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Instagram Architecture Explained: How Instagram Works Behind the Scenes

By Ravindra Bagale, AWS & DevOps trainer · LinkedIn · Published: 18 October 2026 · Last updated: 18 October 2026

Short answer: Instagram is a large Python Django application running in Meta's own data centres. Its main databases are PostgreSQL (users, media information, friendships) and Cassandra (feeds and activity), with Memcache in front for speed and an async queue for slow jobs. Instagram started on AWS (EC2, S3, CloudFront) and moved to Facebook's data centres in 2013–2014. Below you will see the real public picture, then a clearly labelled design for building something similar on AWS.

What does Instagram's architecture look like today (2023 to 2026)?

Today Instagram shares much of Meta's common infrastructure:

1. Threads (2023) was built on the Instagram stack and Meta's shared systems, such as the ZippyDB key-value store and the Async serverless job platform. That is how it launched in about five months.
2. Shared video backend (2023): video uploads, processing and delivery run on horizontal backend layers shared by Meta's apps (Video @Scale 2023).
3. Python performance: Instagram's Django server runs on Meta's Cinder/CinderX runtime, and in 2023 Meta upstreamed Immortal Objects to Python to save memory in its pre-forked web servers.
4. Recommendations: Explore and Reels use large machine learning systems; in 2025 Instagram scaled to about 1,000 recommendation models.
5. Live video (October 2026): Instagram Live ingest now uses Media over QUIC at Meta scale.

The core idea from the early days still holds: a big Python Django web tier, caches, and replicated databases across Meta data centres. Those older details are in the "How it started" part below.

Suppose you open Instagram and tap a reel

Let us start with something you do every day. You open Instagram on your phone in Pune and tap a reel. In less than a second, the video starts, and you see the likes, comments and the creator's name. What happened behind the screen?

1. Your phone sends a request to Instagram's servers over the internet.
2. The request reaches the nearest entry point of Meta's network and a load balancer.
3. The load balancer sends it to one of thousands of web servers running Instagram's Python code.
4. The web server first checks a fast in-memory cache (Memcache) for data like the like count.
5. If the data is not in cache, it asks the databases: PostgreSQL for the post and user details, Cassandra for feed and activity data.
6. The video file itself comes from Meta's storage and delivery network, close to you.
7. Small "later" jobs, like telling the creator you liked the reel, go into a queue and run in the background.

Keep this example in mind. Every part of this article is one of these seven steps.

What architecture style does Instagram use?

Instagram is famous for running one of the biggest Django deployments in the world. In 2019 the server was a Python/Django monolith with several million lines of code and thousands of endpoints, deployed continuously about every 7 minutes, around 100 times a day. Django is a Python web framework. Instead of splitting everything into hundreds of tiny services from day one, Instagram kept a large Python application at the centre and added separate systems around it when needed.

In simple words, the style is:

1. Stateless web tier: any web server can handle any request, because no user data is kept on the server itself. This makes it easy to add more servers.
2. Cache in front of databases: Memcache answers the most common reads so the databases are not overloaded.
3. Two kinds of databases: a relational database (PostgreSQL) for data that needs strong structure, and a wide-column NoSQL database (Cassandra) for huge, fast-growing data like feeds.
4. Async (background) processing: the web server puts slow jobs on a queue, and separate "async workers" pick them up.
5. Multiple data centres: In 2015 Instagram grew from one to three data centres so that a single failure does not take the app down.

Instagram's engineers have also worked on Python performance. Meta open-sourced Cinder, the performance-focused version of CPython that Instagram uses in production.

How it started: Instagram on AWS (2010 to 2014)

Yes. This is the most interesting part for AWS learners. In 2011, with only a few engineers and 14 million users, the whole app ran on Amazon EC2:

1. Ubuntu Linux servers on EC2.
2. Route 53 for DNS, and an Elastic Load Balancer (with SSL ended at the ELB) in front of three Nginx servers.
3. Django on Gunicorn as the app server.
4. PostgreSQL, split into thousands of logical shards placed on a few physical servers, with replicas in a different Availability Zone and EBS snapshots for backups. Each photo got a 64-bit, time-sortable ID generated inside PostgreSQL ("Sharding & IDs at Instagram", 2012).
5. Redis for the main feed, activity feed and sessions; Memcached for caching.
6. Photos stored in Amazon S3 and delivered by Amazon CloudFront.
7. Gearman as the task queue for jobs like sharing to Twitter and notifications.
8. Monitoring with Munin (graphs and alerts), Pingdom (external checks), PagerDuty (incidents) and Sentry (Python errors).

After Facebook bought Instagram in 2012, the team moved everything to Facebook's data centres. In 2014 they used a clever trick: Facebook's private IP addresses clashed with EC2-Classic, so they first moved thousands of instances into an Amazon VPC (in under three weeks), and then connected that VPC to Facebook using AWS Direct Connect. They built a tool called Neti (Python + ZooKeeper, managing iptables rules) to make EC2 and VPC instances behave like one network.

Instagram architecture diagram (public parts)

This diagram shows only what Instagram has shared publicly. Press Play to follow a request step by step.

What does Instagram use? Main technologies

About Cassandra: Instagram started using Cassandra in 2012, replacing Redis, for fraud detection, Feed and the Direct inbox. Later Instagram built Rocksandra, which uses RocksDB as Cassandra's storage engine, and open-sourced it in 2018. It gave a big drop in tail latency (slowest responses).

Which cloud provider does Instagram use?

Today Instagram does not use a public cloud like AWS, Google Cloud or Azure for its main app. It runs on Meta's own data centres, using Meta's internal tools for deployment, networking and storage. That is the honest answer.

1. 2010–2013: AWS (EC2, ELB, S3, CloudFront, EBS).
2. 2013–2014: moved to an AWS VPC, then to Facebook data centres via Direct Connect.
3. 2015 onwards: spread across several Meta data centres in different regions.

How does Instagram store photos and videos?

In 2011, photos went straight to Amazon S3 and were served through CloudFront. Today the media lives in Meta's storage systems and is delivered through Meta's own edge network. Instagram has not publicly shared a detailed design of its current media storage, so we will not guess.

What is known about the 2015 multi data centre setup:

1. PostgreSQL and Cassandra data is global: it is copied (replicated) to every data centre.
2. Each region has read replicas, so reads stay local and fast.
3. Writes to PostgreSQL still go to the primary database, even if it is in another region.
4. Memcache is local to each region. To stop old data being shown, a database change triggers a cache invalidation message in other regions.

How does Instagram monitor its systems?

In the AWS days the team used Munin, Pingdom, PagerDuty and Sentry, as listed above. Today Instagram uses Meta's internal monitoring and alerting tools. The details of the current internal setup are not publicly shared, so this article does not name them.

If we built an app like Instagram on AWS (illustrative)

Suppose you want to build "PuneGram", a small Instagram-like app for your college. Here is how you could lay it out on AWS.

1. Network: VPC, subnets, route tables, gateways

1. Create one VPC with CIDR 10.0.0.0/16 in the Mumbai region.
2. Use two Availability Zones (A and B), so one AZ failing does not stop the app.
3. In each AZ make three subnets: public (load balancer, NAT), private app (your code), private data (databases, cache).
4. Attach one internet gateway (IGW) to the VPC.
5. Public route table: 0.0.0.0/0 → IGW. This is what makes a subnet public.
6. Private app route table: 0.0.0.0/0 → NAT gateway, so servers can download updates but nobody can connect in.
7. Private data route table: only the local VPC route. Databases never talk to the internet.

2. Compute: where the code runs

1. Put an Application Load Balancer (ALB) in both public subnets.
2. Run the API (Django like Instagram, or any language) on Amazon EKS or on EC2 with an Auto Scaling group in the private app subnets.
3. Scale out when CPU or request count goes up, scale in at night.
4. Run background workers (resize images, send notifications) as a separate deployment that reads from SQS.

3. Storage and databases

1. S3 stores the original photos and reels. Users upload directly using pre-signed URLs.
2. CloudFront caches photos and videos at edge locations close to users.
3. Aurora PostgreSQL stores users, posts and follows (writer in AZ A, reader in AZ B).
4. DynamoDB stores very high-traffic data like like-counts and activity items.
5. ElastiCache (Redis) keeps feeds and sessions in memory.

4. Monitoring

1. CloudWatch metrics and alarms: ALB 5xx errors, latency, CPU, database connections.
2. CloudWatch Logs for app logs; X-Ray traces to see which step is slow.
3. An alarm on the SQS queue age, so you know when workers fall behind.

Request flow diagram

Press Play. The numbered list below the diagram lights up with each step.

Instagram then, now, and our AWS design