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// 05 · Service

MLOps & AI infrastructure

The queues, workers, observability, and cost controls that keep models fast, reliable, and on budget once real traffic arrives.

  • Docker
  • Kubernetes
  • River
  • GitHub Actions
Start with a Sprint

// The problem

Why this is hard

Models behave differently under real traffic than in a demo: latency spikes, costs balloon, and a provider hiccup becomes an outage. The infrastructure around the model — queues, workers, observability, rate and cost controls — is what keeps an AI feature fast, reliable, and on budget once it actually matters.

// What we build

What you get

Queue & worker pipelines

Durable background processing so spikes and slow calls never drop work or block the UX.

Observability & tracing

Metrics and traces across every stage, so you can see and fix what's slow or failing.

Cost & rate controls

Budgets, rate limits, and caching so spend stays visible and bounded under load.

Deploy & autoscaling

Repeatable deploys and autoscaling so the system handles real traffic without a 2am page.

// How it fits together

The system we build

  1. Enqueuedurable jobs
  2. Workersscale with load
  3. Observemetrics + traces
  4. Cost + ratebudgets, caching
  5. Deployautoscaling
A representative shape — abstract by design; we build it in your stack and your conventions.

// Deliverables

  • Queue & worker pipelines
  • Observability & tracing
  • Cost and rate controls
  • Deploy & autoscaling

// How we work

From prototype to production, in four moves.

01

Discovery

We map the problem, the data, and the eval that defines "done".

02

Prototype

A working slice in weeks — real model, real data, measured.

03

Production

Hardened, observable, evaluable. Shipped where users live.

04

Scale

Cost, latency and reliability tuned as load and scope grow.

// Typical engagement

What it takes to work together

  • Architecture Sprint

    $4–8kfixed · 1–2 wks

    1–2 weeks

    De-risk before you build — architecture, a plan, and a working proof-of-concept.

  • Build

    from$20kfixed scope or pod

    typically 1–3 months

    Ship the product end-to-end, in your repo and conventions.

  • Run & Scale

    from$4k/ month

    ongoing

    Operate and improve after launch — SLA, monitoring, and iteration.

Indicative ranges — the final price and timeline depend on your project's scope, complexity, and integrations. A paid Architecture Sprint pins them down.

// FAQ

Common questions

With budgets, rate limits, and caching wired in from the start, plus observability so spend is visible per stage rather than a surprise at month end.

Yes — we start by reviewing what's running, instrument it for observability, then improve the queues, scaling, and cost controls incrementally rather than demanding a rewrite.

Model calls run through a durable queue with retries and backoff, a health-checked backup provider, and caching on hot paths — so a provider outage means slower or queued work, not a dropped request or a hard failure your users feel.

// Related

All services

// Industries

Where teams put this to work:

15+systems shipped
6+ yrsin production
~4 wksto a first slice
99.9%uptime SLA

// Let's build

Ready to build with mlops & infra?

Tell us where you are. We reply within a day with a concrete next step.