A growing number of companies have recently been openly reassessing their technological strategies. For example, Discord is handling trillions of messages with renewed infrastructure, while Netflix continues to rely on an upgraded version of Java, even in 2025.
Meta also joins the list, and it turns out that Instagram’s recommendation system doesn’t rely on one giant AI model. Instead, it operates over 1,000 machine learning models in production, each powering a different slice of the user experience—whether it’s deciding what shows up in Feed or Reels, who gets tagged in a post, or which notifications are marked as important. How is all of this made possible?
In a technical post detailing this transformation, Meta engineers admitted that even the Instagram team itself struggled to keep track of what was running in production.
“Even as a team focused on one app, Instagram, we couldn’t stay on top of the growth, and product ML teams were maintaining separate sources of truth, if any, for their models in production,” they wrote.
What followed was a sweeping internal overhaul—from introducing a formal model registry and building launch automation to defining a new standard for model health.
A Central Registry for All Models
The turning point came with the introduction of the model registry, described as a ledger that documents each model’s business role, traffic criticality, and technical metadata.
Before this, responding to an issue meant scrambling to figure out which team owned the model, what it did, and whether it was safe to shut down. With the registry in place, operational triage became faster and more accurate.
“Depending on the importance of the model and the criticality of the surface it’s supporting, the response is going to differ in kind,” the team noted.
This registry wasn’t just about documentation; it enabled everything from policy enforcement and observability to structured automation. Moreover, it became a backbone for new tools like dashboards and alerts, giving engineers system-wide visibility for the first time. “We standardised the collection of model importance and business function information, ensuring most of our operational resources were going towards the most important models,” the engineers explained.
Redefining Model Health
With hundreds of models running in production, small degradations were getting lost in the noise. Conventional reliability metrics—like uptime or request success rate—weren’t enough. The real problem was invisible inaccuracy, where models returned technically correct responses that failed to match user preferences. To tackle this, Instagram introduced model stability as a core health metric.
Unlike basic backend services, ranking models predict how likely a user is to take an action, like clicking or following, and then sort content accordingly.
“It’s important that these scores accurately reflect user interest, as their accuracy is directly correlated to user engagement,” the engineers wrote.
If any model’s predictions fall outside expected ranges, it is marked unstable. “This has unlocked our ability to build generic alerting to guarantee detection of our most important models becoming unstable,” they added.
Rebuilding the Launch Pipeline
Model iteration at Instagram used to be slow. Launching a new model meant manually testing its impact on system load, running performance diagnostics, estimating replica counts, and gradually shifting traffic in small batches. “By the time we got to the end of this arduous process, the ordeal still wasn’t over,” the technical post stated.
This process has now been automated end-to-end. Engineers can benchmark performance using recorded traffic, simulate load, and estimate deployment costs before anything goes live. The launch platform then handles the rollout process, including capacity scaling automatically.
“This suite of launch automation has dramatically reduced the class of SEVs related to model launches, improved our pace of innovation…and reduced the amount of time engineers spend conducting a launch by more than two days.”
Operating With 1,000+ Models At Scale
As Instagram’s model collection grew, resource allocation became another source of friction. Without quotas, teams competed for infrastructure and blocked each other’s progress.
Meta’s solution was to create virtual capacity pools for each team, enabling parallel experimentation without central bottlenecks. This change gave teams clearer guardrails, more autonomy, and a predictable path to shipping.
Instagram’s journey to over 1,000 models is an interesting technology story in the AI-driven era. It also offers us a sneak peek into what goes on behind the scenes to enable the endless doomsday scrolling that users have come to know and love. After all, it only takes a small army of machine learning models to decide which cat video or dance reel they absolutely must see next.
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