Live sports, OTT subscriber scale, ad delivery, and broadcast modernization. Where streaming platforms break under load, and how to find out before your viewers do.
Live Session
Build, run, and interpret your first load test. A practical session: by the end, you'll know exactly what Enterprise gives you out of the box.
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30 minutes
Live + Q&A
Most broadcast and streaming organizations don't operate at Super Bowl scale. But every one of them has its version of a tentpole event: a match day, a season premiere, a live concert, a product launch stream. And in every case, the moment of peak demand is known in advance.
The traffic spike is foreseeable. The failure, if it happens, is preventable.
The industry is in structural transition. Linear television is moving to IP delivery. OTT platforms compete for attention measured in seconds of startup delay. Ad-supported models turn every failed ad insertion into a revenue event. And live sports, the last category driving appointment viewing, now runs on the same CDN infrastructure as everything else, at 10 to 100 times normal operating load.
In this environment, performance failures are viewer defection events. Research across 150 video services shows that when more than 2% of viewing sessions are subpar, new user churn can reach 50%, more than double the average OTT churn rate in the United States. A buffering ratio above 2% of viewing time is the boundary between a platform viewers trust and one they leave.
The core question is simple: are you managing performance risk, or accepting it?
Use case: live sports & tentpole events
Live events are the most demanding infrastructure problem in streaming. Traffic does not ramp gradually. It arrives in surges.
CDN providers sustain more than 12 million requests per second during peak live events. Capacity plans built on average traffic don't survive the first minute of kickoff.
Traffic surge during major live broadcast events
Traffic spike within seconds of a scoring play, as viewers join or replay content
Average cost per minute of IT downtime in 2024
One tentpole event. Six systems under simultaneous pressure. A 60-second window where failure becomes defection.
Authentication and session validation
Risk: Session timeouts under concurrent peak load.
Content delivery from the nearest point of presence
Risk: Cache miss rate spikes on new content; origin pulls flood the ingest layer.
Master playlist and segment delivery
Risk: At a 5% miss rate and 10M viewers, that's 500,000 simultaneous origin requests.
Live transcoding and packaging
Risk: Encoding latency adds end-to-end delay; segment generation falls behind under load.
Dynamic ad insertion at scale
Risk: Ad decision latency causes buffering; ad timeouts cause content gaps or viewer exits.
ABR ladder management per viewer
Risk: Bitrate degrades at scale; viewers drop to the lowest tier or abandon.
A CDN that passes capacity planning fails when the top 1% of channels, which carry more than 70% of total platform traffic, all spike at once. Startup failures at kickoff. Buffering during decisive moments. Ad insertion timeouts that turn a 30-second break into a viewer's exit point.
Use case: OTT platform reliability & subscriber scale
OTT platforms compete on experience, not just content. With 5.27 billion users worldwide, platform performance is a retention factor.
Startup delays past 2 seconds, buffering above 2% of viewing time, and bitrate drops are problems viewers notice even when they can't name them.
Global OTT users in 2024
Viewer abandonment per additional second of delay beyond 2 seconds
New user churn when more than 2% of sessions are subpar, over double the average OTT churn rate
One user opens the app. Six systems must respond correctly, quickly, and in sequence.
Login, token validation, entitlement check
Risk: Auth service degrades under a login surge on premiere or release day.
Homepage personalization, search, recommendations
Risk: Recommendation latency adds to perceived startup time before the user hits play.
License acquisition, DRM validation, manifest fetch
Risk: DRM timeouts cause a black screen; manifest delay is the first buffering event.
Segment delivery from edge cache
Risk: Cache misses on new content force origin pulls at scale on premiere day.
ABR algorithm selects a quality tier per session
Risk: Rebuffering and bitrate drops, the top causal drivers of quality of experience, rise under load.
Playback metrics, heartbeats, quality reporting
Risk: Telemetry backpressure at scale degrades real-time monitoring.
Your dashboards tell you when users are already having a bad experience. Without performance testing in your delivery pipeline, you find out after the damage is done.
Use case: ad delivery & monetization infrastructure
A failed ad insertion is not a user experience issue. It is a direct revenue loss.
Every 100 ms of ad decision server latency at 10 million concurrent viewers is a compounding financial exposure.
Streamed video ads that failed to play globally in Q2 2021, down from 47% in 2018
Annual revenue loss from live-streamed video latency over CDNs in 2021
Projected accumulated loss through 2026 under HTTP streaming conditions
One viewer reaches an ad break. The whole chain has under 200 milliseconds to respond.
Player signals the ad break to the ad manager
Risk: Simultaneous ad break signals across millions of sessions create a request spike.
Programmatic bid request and creative selection
Risk: Third-party latency cascades inward. It's your SLA, even when the failure is theirs.
VAST tag fetch and media asset retrieval
Risk: Creative CDN misses on new campaigns cause timeouts before the ad starts.
Server-side ad insertion into the video stream
Risk: SSAI latency lengthens the ad break gap; stitching errors show the viewer a black screen.
Ad event data flowing to measurement and attribution systems
Risk: Handoff points between systems become the weak points under load.
Impressions, quartile events, completion
Risk: Beacon failures invalidate impressions for billing: revenue recognized but not delivered.
Ad delivery sits at the intersection of real-time video delivery and programmatic advertising, and both have to work together in milliseconds. When they don't, the failure is invisible to the viewer, who sees a black screen or a content gap, but immediately visible in fill rate and revenue reporting.
Use case: legacy broadcast modernization
Traditional broadcasters are moving from linear delivery and on-premises infrastructure to IP-based, cloud-native architectures.
Every integration milestone is a point where untested behavior under load can quietly undermine the migration's business case.
Annual growth in digital viewership for major live sporting events
Driven by rising demand, legacy strain, and added complexity from 5G and software-defined networking
Legacy systems, cloud migration, and untested load. Silent performance risk at every integration point.
Broadcast ingest and media asset management
Risk: Stable under known conditions, brittle under the new concurrency patterns of IP delivery.
Encoding and packaging for multi-format delivery
Risk: What worked as scheduled batch processing becomes real-time demand.
APIs connecting legacy systems to modern delivery
Risk: Each new wrapper adds latency and a failure surface; integration points weaken under load.
Content origin for CDN delivery
Risk: Capacity planning built for linear audiences fails under simultaneous sessions.
Simultaneous delivery across web, mobile, and CTV
Risk: Real peak load per platform doesn't match what the test team modeled.
Live events, content launches, seasonal peaks
Risk: Untested behavior surfaces exactly when the business needs the infrastructure to hold.
Your monitoring tells you when things break. Without performance testing in your migration process, you miss the window to find out first, while you can still do something about it.
Datadog, Dynatrace, New Relic, InfluxDB: your observability stack already defines what good looks like for latency, error rates, and throughput in production. Performance testing applies those same thresholds before production, where regressions are still cheap to fix.
The question isn't monitoring or testing. It's whether your load tests use the same success criteria your dashboards enforce.
Performance testing doesn't replace monitoring. It makes sure less slips through in the first place, moving from a periodic activity to a continuous discipline governed as part of the release process.
Across every broadcasting and streaming company Gatling works with, one constraint holds: load generators must reproduce the real concurrency patterns of live events, not average traffic.
Unlike e-commerce, where load ramps gradually, live broadcast concentrates millions of connection attempts into a window of seconds. Linear ramps can't reproduce that. Gatling's injection profiles simulate the instantaneous surge your infrastructure has to absorb, across the full streaming API surface: authentication flows, content delivery APIs, ad decision calls, playback reporting, and license acquisition, with JWT, OAuth, dynamic tokens, and chained requests across microservices.
Origin servers, internal ad systems, and legacy ingest pipelines can't be exposed to the internet. For these, load generators running inside your network perimeter aren't a preference, they're a requirement. Private locations deploy them in your VPC, Kubernetes cluster, or data center, without firewall exceptions or architectural compromises.
Continuous Performance Intelligence: tests in Git, automation in CI, and results tied to the same telemetry your SRE teams watch on match day.
Deploy load generators inside your VPC, Kubernetes cluster, or on-premises data center. Test traffic never leaves your network.
Define p95 and error rate targets. Every run returns a compliance score: the precise share of time your system held the line. Breach it and the pipeline stops.
SSO (OIDC/SAML), role-based access control, and audit logs that plug into the governance frameworks you already enforce.
Automated run summaries surface what went wrong without manual analysis. IDE integration and the MCP server bring load testing into the tools your engineers already use.
Trigger runs, enforce thresholds, and gate promotions from GitHub Actions, GitLab CI, Jenkins, Azure DevOps, or any pipeline.
Java, Kotlin, JavaScript, TypeScript, or Scala. Versioned, reviewed, and living in the same repository as your application.
HTTP, WebSocket, SSE, gRPC, JMS, and MQTT for APIs, streams, messaging, and microservices.
Overlay each run on your baselines to see whether a release improves, holds, or degrades under load.
Spin injector fleets up and down as IaC. Nodes appear in your VPC for the run and disappear after it.
Spread injection across multiple nodes and regions so no single machine becomes a CPU bottleneck on TLS-heavy workloads.
Monoliths, APIs, microservices, Kubernetes clusters, and globally distributed streaming platforms.
Concurrent virtual users per load generator
Requests per second per load generator
Concurrent virtual users with 20 load generators
Load generators running simultaneously
Automated tests running in parallel, 24/7
Progressive capacity, soak, and stress campaigns built in Java, run from private load generators provisioned with Terraform, and wired into GitHub Actions. Bottlenecks in caching and licensing APIs were found and fixed before match day.
Zero incidents during a major football broadcast serving millions of concurrent viewers.
Read the Canal+ storyA wicket or a star batsman walking out can add 7 to 8 million concurrent users within 90 seconds. JioStar reproduces those spikes at match milestones, including OTP login flows that depend on third-party SMS services.
30 million concurrent users supported, without over-scaling infrastructure.
Read the JioStar storyStreaming teams preparing for record-breaking peaks with Gatling
Take the full whitepaper with you, or see how streaming teams test the kickoff, the premiere, and the ad break before viewers do.
All four use cases, the kickoff surge example, and the Canal+ and JioStar stories.
PDF · free, no form Download the PDF downloadA guided walkthrough with a Gatling engineer, with time for your questions.
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