How to Make an App Like Inoreader

A practical breakdown of the architecture, data pipelines, and product decisions behind an RSS reader and content aggregation platform — from feed parsing to personalisation.

What Does an App Like Inoreader Actually Do Under the Hood?

Inoreader is an RSS and Atom feed aggregator. Users subscribe to feeds, and the app fetches, normalises, and presents articles from hundreds or thousands of sources. The surface looks simple. The engineering underneath is not.

The core loop is: discover feeds, poll them on a schedule, parse the content, deduplicate, store, index, and serve it to users — with filtering, search, and personalisation layered on top. Each of those steps has real complexity, especially at scale.

Inoreader reportedly handles billions of articles. You probably won't start there, but your architecture needs to survive the growth curve without a full rewrite at 100k users.

Designing the Feed Ingestion Pipeline

This is the most technically demanding part of the system. Get it wrong and you'll either hammer third-party servers with unnecessary requests, or miss updates for hours.

Polling vs. Push

Most RSS and Atom feeds are pull-only. You poll a URL, get XML, parse it. The standard protocol for push is WebSub (formerly PubSubHubbub). Feeds that support WebSub publish update notifications to a hub, and your app subscribes to that hub. This reduces polling frequency dramatically for high-volume feeds.

In practice, a large fraction of feeds on the open web don't support WebSub. You'll need both mechanisms.

For polling, a naive "poll everything every 15 minutes" approach breaks down fast. A feed with 1 update per week doesn't need the same cadence as a breaking news RSS feed. Use an adaptive polling algorithm: track the average publish frequency per feed and set the interval to roughly half that. Cap minimum intervals at something like 5 minutes for the fastest feeds, and 24 hours for the slowest.

The Parser Layer

RSS comes in multiple formats: RSS 0.9x, RSS 1.0 (RDF-based), RSS 2.0, and Atom 1.0. Real-world feeds violate spec constantly — encoding issues, malformed XML, missing required fields. Use a battle-tested library rather than writing your own parser. In Python, feedparser handles most edge cases. In Node.js, rss-parser is reasonable. Plan for a fallback HTML scraper for sources that don't offer structured feeds at all.

Normalise everything into a single internal schema immediately after parsing. Fields like published_at, updated_at, author, content, and guid need to be present or inferred from what's available. The guid (or id in Atom) is your deduplication key. If it's absent or unreliable, fall back to a hash of the URL or title plus date.

Storage Architecture

You're storing two kinds of data: feed metadata and article content. Keep them in separate stores.

Data Type	Recommended Store	Reason
Feed metadata, subscriptions	PostgreSQL	Relational, transactional
Article content (full text)	Elasticsearch or OpenSearch	Full-text search, field filtering
Article metadata index	PostgreSQL + Redis	Fast reads, caching unread counts
Media attachments	S3-compatible object store	Cost-efficient blob storage

Full-text search is a product requirement, not an optional feature. Users expect to search across their entire history. Elasticsearch handles this well, but operating it adds infrastructure overhead. OpenSearch (the AWS fork, currently at version 2.x) is a viable alternative with a more permissive licence.

How Do You Handle Personalisation and Filtering?

This is where the product differentiates. Basic RSS readers just show everything chronologically. Inoreader adds rules, filters, saved searches, and eventually ranking.

Start with user-defined rules: keyword filters, source-based filters, tag assignment. These are just predicate functions applied at ingestion time. Store rules in PostgreSQL, evaluate them in your ingestion workers, and tag articles before they hit the user's feed.

Algorithmic ranking is harder. A simple approach is to score articles by a combination of recency, source authority (based on user engagement with that source), and keyword relevance to the user's reading history. You don't need a neural model to do this usefully — a weighted scoring function with tunable parameters gets you far.

If you want machine learning, a collaborative filtering model trained on reading and starring behaviour works well. Matrix factorisation (SVD) or approximate nearest-neighbour methods (FAISS, Annoy) can surface relevant articles the user hasn't explicitly subscribed to. This is a meaningful engineering investment, so plan it as a separate phase.

What's the Right Backend Architecture?

A monolith is the right starting point if your team is small. Split into services when a specific component has scaling or deployment needs that differ from the rest.

The natural split points for a content aggregator are:

• Ingestion workers: CPU and I/O bound, need horizontal scaling independently of the API
• API layer: Stateless, scales with request volume
• Search service: Elasticsearch/OpenSearch cluster, scales with index size and query load
• Notification service: Handles email digests, push notifications, webhooks

Use a message queue (RabbitMQ or Kafka, depending on your throughput requirements) between ingestion workers and the API database. This decouples feed fetching from article storage and gives you backpressure handling for free. For most early-stage builds, RabbitMQ is sufficient. Kafka adds operational complexity that only pays off above roughly 100k messages per second.

For the API itself, a REST API with GraphQL for the client layer is a workable pattern. REST for feed management operations, GraphQL for the article reading interface where clients need flexible field selection.

Authentication and Multi-Device Sync

Use OAuth 2.0 with refresh tokens. Sync state (read/unread, starred, tagged) needs to be conflict-free across devices. A last-write-wins strategy breaks when users read on two devices offline. Consider a CRDT-based approach for read state, or at minimum, store a vector of timestamps per device per article and resolve on the server side.

Mobile and Web Clients

Inoreader has web, iOS, and Android clients. The web client is typically built with React or Vue; both work fine for this use case. The critical UX piece is virtualised list rendering — users can have thousands of unread articles, and DOM performance collapses without windowing (react-window or similar).

For mobile, React Native is a reasonable choice if you want code sharing across platforms. Native Swift and Kotlin give you better performance for the offline reading experience and background feed sync. If offline reading with full-text caching is a core feature, native is worth the extra build time.

Background sync on mobile is constrained by OS-level battery policies (iOS Background App Refresh, Android WorkManager). Design your sync logic to work within these constraints from day one, not as an afterthought.

Conclusion

Building a content aggregator like Inoreader is primarily a data pipeline problem wrapped in a product. The feed ingestion layer, deduplication logic, and search index are where most engineering effort goes. The client is table stakes.

If you're scoping this out, start with the ingestion pipeline and a minimal article reading API. Validate that your polling strategy handles the real-world messiness of RSS feeds before building personalisation. The product features are easier to add once the data foundation is solid.

If you want to talk through architecture choices specific to your use case, the team at Sodio has built content platforms and data pipelines at various scales. Reach out and we can work through the trade-offs.

FAQ

How long does it take to build an app like Inoreader? A working MVP with feed ingestion, article storage, search, and a web client typically takes 4 to 6 months with a team of 3 to 4 engineers. A production-ready product with mobile clients, personalisation, and reliable sync adds another 3 to 6 months depending on scope and team size.

What's the biggest technical challenge in building an RSS reader? Feed ingestion reliability. Real-world RSS feeds have malformed XML, inconsistent GUIDs, and unpredictable update frequencies. Building a parser and deduplication system that handles this without missing articles or creating duplicates takes more time than most teams expect.

How much does it cost to run a content aggregator at scale? Infrastructure costs vary widely. A service handling 1 million active users with a large article corpus could reasonably spend $5,000 to $20,000 per month on compute, storage, and search infrastructure, depending on caching efficiency and cloud region. Elasticsearch or OpenSearch clusters are typically the largest line item.

Should I use a third-party feed aggregation API instead of building my own? Services like Feedly's API or Superfeedr exist and can accelerate early development. The trade-off is vendor dependency and per-request costs that grow linearly with your user base. If feeds and content are core to your product, building ingestion in-house gives you more control over freshness, filtering, and data ownership.

What database should I use for storing articles? PostgreSQL for relational data (users, subscriptions, feed metadata) and Elasticsearch or OpenSearch for full-text article search and indexing. Avoid trying to do full-text search in PostgreSQL at scale — the tsvector approach works up to a point but doesn't match Elasticsearch's relevance tuning or query flexibility for large corpora.

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