Cloud & DevOps

Systems that handle real traffic

T7 Solution engineers scaling and performance — autoscaling, load testing, caching layers, database tuning and CDN edge — for sudden traffic, steady growth and predictable events.

Overview

Scaling problems rarely announce themselves — they land on launch day, sale day or press day. Systems fail at the slowest link, and most teams don't know what that is until it fails.

We load-test against realistic scenarios, tune database indexes and queries, add cache layers (Redis, CDN), and set autoscaling policies with sensible headroom.

Every engagement produces a documented capacity plan: at X RPS this breaks, here's the next-hop fix, and here's the cost curve.

What we ship

Load testing

k6, Gatling and Locust scenarios that model real user journeys — not synthetic /health hammering.

Database performance

Query plans, indexes, connection pooling (PgBouncer), read replicas and partitioning.

Caching layers

Redis, Memcached and CDN caching with correct invalidation and stale-while-revalidate.

Autoscaling

HPA/VPA, Karpenter, Cluster Autoscaler and cloud native ASG policies tuned to lead traffic.

Async + queues

Sync vs async decomposition, queues (SQS, RabbitMQ, BullMQ) and back-pressure handling.

Capacity planning

Documented headroom, break-points and next-hop fixes with cost curves.

How we're different

Realistic load tests

We model user journeys and cache/DB hit patterns — not just hammer /health.

DB-first tuning

Most scale problems are DB problems. We fix indexes and queries before we scale pods.

Cache-safe

Invalidation done right — no stale-forever bugs or thundering-herd fills.

Documented capacity

You get a written 'this breaks at X RPS, next fix costs Y' — no more launch-day surprises.

Tech stack

k6GatlingRedisPgBouncerCloudFrontCloudflareKarpenterPostgreSQL

Frequently asked questions

Can you load-test our system?

Yes — k6/Gatling/Locust scenarios modelling real user journeys, with reports and next-fix recommendations.

Do you tune databases?

Yes — query plans, indexes, PgBouncer, read replicas and partitioning for PostgreSQL, MySQL, MongoDB and others.

Autoscaling: how aggressive?

Depends on traffic pattern. We tune with lead scaling so pods spin up before latency hits — not after users notice.

Can you plan for a launch or sale?

Yes — capacity planning, pre-warming caches, pre-scaling ASGs, on-call setup and post-event review.

Ready to Build Your AI Product?

Talk to a senior AI consultant from T7 about your industry, workflow, or product idea. Free, no commitment — reply within one business day.

  • · AI feasibility & architecture review
  • · Product / MVP roadmap
  • · Integration & automation strategy