yroByteBuilt to Take the Load
Performance, reliability, and cost engineering for products that are winning. When traffic becomes the problem you always wanted, we make sure it stays a good problem.
Start a ProjectOur work ships under strict NDAs. We show what we know, not who we built it for.
profile → find the real hot path
measure → before and after, always
cache → cut the redundant work
partition → spread the load
harden → survive the failure modes
observe → verify under real trafficSignals You Need This
Scaling problems rarely announce themselves. They creep in as slower pages, longer deploys, larger invoices, and a team that spends more time firefighting than building. If these sound familiar, the architecture is asking for attention.
Latency Creep
Pages and API calls that used to feel instant now visibly drag — and it's getting worse each month.
Outages Under Peak
The system falls over exactly when the most customers are watching — launches, campaigns, busy hours.
Runaway Cloud Bills
Infrastructure spend grows faster than usage, and nobody can say precisely where it's going.
Velocity Collapse
Simple features take weeks because every change risks breaking something under load.
Performance Engineering
We don't guess at performance — we measure it, fix the thing the profiler points at, and measure again. Optimization without evidence is just superstition.
Profiling First
Trace the real hot paths under real traffic before touching a line of code. The bottleneck is rarely where intuition says.
Query Strategy
Indexing, query rewrites, N+1 elimination, and read/write separation — the database is usually where the wins live.
Caching Strategy
Multi-layer caching with deliberate invalidation — because a stale cache is worse than no cache.
Hot-Path Rewrites in Rust
When a critical path outgrows its runtime, we rewrite that path — and only that path — in Rust for predictable speed.
Async & Batching
Move slow work off the request path; batch what doesn't need to happen one at a time.
Load Testing
Prove headroom before launch day does it for you — realistic load tests against production-shaped environments.
Reliability & Resilience
Fast is worthless if it's down. We engineer for the failures that will happen — hardware dies, networks partition, dependencies stall — so your product degrades gracefully instead of collapsing.
- Redundancy and failover across services, databases, and regions
- SLOs that define what reliable actually means for your product
- Circuit breakers, timeouts, and backpressure so one slow dependency can't take everything down
- Incident response — alerting that means something, runbooks, and blameless postmortems
resilience:
redundancy: no single point of failure
failover: automatic, rehearsed
slos: defined, measured, alerted
degradation: graceful, by design
incidents: detected before customers callInfrastructure & Cost
Cloud spend is an engineering problem wearing a finance costume. The same discipline that makes systems fast makes them cheaper to run.
Autoscaling
Capacity that follows demand — up for the spike, back down the moment it passes.
Right-Sizing
Match instances, databases, and storage tiers to measured workloads instead of guesses.
Multi-Region
Serve users from where they are and survive the loss of an entire region.
Cost Visibility
Attribute spend to services and features, so every invoice line has an owner and a reason.
Data at Scale
The data layer is where scaling problems concentrate. We restructure storage, movement, and analysis so the database stops being the ceiling on your growth.
Partitioning & Sharding
Split data along its natural seams so no single node carries the whole product.
Queues & Streams
Decouple producers from consumers so spikes queue up instead of knocking services over.
Analytics Pipelines
Move reporting off the production database and into pipelines built for it.
Retention & Archival
Tier cold data out of hot storage so working sets stay small and fast.
Working Inside Existing Codebases
We scale what exists before proposing rewrites. A consultancy that recommends starting over is usually solving its own problem, not yours. Our default is to work inside your codebase — understand it, measure it, and improve it incrementally — reaching for a rewrite only when the evidence says a specific component has genuinely hit its ceiling.
Read Before We Write
We learn your system's history and constraints before changing it.
Incremental Change
Small, reversible steps that ship continuously — never a big-bang cutover.
Evidence Over Opinion
Rewrites are argued with profiles and measurements, not preferences.
Your Team Stays Fluent
We work alongside your engineers so the knowledge stays when we leave.
Who This Is For
Products Outgrowing Their Architecture
Growth arrived faster than the system was designed for, and the strain is showing in latency, incidents, or cost.
Teams That Need Reinforcement
Your engineers know the product; you need deep platform experience alongside them for the scaling push.
Companies Facing a Known Peak
A launch, a season, or a contract will multiply your traffic — and the system has to hold when it does.
Make Growth a Good Problem
Tell us where the system hurts — latency, outages, cost, or all three. We'll find the bottleneck and engineer it away.