--- title: AI optimization: How we cut energy costs in social media... description: A software engineer who worked on Instagram Reels recommendations at Meta shares how the team achieved megawatt-scale energy savings and eight-figure annual... canonical: https://daily.dev/posts/ai-optimization-how-we-cut-energy-costs-in-social-media-recommendation-systems-aui3bciak twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: AI optimization: How we cut energy costs in social media recommendation systems | daily.dev og:description: A software engineer who worked on Instagram Reels recommendations at Meta shares how the team achieved megawatt-scale energy savings and eight-figure annual... og:url: https://daily.dev/posts/ai-optimization-how-we-cut-energy-costs-in-social-media-recommendation-systems-aui3bciak og:image: https://api.daily.dev/og/posts/AUi3bCiAk.png og:image:alt: AI optimization: How we cut energy costs in social media recommendation systems og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # AI optimization: How we cut energy costs in social media recommendation systems **[InfoWorld](https://daily.dev/sources/infoworld)** · 8 min read · 0 upvotes · 0 comments ## Summary A software engineer who worked on Instagram Reels recommendations at Meta shares how the team achieved megawatt-scale energy savings and eight-figure annual cost reductions by optimizing the data pipeline rather than the models themselves. Three key techniques were used: 'lazy logging' (only persisting feature vectors for the first few items a user is likely to see, then paginating on demand), batched storage schemas (storing user features once per request instead of duplicating them per impression, cutting storage by 40%), and large-scale feature auditing (removing thousands of statistically insignificant features to reduce inference latency and storage costs). The core insight is that efficiency in large-scale AI systems often comes from rethinking how data is computed, fetched, and stored rather than from model architecture changes. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [The software fix that could shrink AI’s energy bill without new hardware](https://daily.dev/posts/the-software-fix-that-could-shrink-ai-s-energy-bill-without-new-hardware-e6l2pwloq) · The New Stack · 0 upvotes · 0 comments - [The Economics of Reuse](https://daily.dev/posts/the-economics-of-reuse-vos6cxtcd) · Medium · 0 upvotes · 0 comments - [Optimizing Cost per Insight in AI Data Analytics Systems](https://daily.dev/posts/optimizing-cost-per-insight-in-ai-data-analytics-systems-9skawywiz) · SitePoint · 0 upvotes · 0 comments - [Building Highly Efficient Inference System for Recommenders Using PyTorch – PyTorch](https://daily.dev/posts/building-highly-efficient-inference-system-for-recommenders-using-pytorch-pytorch-td53tfvcb) · PyTorch · 0 upvotes · 0 comments - [15 Techniques to lower the cost of AI](https://daily.dev/posts/15-techniques-to-lower-the-cost-of-ai-zhqxndu8c) · Lightbend · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#data-engineering](https://daily.dev/tags/data-engineering), [#distributed-systems](https://daily.dev/tags/distributed-systems), [#recommendation-systems](https://daily.dev/tags/recommendation-systems) [View this post on daily.dev](https://daily.dev/posts/ai-optimization-how-we-cut-energy-costs-in-social-media-recommendation-systems-aui3bciak) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"AI optimization: How we cut energy costs in social media recommendation systems","url":"https://daily.dev/posts/ai-optimization-how-we-cut-energy-costs-in-social-media-recommendation-systems-aui3bciak","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/ai-optimization-how-we-cut-energy-costs-in-social-media-recommendation-systems-aui3bciak"},"datePublished":"2026-03-20T09:06:37.584Z","dateModified":"2026-03-20T09:07:14.263Z","description":"A software engineer who worked on Instagram Reels recommendations at Meta shares how the team achieved megawatt-scale energy savings and eight-figure annual...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/4f98bf0bd124d7c04144c7f35241b5ef?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/4f98bf0bd124d7c04144c7f35241b5ef?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"InfoWorld","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"InfoWorld","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/bf6d68a999064029b0bb09aa6268f1f3","url":"https://daily.dev/sources/infoworld"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/ai-optimization-how-we-cut-energy-costs-in-social-media-recommendation-systems-aui3bciak","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,data-engineering,distributed-systems,recommendation-systems","timeRequired":"PT8M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"InfoWorld","item":"https://daily.dev/sources/infoworld"},{"@type":"ListItem","position":3,"name":"AI optimization: How we cut energy costs in social media recommendation systems"}]} ```