With the 2026 FIFA World Cup expected to draw hundreds of millions of viewers across broadcast and streaming platforms, even a few minutes of downtime could have serious consequences.
In a worst-case scenario, a live stream failure during a flagship event triggers a domino effect: social media backlash, mass subscriber churn, and significant revenue loss. But when successful, live events can have a huge impact on business growth. For example, Ampere Analysis estimated that Netflix saw around 1.5 million US sign-ups around the Paul vs. Tyson boxing match in November 2024 — the largest spike in US sign-ups the firm has recorded since it began tracking in 2018.
For the engineering and operations teams responsible for delivering large-scale live streams, the biggest risk is usually not the video delivery itself, but everything surrounding it. Many incidents aren’t caused by a CDN running out of bandwidth, but by dependencies around the stream.
This blog lists some of the most common mistakes we see when teams prepare for large live streaming events — and how to avoid them.
1. Assuming If It Worked Before, It Will Work at Event Scale
One of the most common risks we see is caused by assumptions: the belief that just because something worked before, it will work at large-event scale. Live events introduce too many variables to rely on assumptions. Below are a few examples of mistakes we’ve seen customers make. Details have been anonymized to protect customer confidentiality.
Introducing a new workflow shortly before a major event. In one case, a broadcaster rolled out a new production workflow shortly before their largest event of the year. It worked at a smaller scale, but under peak concurrency, it exposed unexpected behavior.
Assuming traffic patterns will match historical demand. In one case, a customer hadn’t previously observed significant demand from Brazil before a major event, so historical patterns didn’t predict the spike.
Focusing on video delivery while overlooking upstream dependencies. In one high-profile series launch, users couldn’t validate entitlements/authorization, so they never reached the video regardless of CDN readiness. The early issues were tied to API services responsible for entitlement and authorization.
Treating digital ad insertion as an add-on rather than a critical dependency. In one major event, a DAI solution wasn’t included in large-scale load testing despite being essential to monetization. Under peak conditions, it became a risk.
Assuming cloud infrastructure will automatically absorb traffic spikes. In one case, testing revealed that certain load balancers weren’t highly reactive to sudden surges and required pre-warming to perform optimally.
Live streaming is a chain, and the weakest link defines the experience. Common single points of failure include DNS, global load balancing/steering in multi-CDN setups, mid-tier shielding layers, and origin regions. Separately, entitlement/authorization APIs are a critical dependency that can block playback.
2. Planning Capacity Around Total Bandwidth Instead of Traffic Velocity
Peak bandwidth numbers can look impressive on dashboards, but they’re one of the least reliable predictors of failure. We regularly see traffic ramp from zero to 5 Tbps in a matter of minutes, with continued growth beyond that. But those ramps rarely distribute evenly. Streams don't usually degrade globally; they degrade regionally.
When one of our big broadcasting customers launched a sports streaming service, the first game saw an unexpectedly massive surge in Chile. Forecasts hadn’t predicted it. Much of the traffic turned out to be pirated streams. The global picture looked healthy, but regionally, the system had to absorb sudden concentrated demand. Traffic engineering adjustments were required in real time, and mitigation strategies were put in place for subsequent events.
We’ve also seen cases where multi-CDN steering shifted traffic disproportionately to one provider, not because the event was larger than forecast, but because routing behaved differently under load. From a global bandwidth perspective, everything looked fine. From a regional and provider-specific perspective, pressure was building fast.
What breaks systems isn’t always sustained throughput. It’s velocity. A sudden surge of manifest requests or entitlement checks can overwhelm origins before the first video frame plays.
Planning around “average global traffic” is planning for the wrong failure mode. Capacity models have to account for sudden, localized peaks and for the reality that traffic doesn’t always behave politely.
3. Testing for Traffic Instead of Testing Real Viewer Behavior
Generating large request-per-second numbers is straightforward. However, simulating large numbers of real viewers is not.
If you want to test 10,000 viewers watching a stream, that doesn’t mean firing 10,000 requests as quickly as possible. It means simulating 10,000 independent players, each maintaining state, fetching manifests at the correct interval, requesting segments sequentially, maintaining cookies and device characteristics.
We’ve seen testing environments rely on pre-encoded content at relatively low sustained RPS, effectively simulating a VOD-style workflow. Live streaming is different. Segments are generated dynamically by encoders, introducing latency and compute characteristics that shouldn't be overlooked.
The goal of testing shouldn’t be simply to generate traffic, but to simulate real viewers. That means using player-based load testing, testing with encoder-generated streams rather than static content, and validating the full workflow from entitlement APIs to ad insertion and origin delivery.
Many tests pass because they measure throughput and error rates. These tests struggle live because they don't simulate behavior. The purpose of testing shouldn’t be to offer reassurance, but to find where the system bends before the internet does.
4. Preparing for Success Instead of Rehearsing Failure
Architecture reviews and load testing help surface potential risks. But in the weeks before an event, the real question isn’t whether risks exist, but whether the team can respond when those risks become real.
The most useful tabletop exercises assume things will go wrong, not that everything will work successfully. What happens if an origin becomes slow but doesn’t error? If entitlement services start returning elevated 403s? If multiple CDNs approach regional capacity and you need to remove higher bitrates from manifests?
Tabletop exercises often reveal that the biggest gaps aren’t architectural; they’re organizational. Who declares an incident? Who communicates externally? How quickly can a redundant system be brought online? A redundant component that relies on a long runbook no one has practiced is, in practice, a single point of failure.
We’ve seen the only true full failure occur not in a global sports event, but in a corporate town hall. The primary encoder failed and a secondary wasn’t ready to take over quickly. Within 30 minutes, the event was effectively over. It wasn’t a bandwidth issue, but a single point of failure in the encoding chain.
5. Relying on Heroics Instead of Operational Structure
Live events demand structure. Everyone involved must understand their role before the event begins.
There needs to be a clear incident lead acting as quarterback — triaging issues, assigning domain experts, maintaining communication cadence, and marking resolution. When multiple issues surface simultaneously, triaging them in real time and assigning the right resources is critical.
One of the most common mistakes in war rooms is losing focus. When things appear stable, chatter increases. Sports events in particular are long and emotionally engaging. Pre- and post-coverage can stretch hours. It’s easy to drift into spectator mode. That’s often when edge cases surface.
In some of the strongest events we’ve supported, customers placed all critical vendors in the same room. Physical proximity accelerated coordination and reduced silos. When vendors are isolated in separate virtual channels, collaboration naturally slows.
Live events don’t reward heroics. They reward disciplined coordination.
6. Waiting for Problems to Appear in Dashboards
Redundancy doesn’t alone guarantee stability. What consistently changes outcomes is real-time visibility and the ability to act quickly.
We routinely see signals at the network or ASN level — for example, a specific ISP in a specific city slowing down — before those patterns appear in broader dashboards. In some cases, anomalies are visible 30 to 90 seconds before they’re widely recognised elsewhere in the ecosystem. That early window can determine whether an issue is contained or amplified.
Fastly’s Edge Cloud Platform provides this level of real-time visibility across networks and regions, allowing operators to detect and respond to emerging issues quickly.
Equally important is agility. During live events, adjustments may need to happen in minutes: blocking traffic from a region driving abusive load, rewriting an unexpected URL pattern causing errors, adjusting origin timeouts, or shifting traffic deliberately. Fastly’s platform allows these kinds of configuration changes to be deployed globally in seconds, giving operators the flexibility to react during a live event.
At scale, speed of insight and speed of change matter as much as raw capacity.
Cheat Sheet for Digital Live Events
Weeks Ahead: Operational Readiness
Tabletop exercises: Walk through realistic failure scenarios (origin outages, slow responses, entitlement errors, or unexpected traffic spikes) to test both the system and the team’s response.
Clear escalation paths: Establish decision-making authority and communication protocols. When multiple issues occur simultaneously, ambiguity can delay resolution.
Redundancy readiness: Validate how quickly backup systems can be activated and whether they are hot or cold failovers.
Event Day: Execution
Incident command structure: A strong incident commander “quarterbacks” the event — triaging issues, assigning domain experts, and maintaining communication cadence.
War room discipline: Live events require a focused operational posture. One incident commander, one source of truth, and no side-channel troubleshooting.
See something, say something: If anything looks even slightly off, flag it immediately. Early actions often prevent larger incidents.
Stay vigilant during stable periods: When events appear stable, teams can drift into spectator mode. That’s often when edge cases emerge.
Visibility and Monitoring
Fewer, clearer dashboards: Monitoring views must be instantly understandable. If a dashboard requires interpretation, it’s not suitable for live event operations.
Key metrics: Focus on traffic levels (globally and regionally), latency (edge and origin), and error rates.
Don’t rely on total bandwidth: Aggregate bandwidth can be misleading; capacity pressure usually emerges regionally.
Simple Incident Severity Model
Many teams use a stoplight model during events:
Green: Normal operation.
Yellow: Deviation detected; monitor closely and look for correlated issues.
Red: User-impacting issue; declare an incident and initiate coordinated response.
Final Thought
Live streaming at scale is not primarily a bandwidth problem. It is a systems problem.
It requires acknowledging that dependencies beyond video are critical, that traffic accelerates unevenly, that realistic testing is expensive and uncomfortable, and that disciplined coordination matters as much as architecture.
When it works, it’s simple: the audience sees a stream, the dashboards stay controlled, and the war room remains quiet. Not because things didn’t go wrong, but because the system was designed, tested, and operated to absorb mistakes.
At Fastly, we’ve supported many large-scale live events and have seen firsthand how preparation determines success. Learn more about how Fastly supports large-scale live streaming.