it's not a hard thing to achieve in Gatling," says Chaitanya Bhatt, Principal Engineer at Intuit. "You don't have to tune it. You don't have to call in an expert. Any developer getting started would be able to drive that kind of load with ease."

How test-as-code lowers the barrier

Test-as-code means writing load tests as ordinary source code, in the languages and workflows your team already uses. A simulation is a program with three parts: a protocol definition (base URL, headers), a scenario (the user journey) and a setup that wires an injection profile to assertions.

• Familiar languages: Java, JavaScript, TypeScript, Kotlin or Scala, with the same concepts across all five
• Version control: Tests live next to application code in Git and get reviewed in pull requests
• IDE support: Autocomplete and type-checking in IntelliJ, VS Code, Cursor, Eclipse or Windsurf, with no special software to install

Language fit matters more than it sounds. EPI Company, which builds the Wero payment scheme, writes its tests in Kotlin because that's its backend language. "That really lowers the barrier for developers to contribute," says Ulrich Winter, Team Lead Infrastructure & Technology. Tikamoon rewrote its simulations from Scala to JavaScript for the same reason: so the whole team, not just the original author, could review and challenge them.

The ramp-up is short. InPost reports onboarding times of about 5 minutes for a no-code URL check and 30-60 minutes for a first test-as-code simulation, using a shared README and standards.

AI-assisted test creation for non-specialists

AI assistants shorten the path further. Gatling's AI Assistant for VS Code, Cursor, Windsurf and Antigravity includes a 7-step guided wizard that goes from a simulation name to a complete, runnable test with a data feeder. It can also explain or refine existing test code. The assistant talks directly to your own AI provider (OpenAI, Anthropic or Azure OpenAI), and your code and API keys never reach Gatling's servers.

AI-generated tests should still be reviewed and run like any other code before anyone trusts them. Specialists still matter too, especially for workload modeling and result interpretation. The difference is that they now coach and design instead of acting as a gate.

Myth 2: Performance testing is too expensive to justify

This belief dates from per-virtual-user licensing, dedicated test labs and multi-week test cycles. The economics have changed. The useful question now is what skipping it costs.

The real cost of skipping load tests

Failures under load land in budgets that no testing plan accounts for:

• Unplanned downtime: War rooms, hotfixes and rollbacks cost more than a planned test
• Customer churn: Users who hit a slow checkout or a failed login often don't come back
• Lost deals: Aircall couldn't guarantee load capacity to prospects, and lost signatures because of it
• Engineering time: Debugging production incidents pulls teams off roadmap work for days

The flip side is just as concrete. After building a load testing practice, Aircall could guarantee handleable load precisely, and it used that to sign large enterprise clients.

Testing also cuts infrastructure costs, not just incident costs. JioStar uses load tests to avoid over-scaling for live cricket, where concurrent users can jump by 7-8 million within 90 seconds. TRAY used them to validate its autoscaling strategy and avoid unnecessary spend. Sophos uses them to find services that consume more cloud resources than they should.

Open-source tools that scale to enterprise

Open-source frameworks remove the licensing barrier. Gatling's Community Edition is free under the Apache 2.0 license, and more than 300,000 organizations use it.

Engine efficiency matters for cost too. Thread-per-user tools need a thread, and its memory, for every virtual user, so large tests need large fleets of machines. Gatling's asynchronous, Netty-based engine sustains up to 60,000 concurrent virtual users or 300,000 requests per second on a single load generator, depending on protocol complexity. That's 10-20x more resource-efficient than thread-per-user tools at equivalent load.

When a team outgrows local runs, an enterprise platform adds managed load generators, shared dashboards and run history. InPost's SRE team describes the trade plainly: "With autoscaled generators we stopped maintaining boxes. We likely saved FTEs. Now we pay a subscription instead of paying with people's time."

One honest caveat: realistic end-to-end testing takes effort. Workload modeling, test data and environment setup need skill and time. Spend that effort where risk is concentrated. Don't skip it.

Myth 3: You must replicate production to test performance

Production-sized test environments are expensive, and many teams treat the lack of one as a reason to postpone testing. Waiting for a perfect mirror means testing nothing while you wait.

The opposite mistake is just as risky: testing on a quarter-size environment and multiplying by four. Performance is non-linear. Response time climbs sharply as utilization approaches saturation, and contention, cache hit ratios and database query plans all change with scale. A 10-million-row table doesn't behave like a 10-thousand-row one.

The practical answer is to match environment fidelity to the question you're asking.

Cloud-based load generation without full environments

You don't need to own hardware to generate realistic traffic. Gatling Enterprise Edition runs managed load generators in 10+ regions across Europe, the US, Asia Pacific and South America. You can weight traffic by location, for example 70% US and 30% Europe, to match where your users actually are.

For systems that aren't reachable from the internet, Private Locations run load generators inside your own network. A control plane in your infrastructure polls Gatling's API outbound, so there are no inbound connections and no firewall holes. Dedicated static IPs cover the middle case of a public endpoint behind an allowlist. AidenAI used one to test a Singapore client's environment from a region-specific load generator without loosening the client's firewall rules.

When partial environment testing delivers accurate results

Partial environments answer some questions well:

• Regressions: Is this build slower than the last one on the same environment? Relative comparisons stay valid even when absolute numbers don't transfer.
• Component limits: How much load can one service instance handle before latency degrades?
• Critical paths: Does login or checkout hold up at expected concurrency?

Several customers show what makes those results trustworthy. Nickel runs every test in a single pre-production environment that mirrors production. Tikamoon tests in a pre-production environment close to production and freezes its main scenario in Git so runs stay comparable. HMH's Staff Performance Engineer, Muzzamil Shaikh, recommends testing against a populated database, not an empty one, and keeping a dedicated performance environment so scripts don't fail on unrelated functional bugs.

When no environment is close enough, some teams test in production, carefully. Canal+ found its environments weren't isomorphic to production, so it ran progressive production tests that raised load in each session based on the previous one. It then served a major football broadcast to millions of viewers with zero incidents.

Whatever you choose, record how the environment differs from production and don't draw conclusions those differences rule out.

Myth 4: Only large enterprises need performance testing

Failure depends on load relative to capacity, not absolute scale. It also depends on the shape of the load. A small app, or a single service inside a large company, can fail at traffic levels that sound trivial.

Korem, a geospatial software company with about 100 employees, found its microservices crashing at around 50 concurrent users. The cause was Tomcat's default thread limits. After tuning thread counts, database connection pools and instance sizing, the services ran without crashes at much higher loads.

Why startups and small apps face scalability risks

Spikes follow events, not averages:

• Press coverage or a viral post: One Hacker News thread can multiply traffic in minutes
• Launches and promotions: Chipotle's free-guacamole promotion in 2018 overloaded its ordering app and forced it to extend the offer
• Deadlines and sale openings: Tape à l'œil customers fill carts ahead of big sales, then check out together at 8 a.m.
• A single large customer: One enterprise deal can double your concurrency overnight

Small teams don't need a performance department to handle this. ExploreAI had one software engineer and no prior experience with a formal load testing tool. It cut response times from 3,000-5,000 ms to 200-300 ms and now handles 10x its original traffic. TRAY, with six developers, brought its place-order flow from 18-20 seconds down to 2 seconds at a 20,000-user concurrency target.

Right-sizing performance tests for any team

Performance testing scales down as easily as it scales up:

• Start small: Test the one or two journeys closest to revenue first, such as checkout for e-commerce or login-to-dashboard for SaaS
• Grow gradually: Add scenarios as your application and traffic mature
• Automate early: Even one automated test in CI catches regressions before users do

A first meaningful test for one journey typically takes less than an hour.

Myth 5: Performance testing belongs at the end of the project

This habit comes from waterfall plans, where performance was a phase after functional stabilization, and from early tools that needed a complete system to run at all. The constraint disappeared. The habit stayed.

The problems that matter most are architectural: chatty service calls, synchronous dependencies, missing indexes, lock contention, excessive fan-out. Finding them at the end means redesigning under deadline pressure, or shipping known problems. A major Asian airline saw exactly that. Late-stage testing made design changes hard to implement and caused delays and unnecessary expense, until it moved performance testing into developers' CI pipelines.

Shift-left performance testing in modern workflows

Shift-left testing means moving performance checks earlier, while the code that caused a problem is still fresh and the fix is still small. It doesn't mean running full-scale tests on every commit. It means layering:

• Short component and API tests with pass/fail thresholds on every pull request
• Realistic load tests on integrated environments on a regular cadence
• Stress, spike and soak tests before major releases
• Production telemetry feeding real traffic patterns back into test models

The airline started with component-level tests in Jenkins so poor-performing pages never reached customers. Its home page now handles peaks of more than 5 million daily visits.

One caveat: shift-left complements realistic end-to-end testing, it doesn't replace it. Component tests catch regressions cheaply but can't reproduce system-level behavior like autoscaling lag or cross-service contention.

Embedding load tests in CI/CD pipelines

Gatling provides build plugins for Maven, Gradle and sbt, and CI integrations for GitHub Actions, GitLab CI, Jenkins, Azure DevOps, Bamboo and TeamCity. The pattern is the same everywhere: trigger a simulation, wait for it to finish, fail the pipeline if assertions fail. A deploy that regresses p95 latency by 15% fails its quality gate the same way a broken unit test does.

A few details make this practical at scale:

• Configuration as code: Test configuration lives in a versioned file in the repo, reviewed in pull requests and deployed with one command
• Scoped API tokens: CI gets a token that can only start tests, not reconfigure them
• Auto-cancellation: If a pipeline is canceled, the load test stops too, so nobody pays for an abandoned run

Myth 6: Performance testing only measures speed

"How fast is it?" is the first question most teams ask. It's also the least complete one.

Scalability, stability, and resource efficiency metrics

A useful performance picture covers several dimensions:

• Scalability: Does throughput grow as you add resources, and where does it stop growing?
• Stability: Does performance hold over hours or days, or degrade slowly?
• Resource efficiency: How much CPU, memory, bandwidth and money does each successful transaction consume?
• Failure behavior: When load exceeds capacity, does the system shed load cleanly or collapse?

Each dimension needs a different test shape. Stability is the one most teams skip because it takes time. InPost ran a 5-day continuous simulation of its full parcel journey, from a Saturday purchase to locker pickup days later. It exposed bottlenecks that short runs never showed. "The five-day test revealed bottlenecks from code to cables," says SRE Mateusz Piasta. The team tuned caches and database indexing and replaced a physical storage cable. InPost then scaled from 1.6 million to more than 10 million parcels a day. (Gatling Enterprise Edition runs can last up to seven days, which covers most soak scenarios.)

Purse and Interdiscount run long-duration tests for the same reason: memory pressure, connection pool exhaustion and cache behavior only surface over time.

What response time alone fails to reveal

Latency distributions are long-tailed and often multi-modal: cache hits and misses, garbage collection pauses, retries. Gatling's own documentation warns that mean and standard deviation are unreliable for load testing because they only describe symmetric, single-peaked distributions well. Two very different distributions can share the same mean.

Distributed systems make the tail worse. In "The Tail at Scale," Google's Jeffrey Dean and Luiz André Barroso showed that if a request fans out to 100 servers that each respond slowly 1% of the time, 63% of user requests will be slow.

A healthy-looking average can hide:

• Tail latency: p95, p99 and p99.9 describe the requests users complain about
• Memory leaks: Heap usage that climbs until the process restarts
• Connection pool exhaustion: Requests queuing behind a fixed pool while CPU looks fine
• Hidden failures: Fast errors pull the average down, not up

Define objectives on percentiles at a stated throughput, for example p95 under 300 ms and p99 under 1 s at 500 requests per second, with errors under 0.1%. Then translate them into business terms. "We held 1,200 requests per second, or 4.3 million transactions an hour, and our Black Friday peak is 2 million" is a sentence a product leader can act on.

Myth 7: Test scripts never match real user behavior

This skepticism is partly earned. A test that hammers one endpoint with identical requests measures something that never happens in production. The fix is better modeling, not abandoning synthetic tests.

Browser recording and HAR import for realistic journeys

Recording tools start you from real behavior instead of guesswork. Gatling Studio, a desktop app for macOS, Windows and Linux, records a session in an embedded Chromium browser, filters out static resources, groups requests, adds pauses based on real timing and exports a runnable Java project. HAR files can be converted too, and JavaScript projects can import Postman collections.

Journey-level modeling catches what request-level modeling misses. HMH compared an API-based script with a UI-based simulation of the same flow. The UI simulation revealed a service being called far more often than the API test's manually calculated rates assumed.

Parameterization and dynamic data techniques

A raw recording replays one user's session: the same tokens, the same product IDs, the same search terms. Replayed as-is, every virtual user hits the same cached data and the test measures a best case. Turning a recording into a realistic test takes four things:

• Correlation: Extract dynamic values like session IDs, CSRF tokens and order IDs from responses and reinject them
• Data feeders: Supply varied users and products from production-shaped data, sharded across load generators so they don't replay identical data
• Traffic mix: Weight scenarios to match analytics. Tikamoon weights product pages, listing pages and the homepage to mirror production traffic, and rechecks that mix against analytics over time.
• Arrival modeling: Control how fast new users arrive, not just how many exist

The last point is where many tests go wrong. Real public traffic is an open system: new users keep arriving whether or not your server is slow. A closed model runs a fixed pool of virtual users, and each one waits for a response before sending the next request. When the system slows down, a closed-model test automatically sends less traffic, which hides the exact problem you're looking for.

The difference is easier to see side by side.

Open vs closed workload models Load testing • Workload models

Dimension	Open model	Closed model
What you control	Arrival rate (users per second)	Number of concurrent users
When the system slows down	New users keep arriving and queues build	Users wait, so less traffic is sent
What it can hide	Nothing extra; it reproduces the queue	Overload, queuing and tail latency
Matches	Websites, public APIs, most internet traffic	Call centers, batch workers, fixed client pools

Gatling's documentation calls using a closed model for an open system one of the most common load testing mistakes. It's also why p95 numbers from a team testing closed and a team testing open aren't comparable.

// Open model: users arrive at a rate, whatever the response time scn.injectOpen(constantUsersPerSec(50).during(1800)); // Closed model: a fixed number of users, each waiting for its response scn.injectClosed(constantConcurrentUsers(500).during(1800));

Closed models are right for genuinely closed systems, like a fixed pool of call-center agents or batch workers. For internet-facing traffic, start open.

Myth 8: Performance testing guarantees zero production bugs

Performance testing validates specific scenarios under specific conditions. It isn't a certificate that nothing can go wrong.

What load testing actually validates

A well-designed load test answers concrete questions:

• Throughput capacity: The maximum requests per second before latency or errors degrade
• Latency thresholds: Response time percentiles at expected and peak load
• Breaking points: When the system fails, which component fails first and whether it recovers

Some bugs only exist under load, and a load test is the only place you'll find them before users do. Tape à l'œil's mobile app fired 8+ API calls at launch. It worked fine in QA, but at scale it caused memory spikes. The team consolidated those calls into one. Ticketmaster's presale failures included passcode validation errors that made fans lose tickets already in their carts: a functional failure that only appeared under extreme load.

What a load test doesn't validate is anything you didn't model: traffic patterns you haven't seen, data volumes you didn't reproduce, dependencies you mocked out, or code merged after the run. Protocol-level load tools also don't render pages or execute JavaScript the way a browser does. Sonepar tracks Core Web Vitals under generated load separately for exactly that reason.

A single passing run is also one sample from a noisy process. Cache warm-up, garbage collection timing and cloud neighbor noise all introduce variance, so compare repeated runs against a baseline before declaring a result.

Complementary testing you still need

Performance testing is one layer of a quality strategy:

• Functional testing confirms features behave correctly
• Security testing finds vulnerabilities a load test isn't designed to catch
• Observability in production catches conditions no test anticipated, and feeds real traffic data back into better test models

Monitoring observes load you've already had. Load testing creates load you expect but haven't had yet. You need both.

Myth 9: Agile and DevOps teams cannot fit performance testing

When performance testing meant a weeks-long engagement with a separate team, it genuinely didn't fit a two-week sprint. With tests as code and automated execution, it fits the same way unit tests do.

Automated performance tests in sprint cycles

The teams that make this work treat testing as routine, not as an occasion. "The goal was to make testing a commodity, not an event where you have to mobilize people," says Nicolas Zangari, QA lead at Purse. Popken Fashion Group runs load tests daily in its CI/CD pipeline. LoginRadius made performance testing part of the sprint and cut production performance regressions by more than 80%.

Ownership matters as much as automation. At IMA Group, the explicit goal was "to make developers responsible for the scalability of their applications, using tools and languages they already work with." At Attentive, service owners test their own services, with no dedicated testing department.

Performance gates that block bad deployments

A performance gate turns results into an automatic pass or fail. You define assertions in the test code, and the build fails if the run misses them:

setUp(scn.injectOpen(rampUsersPerSec(10).to(200).during(600)))  .protocols(httpProtocol)  .assertions(    global().responseTime().percentile(95).lt(300),    global().failedRequests().percent().lt(1.0)  );

Because the thresholds live in Git, they're reviewed like any other change. That makes them a contract rather than an opinion added to a dashboard after the fact.

Tikamoon states its policy plainly: "No Gatling run, no production. It's not a suggestion. It's a rule." Nickel requires a clean performance record, with no degradation against the previous version, before any release passes its Change Advisory Board.

Gatling Enterprise Edition adds a few controls on top. SLOs report the percentage of the run each target held, color-coded green, orange or red. Time windows exclude ramp-up and ramp-down from the verdict so warm-up noise doesn't skew it. Run Stop Criteria end a run automatically when CPU, error ratio or a latency percentile crosses a threshold. A test that has already failed doesn't need to keep running.

Myth 10: Performance testing is a one-time checkpoint

A green load test before launch feels like proof. But every code change, dependency upgrade, configuration update and data-volume increase can invalidate it. Regressions creep in commit by commit. A 3% slowdown per release is invisible in any single test and compounds over a year.

Why continuous testing catches regressions

Continuous testing turns performance into a trend instead of a snapshot. When the same tests run regularly against a baseline, a gradual slowdown becomes visible early. The cause is easy to find because only a few changes landed since the last run. Trend views make this concrete: a 30% p95 regression can be traced to a single commit when runs are plotted side by side.

InPost caught an unexpected 2x jump in requests per second before production. The root cause was an unnecessary HTTP-to-HTTPS redirect in an internal path. Interdiscount compares run history before and after every change to confirm performance is equal or better. A global NGO that sells event tickets twice a year compares runs taken months apart to make sure software updates haven't degraded anything in between.

"Three years ago, releasing our core banking system meant days of stress," says Alexandre Baert, Pre-production Platform Manager at Nickel. "Today, deployments are stable and invisible to our users."

Scheduled and triggered test automation

Teams typically combine three ways of running tests:

• Triggered: On every merge, release candidate or deployment through CI/CD
• Scheduled: Recurring runs of longer load and soak tests on a fixed cadence
• On-demand: Before launches, campaigns and infrastructure changes

Attentive triggers automated "game day" tests every morning through the Gatling Enterprise API. It links the results to Datadog traces and shares them in a central readiness table.

To track whether a critical flow is getting healthier over time, Gatling Enterprise campaigns group related tests and score them daily. The score weights the most recent run at 50% and the two before it at 25% each. A test that passes once and fails twice doesn't look healthy just because its last run was green.

Myth 11: Performance testing only surfaces problems without fixes

This frustration is real. A traditional report shows that latency rose at minute 14 and leaves the team to work out why. A test is only as valuable as the speed at which it leads to a fix.

Turning raw metrics into actionable insights

Three capabilities shorten the path from result to fix:

• Live dashboards: See latency and errors during the run, not after it. "The live visualization is what we spend the most time on," says Purse's Nicolas Zangari. JioStar stops tests as soon as 5xx errors appear and starts fixing immediately.
• Run comparison: Overlay up to 5 runs across 11 metrics to see exactly what changed between builds
• Observability correlation: Connect test runs to traces, database metrics and infrastructure data. In one example, a 10% error rate in Gatling traced back through Dynatrace to a database throughput bottleneck.

Connection-level metrics often point straight at the cause. Gatling reports TCP connect, TLS handshake and DNS resolution percentiles alongside response times. Attentive credits these charts with spotting a TCP connection pooling issue. After fixing it and enabling shared gRPC channels, single-node throughput went from about 6,000 to 160,000 requests per second. In another test, Attentive's injectors generated 100,000 requests per second but only 40,000-45,000 reached the service. The gap traced to service mesh autoscaling and protection settings, which the team fixed to full parity. Korem found a load balancer port exhaustion problem the same way.

Automated root cause analysis

AI now handles much of the first-pass investigation:

• AI Run Summary explains what broke, what slowed down and what to fix first, broken down by area
• AI Trend Analysis reads the last 10 runs and returns a verdict: Stable, SomeIssues or Degrading
• AI Run Comparison explains the differences between 2-5 runs
• Failure Diagnosis and the Gatling MCP server identify why a run failed: a build error, an injection crash or a stop-criteria breach

Each AI report carries a confidence level and says when there isn't enough signal instead of forcing a verdict. The written output can go straight into Slack or a Jira ticket. That matters because the people who run tests are rarely the people who decide whether to ship.

Myth 12: AI will make performance testing obsolete

If AI can write code and read logs, the argument goes, performance problems will take care of themselves. In practice, AI changes how teams run performance tests. It hasn't removed the need for them, and AI-powered features have added new performance risks.

Where AI accelerates performance testing

AI is already removing manual work around testing:

• Script generation: Create tests from natural-language descriptions of user journeys
• Result interpretation: Summarize runs, trends and regressions automatically
• Legacy migration: Convert JMeter or LoadRunner scripts into modern test-as-code projects

Gatling's JMeter and LoadRunner converters map an existing script to a working simulation in the language and build tool you choose, and flag anything that needs manual review. For teams with years of scripts, that changes migration from a rewrite project into a proof of concept.

Why human judgment remains essential

AI can generate a test and summarize a result. It can't decide what your business needs. Humans still:

• Define good performance: Which percentiles, at what load, for which journeys
• Design realistic scenarios: Which events, spikes and failure modes are worth testing
• Make architectural decisions: Whether to add caching, redesign a service or accept a trade-off

AI features also make testing harder, not easier. LLM-backed endpoints look like ordinary HTTP APIs, but almost everything underneath behaves differently.

Classic API vs LLM-backed endpoint AI • Load testing

Dimension	Classic API	LLM-backed endpoint
Work per request	Roughly constant	Varies with prompt and output length
Latency metric	Response time percentiles	Time to first token plus total generation time
Throughput unit	Requests per second	Tokens per second
Capacity limit	CPU, connections, database	Bounded GPU slots and provider tokens-per-minute limits
Cost per request	Fixed and small	Variable, driven by output tokens
Behavior at overload	Degrades gradually	Queues, then latency cascades and retry storms

So a load test that sends one short, fixed prompt and checks it against a classic "p95 under 500 ms" target is the right system measured with the wrong ruler. Testing AI features well means using a corpus of real prompts and modeling client timeouts and retries, so a retry storm on a saturated inference pool shows up in the test instead of in production. The pass criterion shifts too: at capacity, the system should shed load cleanly with fast 503s or a cost circuit breaker, not melt into a latency cascade.

Build a performance testing practice that prevents failures

Every myth above shares a root cause: treating performance as an event instead of an engineering practice. The teams that avoid production failures don't necessarily run the biggest tests. They run the right tests, often, and act on what they find.

Start with three steps:

1. Pick your most critical user flow and write a test for it in the language your team already uses.
2. Add it to CI with percentile-based assertions, so a regression fails the build automatically.
3. Expand deliberately: Add spike tests before campaigns, soak tests before major releases, and stress tests to find your real limits.

Gatling lets teams write load tests as code in Java, JavaScript, TypeScript, Kotlin or Scala, gate every deployment on SLOs, and track trends, comparisons and AI-assisted analysis across every run. Start with the free, open-source Community Edition, or explore Gatling Enterprise Edition when your team is ready to make performance testing continuous.