How Netflix Serves 325M+ Members Without Going Down

By Devang Pandya · Cloud Tech by Devang · Cloud • DevOps • AI — Made Simple

You open Netflix on a Friday night. You press Play. The video starts in a second or two, in good quality, with no buffering. Millions of other people around the world are doing the same thing at the same time.

Netflix crossed 325 million paid memberships in late 2025. That is a huge number of people who expect the app to "just work". So how does Netflix stay up?

There is no single magic tool. It is a set of simple ideas, used with a lot of discipline. In this post I will explain these ideas in simple English. At the end, I will show you how to use them in a system design interview. I have spent 14+ years in cloud, DevOps and SRE, and I see the same patterns in every system that scales well, big or small.

1. It started with an outage

In August 2008, Netflix had a major database corruption. For three days, it could not ship DVDs to its members.

For a DVD business, that was painful. For a streaming business, it would have been a disaster. If streaming had been down for three days, nobody could watch anything at all.

Netflix took one big lesson from this: a single, vertically scaled database is a single point of failure. "Vertically scaled" means you make one machine bigger and bigger. When that one machine breaks, everything breaks.

So Netflix decided to move to horizontally scalable, distributed systems in the cloud. "Horizontally scaled" means you add more machines instead of making one machine bigger. If one fails, the others keep working.

They chose AWS (Amazon Web Services). Most customer-facing systems were moved before 2015. In January 2016, after seven years of work, Netflix shut down the last data centre parts used by its streaming service.

The key point: Netflix did not just copy its old systems to the cloud ("lift and shift"). It rebuilt them. In their own words, they moved from a monolithic app to hundreds of microservices, and they changed their data model to use NoSQL databases.

Lesson for us: moving to the cloud does not make you reliable by itself. Changing the design does.

2. Two paths: the "brain" and the "video"

This is the most important idea in the whole post, so let's make it simple.

When you use Netflix, two very different kinds of work happen:

1. The brain (control plane). Logging in, choosing a profile, loading your home screen, showing recommendations, checking your plan and licence, and deciding which server should send you the video. This is lots of small requests. It runs on AWS.

2. The video (data plane). The actual movie file: gigabytes of data flowing to your TV or phone. This comes from Open Connect, Netflix's own content delivery network (CDN).

Here is what happens when you press Play:

1. Your device sends a request to Netflix's front door on AWS. This front door is an API gateway called Zuul. It routes requests, helps with authentication, and can shed load when things go wrong.

2. Zuul sends the request to the right microservices: profiles, recommendations, licensing, playback and more.

3. The playback service looks at which video servers are healthy, which ones have your title, and which ones are closest to you on the network. It sends your device a ranked list of servers to stream from.

4. Your device downloads the video from an Open Connect server, often one that sits inside your own internet provider's network.

Why split it like this? Because the two jobs need very different things. The brain needs flexibility, fast changes and lots of compute. The video needs huge bandwidth, close to the user. Putting each job on the right platform is cheaper and more reliable than forcing both into one place.

3. Open Connect: the video comes from near your home

A CDN (content delivery network) is a group of servers spread around the world that keep copies of content close to users. Most companies rent a CDN. Netflix built its own, called Open Connect, starting around 2012.

As of December 2022, Netflix said it had 18,000 servers in 6,000 locations across 175 countries.

How it works, in simple steps:

• Servers inside ISPs. Netflix gives its Open Connect Appliances (OCAs) free of charge to qualifying internet service providers. Netflix provides the hardware. The ISP provides rack space, power and network connection.

• Servers at internet exchanges. Netflix also places OCAs at internet exchange points, where many networks connect to each other, and peers with ISPs there.

• Filled at night. Netflix predicts what people will watch and copies those files onto the servers during off-peak hours, when the network is quiet. So when a big new show comes out, it is already sitting near you.

• Steered by the control plane. Each OCA reports its health, the routes it has learned (using BGP, the routing protocol of the internet) and the files it has to the Open Connect control plane, which runs in AWS. The control plane uses this data to point your device to the best server, and gives a list of options in case one fails.

The result: the video travels a short distance. It starts faster, buffers less, and puts less load on the wider internet. Netflix estimated that in 2021 alone the program helped ISPs avoid about $1.25 billion in costs.

Lesson for us: move heavy, read-only content as close to users as you can. You may not build your own CDN, but CloudFront, Cloud CDN, Akamai or Cloudflare give you the same idea.

4. Microservices: many small teams, many small services

Netflix runs hundreds of microservices. Each one does one job well: one for profiles, one for viewing history, one for recommendations, and so on.

Why does this help reliability?

• Small blast radius. If the recommendations service has a problem, you might see a simpler home screen, but you can still play a show. One broken part does not take down everything.

• Independent scaling. The playback service can scale up on a Friday night without scaling everything else.

• Independent deployments. Teams can release small changes often, instead of one big risky release.

But microservices bring new problems. When one screen needs many service calls, a slow service can make everything slow. Netflix built (and open-sourced) tools to deal with this:

• Eureka for service discovery (how services find each other).

• Ribbon for client-side load balancing.

• Hystrix for fault tolerance: it isolated calls between services, stopped one failure from spreading to others ("cascading failure"), and gave fallbacks, a simpler answer when a service could not respond.

Netflix later moved to Spring Boot and Spring Cloud as its core Java framework (announced in 2018), and Hystrix is now in maintenance mode. But the ideas are still the base of every good microservice system: timeouts, retries with limits, circuit breakers, and fallbacks. Today you will see them in service meshes like Istio and Envoy, and in libraries like Resilience4j.

A key design rule at Netflix: many services are stateless. They do not keep important data in their own memory. They keep it in caches or databases. This makes it easy to add or remove servers, and even to serve any member from any AWS region.

5. Caching: don't ask the database every time

Your home screen needs a lot of data, very fast: what you watched, what you rated, what rows to show. Hitting a database for every one of these would be slow and expensive.

So Netflix uses EVCache, a caching service built on Memcached. It keeps frequently read data in memory, so reads take milliseconds or less. Netflix's own blog says EVCache is used widely across its microservices and is open source.

Netflix also built global replication for EVCache. Its cloud runs in more than one AWS region (their blog named Northern Virginia, Oregon and Ireland). When data changes in one region, the change is sent to the other regions through a Kafka-based replication system. So if traffic moves to another region, the cache there is already warm.

There are really two layers of caching at Netflix:

1. Data cache (EVCache) for the brain.

2. Content cache (Open Connect) for the video.

Lesson for us: find the data that is read often and changes rarely, and cache it. Think about what happens when the cache is empty ("cold"), and how you keep it fresh.

6. Chaos engineering: break things on purpose

This is the part everyone remembers.

In 2011, Netflix wrote about Chaos Monkey, a tool that randomly shuts down production servers. Why would anyone do that?

Because in the cloud, servers will fail. Netflix's thinking was simple: if failure is going to happen anyway, make it happen often, in a controlled way, while engineers are awake and watching. Then you find weak spots and build automatic recovery. In their words, the goal was that when an instance fails at 3 AM on a Sunday, they won't even notice.

Chaos Monkey ran during business hours, in a carefully monitored environment, with engineers standing by. In July 2012, Netflix open-sourced it under the Apache License.

Chaos Monkey was the first member of the Simian Army:

• Latency Monkey added artificial delays to test slow services.

• Conformity Monkey found servers that did not follow best practices.

• Chaos Gorilla simulated losing a whole AWS Availability Zone.

• Chaos Kong simulated losing a whole AWS region.

Netflix practises region evacuation: moving all traffic out of one AWS region into the others. With a project called Nimble, which keeps extra capacity ready, Netflix said it cut evacuation time from about 50 minutes to about 8 minutes.

Lesson for us: you don't need to start with production. Start with a game day in staging. Kill a pod. Block a dependency. Watch your alerts. Write down what surprised you. Make the blast radius small and grow it slowly. Today, tools like AWS Fault Injection Service, Chaos Mesh and LitmusChaos make this easier.

7. Putting it all together

Here is the full picture in one place:

None of these ideas is new on its own. What makes Netflix strong is that they use all of them together, and they practise failure as a normal part of work.

8. How to use this in a system design interview

A very common interview question is: "Design a video streaming platform like Netflix."

Here is a simple structure you can follow:

1. Clarify. How many users? Which regions? Live or on-demand? Which devices? What matters most: start time, quality, cost?

2. Split control plane and data plane. APIs (login, catalogue, recommendations, playback decisions) on the cloud. Video delivery on a CDN. Say this early. It shows you understand the problem.

3. Content pipeline. Upload, encode into many bitrates and formats, store in object storage (like S3), push popular content to the CDN before peak hours.

4. Playback flow. Client asks the playback service, which returns a ranked list of CDN servers. The client uses adaptive bitrate streaming, so quality goes down instead of stopping when the network is slow.

5. Scale and reliability. Stateless microservices with auto-scaling. Caching for hot data. Multi-AZ always, multi-region for critical services. Timeouts, retries and circuit breakers between services.

6. Prove it. Monitoring on key metrics (stream starts per second is a great business metric), alerts, and chaos testing.

7. Trade-offs. Own CDN vs rented CDN. Active-active multi-region (more cost, faster recovery) vs active-passive (cheaper, slower recovery). Strong vs eventual consistency for things like viewing history.

Senior tip: interviewers listen for trade-offs and for real experience. If you can, add one line from your own work, for example: "In my last project we put static content behind a CDN and cut origin load by X%." Numbers and your role make the answer believable.

9. Key takeaways

• Remove single points of failure. Scale out, not just up.

• Split the brain (control plane) from the heavy data (data plane).

• Put content close to users with a CDN.

• Keep services small and stateless, with timeouts and fallbacks.

• Cache what is read often.

• Practise failure on purpose, starting small.

If this helped, save it, share it with a friend who is preparing for interviews, and follow @cloudtechbydevang for one cloud and DevOps topic a day.

Sources

• Netflix Q4 2025 shareholder letter (Jan 2026): "crossed the 325M paid memberships milestone". https://ir.netflix.net/files/doc\_financials/2025/q4/FINAL-Q4-25-Shareholder-Letter.pdf · CNBC coverage: https://www.cnbc.com/2026/01/20/netflix-nflx-earnings-q4-2025.html

• Variety (Apr 2024), Netflix stops quarterly subscriber reporting from Q1 2025: https://variety.com/2024/tv/news/netflix-stop-reporting-subscriber-numbers-starting-2025-1235975341/

• Netflix, "Completing the Netflix Cloud Migration" (Feb 2016): https://about.netflix.com/news/completing-the-netflix-cloud-migration

• Netflix, "Open Connect: Celebrating a Decade" (Dec 2022): https://about.netflix.com/news/open-connect-celebrating-a-decade-of-smooth-and-efficient-streaming

• Open Connect overview and partner docs: https://openconnect.netflix.com/en · https://openconnect.netflix.com/Open-Connect-Overview.pdf

• Netflix Tech Blog, "The Netflix Simian Army" (Jul 2011): https://netflixtechblog.com/the-netflix-simian-army-16e57fbab116

• Ars Technica, Chaos Monkey open-sourced (Jul 2012): https://arstechnica.com/information-technology/2012/07/netflix-attacks-own-network-with-chaos-monkey-and-now-you-can-too/

• Netflix Tech Blog, "Open Sourcing Zuul 2": https://netflixtechblog.com/open-sourcing-zuul-2-82ea476cb2b3

• Netflix Tech Blog, "Introducing Hystrix for Resilience Engineering": https://netflixtechblog.com/introducing-hystrix-for-resilience-engineering-13531c1ab362

• Netflix Tech Blog, "Netflix OSS and Spring Boot — Coming Full Circle": https://netflixtechblog.com/netflix-oss-and-spring-boot-coming-full-circle-4855947713a0

• Netflix Tech Blog, "Caching for a Global Netflix" (EVCache): https://netflixtechblog.com/caching-for-a-global-netflix-7bcc457012f1

• Netflix Tech Blog, "Project Nimble: Region Evacuation Reimagined": https://netflixtechblog.com/project-nimble-region-evacuation-reimagined-d0d0568254d4

Preparing for a Cloud, DevOps or SRE interview? Book a Free 15-min Cloud & DevOps Interview Guidance Call with me: what to study, how to answer system-design and on-call questions, plus a CV and roadmap check 👉 https://topmate.io/cloudtechbydevang/2358399