How to Make an App Like Google News

A practical breakdown of the architecture, data pipelines, and personalisation logic behind a Google News-style app — what it actually takes to build one, and where the complexity hides.

What Does "Like Google News" Actually Mean?

Before writing a line of code, be precise about what you're copying. Google News is not a simple RSS reader. It is a content aggregation platform that ingests thousands of sources, deduplicates near-identical stories, clusters related articles into topic threads, ranks them per user, and delivers a personalised feed in under 200ms. Those are four distinct engineering problems, and each one has a real cost.

If you want a curated news aggregator with a fixed editorial set of sources and no personalisation, that is a two-to-three week build. If you want genuine ML-driven personalisation at scale, you are looking at several months of infrastructure work before the product feels right. Know which one you are building.

The Core Architecture

A news aggregator at this scale has five layers. Each one feeds the next.

Ingestion

You need a crawler and feed parser that runs continuously. Most publishers expose RSS 2.0 or Atom feeds. Some require HTML scraping via tools like Scrapy or Playwright for JavaScript-rendered pages. Set your crawl intervals based on source frequency: breaking news sources need a poll every 60–120 seconds; weekly publications can be checked every few hours.

Store raw articles in an object store (S3 or GCS) before any processing. This gives you a clean replay log if your downstream pipeline breaks or changes.

Deduplication and Clustering

This is where most teams underestimate the work. On any major news event, 40–60 sources will publish near-identical wire stories. Showing all of them to a user destroys the experience.

Deduplication works at two levels. Exact duplicates are caught with a MinHash or SimHash fingerprint on the article body. Near-duplicates (same story, different wording) require semantic similarity, typically via sentence embeddings. Models like all-MiniLM-L6-v2 from Sentence Transformers give you good accuracy at low inference cost and run comfortably on CPU.

Clustering groups related articles into a "story thread" the way Google News does. HDBSCAN works well here because it does not require you to specify the number of clusters in advance, and it handles noise points gracefully. Each cluster becomes one card in the feed, with the highest-quality article surfaced as the lead.

Taxonomy and Tagging

Every article needs a category (politics, technology, sport) and entity tags (people, organisations, locations). You have two realistic options:

Approach	Accuracy	Latency	Cost
Fine-tuned BERT classifier	High	~80ms per article	Medium (GPU needed for training)
Zero-shot with an LLM (e.g. GPT-4o-mini)	Medium-High	~300ms per article	Higher at scale
Rule-based keyword matching	Low	<1ms	Near zero

For a production system processing thousands of articles per hour, a fine-tuned classifier is the right default. Train on a labelled dataset of at least 10,000 articles per category; anything less and the model will struggle with edge cases.

Named entity recognition (NER) can be handled by spaCy's en_core_web_trf transformer pipeline, which gives you reliable PERSON, ORG, and GPE tags out of the box.

How Does Personalisation Actually Work?

Personalisation is a recommendation problem. The inputs are implicit signals: which articles a user taps, how long they read, what they skip, what they share. You almost never get explicit ratings.

The standard starting point is a content-based filter: represent each article as a vector of topic tags and entity weights, build a user profile vector from their reading history, and rank articles by cosine similarity. This works well enough for the first few weeks of usage but converges on a filter bubble quickly.

Collaborative filtering (matrix factorisation via ALS or a two-tower neural model) adds the "users like you read this" signal. At lower user counts, below around 50,000 daily actives, collaborative filtering does not have enough data to outperform content-based. Invest in it when you have the scale to justify it.

A practical production setup uses a two-stage pipeline: a candidate generator that retrieves the top 500 articles for a user from the full corpus cheaply (approximate nearest neighbour search with FAISS), followed by a ranker that scores those 500 with a more expensive model that includes recency, source diversity, and engagement features. This is the same pattern used by YouTube and Twitter's feed ranking.

What Infrastructure Do You Actually Need?

The ingestion and processing pipeline is event-driven. Apache Kafka handles the message queue between crawler, processor, and ranker. Kafka gives you durable, replayable event logs, which matter when you need to backfill embeddings after a model update.

For the serving layer, article metadata and user profiles sit in PostgreSQL. Embedding vectors go into a dedicated vector database: pgvector works for under a million articles; Weaviate or Qdrant scale further with less operational overhead. Pre-computed FAISS indices can be rebuilt nightly and served from memory.

Mobile clients (iOS and Android) should use a GraphQL API for the feed. It lets the client specify exactly what fields it needs, which matters when you are optimising payload size on mobile networks.

Push notifications for breaking news need a separate pipeline. Topics detected as breaking (velocity-based: more than 20 new articles in 10 minutes on the same cluster) trigger a fan-out via Firebase Cloud Messaging. Keep the notification logic simple; users who receive more than two breaking news notifications per hour tend to disable them entirely.

Licensing, Copyright, and Data Risks

This is the part most teams skip until a lawyer makes them revisit it.

Scraping article full text and storing it is legally murky in most jurisdictions. The standard approach is to store only the headline, summary (under 150 words), source URL, and metadata. Always link back to the original publisher. If you want full-text indexing for better semantic search, you need licensing agreements with the publishers, or you use a licensed news API such as NewsAPI.org, The Guardian API, or the New York Times API, all of which have clear terms of use.

Some publishers block crawlers via robots.txt. Respect it. Beyond the ethical issue, ignoring robots.txt creates legal exposure under the Computer Fraud and Abuse Act in the US and equivalent statutes elsewhere.

Conclusion

The hardest parts of building a Google News clone are deduplication, personalisation cold-start, and feed latency, in that order. The technology choices (Kafka, FAISS, HDBSCAN, a fine-tuned classifier) are well-understood; the difficulty is in the integration and the data quality work that precedes any ML.

If you are starting now, build the ingestion and clustering layer first. Ship a non-personalised feed with good deduplication, gather real user behaviour data, and then layer in personalisation once you have signal to train on. Trying to build the personalisation system before you have traffic is the most common reason these projects stall.

The next concrete step: define your source list, set up a Scrapy-based crawler against a sample of 20–30 RSS feeds, and measure how much duplication you see in a 24-hour window. That number will tell you more about your real engineering problem than any architectural diagram.

FAQ

How long does it take to build a news aggregator app? A basic aggregator with curated sources, categorisation, and a clean mobile UI takes eight to twelve weeks. Adding ML-based personalisation, semantic deduplication, and real-time clustering extends that to five to seven months for a production-ready system. The data pipeline work accounts for most of the additional time.

What is the difference between a news aggregator and a news recommender? An aggregator collects and organises content from multiple sources. A recommender decides which content to surface to which user, based on behaviour and preferences. Google News does both. Many apps start with aggregation and add recommendation later once they have enough user data to make it worthwhile.

Can I use NewsAPI or similar services instead of building my own crawler? Yes, and for most early-stage products you should. NewsAPI.org covers over 150,000 sources, has a clean REST API, and handles the crawling and licensing complexity for you. The trade-off is a cost per API call at volume and less control over crawl frequency. Build your own crawler when you need real-time ingestion or sources the API does not cover.

How do I handle the cold-start problem for new users? New users have no behaviour history, so personalisation has nothing to work with. The standard solution is onboarding topic selection (ask for five to eight interests) combined with popularity-based ranking within those topics. After 10–15 article interactions, you have enough signal to shift to a user-specific model.

What does it cost to run a news app at scale? Infrastructure costs depend heavily on user count and article volume. A system processing 100,000 articles per day and serving 50,000 daily active users typically runs on around $800–$1,500 per month in cloud compute, excluding CDN and push notification costs. Embedding inference and vector search are the dominant cost drivers at that scale.

Have a project in mind? Contact Sodio Technologies to discuss your requirements and explore the right technology solution for your business.