Zerodha, Groww, and Angel One Handle Crores of Transactions Per Day — Here Is What Their Data Infrastructure Looks Like

Zerodha, Groww, and Angel One process crores of transactions per day. During market open, Budget day, or RBI announcements, concurrency and latency become existential. This post is a research-based look at what their data infrastructure likely involves—and how the same class of solution is now within reach for mid-size Indian trading platforms and fintech.

We do not have inside knowledge of these companies' stacks. But their challenges are public: millions of concurrent sessions, order matching at millisecond scale, live price feeds, position and P&L updates, and regulatory reporting. No off-the-shelf database is designed for that combination. At some scale, every large broker has had to:

• •Cache and serve live prices with sub-ms latency
• •Maintain order book and match orders without blocking
• •Store and serve session and position state for millions of users
• •Persist and report trades for compliance

Doing that on generic PostgreSQL and Redis alone would mean massive over-provisioning, replication lag, and still unpredictable latency at peak. The logical step is a purpose-built data layer for the hot path.

• •In-memory storage for prices, order book, and session state
• •Data structures and networking tuned for the exact operations (e.g. order insert, top-of-book read, position update)
• •Lock-free or low-contention concurrency so writes do not block reads
• •Optional durability (write-ahead log) where required
• •Deployment in the broker's own cloud or data centre

That is custom storage engine territory. It is what we build at Hendoi: VeloxDB—purpose-built engines for your schema and workload.

Historically, only the largest brokers could afford an in-house team to build and maintain custom data infrastructure. Today, custom engine development is available as a service. You get the same architectural approach—in-memory, purpose-built, your schema—without the same headcount. We design and build the engine, deploy it in your environment (AWS India, on-premise, or hybrid), and hand over SDKs and documentation.

If you are an Indian trading platform, broker, or fintech scaling toward lakhs or crores of daily users, your data layer will decide whether you survive the next Budget day or market open. Replicas and bigger instances buy time; they do not fix the fundamental mismatch between generic databases and trading workloads. A purpose-built layer does.

We serve Indian fintech and global clients from Chennai. Contact us for a free architecture discussion.