Stream has reached a major milestone in activity feed infrastructure, successfully benchmarking over 37 million operations with a 10% write and 90% read workload distribution across a dataset of 100M users, 500M activities, and 200M follow relationships. Each scenario was tested at 500, 1,000, and 1,500 requests per second to measure performance under increasing load.
Stream Activity Feeds Benchmarks at a Glance
In this benchmark, Stream Activity Feeds simulated a large-scale consumer application with 100 million users, 500M activities, and realistic usage patterns.
Key results include:
- ~11--15ms p50 read latency, stable across all scenarios
- Sub-32ms p50 write latency even at peak load
- 100% success rate across all tests
- Consistent performance up to 4.5 million peak concurrent users
- No degradation as request volume increased
See full architecture and benchmarks docs.
These results remained stable across low, medium, and high concurrency scenarios, demonstrating that Stream's feed performance does not degrade as applications grow.
Why Activity Feeds Don't Scale Easily
Activity feeds look simple on the surface, until they grow.
Every user's feed is unique, shaped by who they follow, what they interact with, and how frequently they open the app. At scale, this creates a fundamental challenge: you can't evenly distribute load when every feed is different.
Many feed systems slow down as user counts grow, forcing teams to compromise on personalization, introduce caching hacks, or rebuild infrastructure under pressure. Latency spikes, inconsistent ranking, and feed freshness issues quickly follow, directly impacting engagement and retention.
Stream was built to avoid those tradeoffs from day one.
Built for Real Personalization at Scale
Out of the box, teams can power "For You" style feeds using combinations of:
- Follow graphs
- Popular and trending content
- Interests, location, and custom attributes
- Suggested connections
- Fully custom queries and ranking logic
Multiple activity sources can be blended together and ranked dynamically, allowing teams to experiment, iterate, and evolve their feed logic without re-architecting backend systems.
For teams exploring AI-driven discovery, Stream also supports enrichment at write time, enabling smarter ranking and interest-based personalization as feeds scale.
Performance You Can Rely On — Even at Peak Load
The benchmark processed tens of millions of operations across a dataset that included:
- Hundreds of millions of activities
- Hundreds of millions of follow relationships
- A realistic mix of reads and writes
- Sudden spikes in concurrent users
Even at the highest tested scale, 4.5 million concurrent users under heavy load, Stream maintained fast response times and flawless reliability.
For teams running social platforms, marketplaces, media apps, or community-driven products, this means no surprises when engagement spikes.
Enterprise-Ready by Design
Scaling feeds isn't just about latency; it's about trust.
Stream Activity Feeds are built with:
- Proven production benchmarking at massive scale
- Infrastructure-level protections against traffic spikes
- Global reliability and predictable performance
- Clear pricing models that scale with usage
Instead of guessing whether your feed infrastructure will hold up, Stream gives teams confidence, backed by real data, not marketing claims.
Transparent Benchmarks. Proven Results.
Many feed providers claim they can scale. Stream shows the numbers.
By publishing real-world benchmarks and continuously testing performance at scale, Stream gives engineering and product teams the clarity they need when choosing long-term infrastructure.
If your application depends on fast, personalized feeds today and needs to support tens or hundreds of millions of users tomorrow, try Stream Activity Feeds.