Scalability Engineering

From Dozens to Millions — Without Rewriting

We have taken systems from 3 requests per second to 3,000+, and handled up to 10,000 transactions per second in production. When your growth curve outruns your architecture, we re-engineer the bottleneck, not the product.

Tech Stack

Technologies We Use

Proven tooling for throughput, data, and traffic at scale — chosen per workload, not by trend.

Kubernetes

Horizontal pod autoscaling and cluster-level scaling primitives

Kafka

High-throughput event streaming for decoupled, scalable pipelines

RabbitMQ

Reliable queues with back-pressure and priority semantics

Redis

In-memory caching and rate limiting at microsecond latency

PostgreSQL

Partitioning, read replicas, and connection pooling for read-heavy loads

CockroachDB

Distributed SQL for geo-replicated, horizontally scaled data

Elasticsearch

Distributed search and analytics at scale

ClickHouse

Columnar analytics for sub-second queries on billions of rows

Istio

Service mesh for traffic splitting, retries, and circuit breaking

NGINX

Edge load balancing and layer-7 traffic shaping

Cloudflare

Global edge caching and DDoS-resilient traffic offload

gRPC

Binary RPC for high-throughput internal service calls

Apache Pulsar

Multi-tenant messaging for scale-out event architectures

Temporal

Durable workflows for long-running, scalable orchestration

HAProxy

High-performance TCP/HTTP load balancing

k6 / Locust

Load generation frameworks for scale validation

How We Work

Our Scalability Playbook

1

Load Profiling & Bottleneck Hunt

We instrument the full request path, run load tests at 2× to 10× current peak, and pinpoint the exact component that breaks first — CPU, IO, lock contention, or database.

2

Architecture for the Next 10×

We design for the traffic shape you will have, not the one you have today — horizontal over vertical, async over sync, cached over computed, partitioned over monolithic.

3

Incremental Rollout

Scalability changes ship behind feature flags and traffic-mirroring. No big-bang rewrites — we move read paths first, then writes, with automated rollback at every step.

4

Continuous Capacity Management

Autoscaling tuned to real traffic patterns, capacity dashboards for product and ops, and quarterly load-test drills so the next growth spike does not become an incident.

Capabilities

What We Deliver

Re-architecture, data scaling, async design, caching, autoscaling, and load validation — the full scalability stack.

Throughput Re-Architecture

From 500 TPS to 3,000 TPS, from 3/day to 3,000+/day — we have delivered these lifts in production. Event-driven pipelines, parallel workers, and back-pressure-aware retries.

Database Scaling & Partitioning

Read replicas, sharding, connection pooling, query plan surgery, and multi-AZ topologies. We keep your database from becoming the ceiling.

Event-Driven & Async Design

Kafka, Pulsar, and queue-based architectures that decouple services so one slow dependency cannot take the whole platform down.

Caching & Edge Delivery

Multi-tier caching with Redis, Varnish, and CDN edge rules — hot paths answered in milliseconds, origin traffic cut by 70-90%.

Autoscaling & Capacity Engineering

Horizontal autoscaling tuned to real traffic shape. Spot + reserved + on-demand portfolios sized to your load profile, not a one-size-fits-all template.

Load Testing & Scale Validation

We prove the architecture before traffic does. Load test harnesses, traffic mirroring, and chaos drills that certify capacity at 10× current peak.

Proven Results

Related Case Studies

ICS Mobile
6× Throughput

Re-architected a telecom messaging pipeline from 500 to 3,000 TPS — SMS, WhatsApp, and RCS delivered near real-time, client complaints dropped to zero.

Kashti FinServ
1,000× Growth

Lifted a loan aggregator from 3 to 3,000+ applications per day — NBFC-ready architecture that passed regulatory due diligence and onboarded partners in days.

Spend The Bits
80% → 99.9% Uptime

Rebuilt a payments core on Kubernetes + Kafka — thousands of daily transactions served reliably, with weekly releases and zero high-severity incidents for a year.

AllIndex
Sub-Second at Scale

Built an institutional backtesting engine with sub-second analytics on Bloomberg-tier datasets — architecture scale-tested to 10× production load.

Eduley
Exam-Window Scale

Thousands of concurrent students sustained through exam windows — Kubernetes autoscaling tuned to the Canadian academic calendar traffic shape.

Growth Is Coming. Be Ready.

Whether you are about to 10× or already breaking under current load, a scalability audit tells you exactly what will bend and when.