Poshmark — Scaling Social Commerce to 50M+ Users
The results
Metric
Manual reporting time
Financial statement accuracy
Leadership meeting prep time
Decision-making speed
Before
Monolithic architecture bottlenecking developer velocity
Frequent scaling issues during peak holiday sales
High latency in product recommendations and feed search
Fragmented deployment pipelines across teams
After
50M+ users supported with 99.99% uptime
$1B+ GMV processed seamlessly at high concurrency
Sub-second real-time search and recommendation latency
Microservices transition that accelerated dev cycles
What our users say
"AppMetry delivered our entire platform ahead of schedule—flawless execution and real partnership."
Scaling Poshmark taught us how to design systems that handle massive transactional loads without breaking. At Appmetry, we embed this exact architectural pedigree into every client project.
Ketan Jain
CTO, Appmetry (Former VP of Engineering, Poshmark)
Key Features Used
- High-throughput architecture — microservices migration and event-driven pipelines
- Search & Recommendations — Elasticsearch · Real-time indexing · ML ranking
- Payment & Checkout — High-concurrency transaction engine
- DevOps & Infrastructure — Cloud scaling · Kubernetes · Automated canary deployments
Ready to build what's next?
Let's discuss your product goals and design a high-performance roadmap for your software.