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// 01 · Service

WebRTC performance and cost audit

A fixed-scope diagnostic of a real-time or voice system already in flight: where the latency actually goes, what breaks under load, what it costs per minute — and a prioritised, costed fix list you can act on with or without us.

  • WebRTC
  • WHIP
  • LiveKit
  • MediaMTX
  • GStreamer
  • FFmpeg
Start with a Sprint

// The problem

Why this is hard

By the time anyone books an audit, the system usually works. That is what makes it hard. Nothing is down, nobody is paged, and the complaint is a shape rather than an error: the bill grew without more users, the box filled up sooner than its headroom suggested, a change that passed staging broke something in production that nobody can reproduce.

The reason those resist debugging is that the wrong number is rarely the obvious one. A hardware encoder can hit its session ceiling and fall through to software with no log line, so the machine keeps serving while its cost per stream multiplies. A queue bounded in buffers is a different depth in wall-clock at 60 fps than at 30, so a value that was fine at development framerate silently becomes too shallow at production framerate. A transcode ladder nobody asked for re-encodes every publisher whether or not anyone watches the extra layers.

None of that is visible from a dashboard, and none of it is guessable. It has to be measured — which is the entire content of this engagement.

// What we build

What you get

Latency budget, hop by hop

Capture, encode, ingest, transport, decode — each hop measured and attributed on your path, so you fix the one that is wrong instead of replacing the component you suspect.

Capacity, measured not estimated

Cores and memory per stream on your hardware, and the ceilings that are not CPU — hardware encoders enforce a session cap, and crossing it can degrade silently rather than fail.

Cost per minute, on your actual path

What a minute of stream costs today, priced against your real configuration and your provider's published rates — usually dominated by transcode rather than by bandwidth or connection time.

A fix list with numbers on it

Each finding costed against what it saves, ordered so the cheap reversible changes come first. Yours to act on with your own team, with us, or not at all.

// How it fits together

The system we build

  1. Inventoryone probe per source
  2. Measurehops, cores, minutes
  3. Reproduceat production load
  4. Priceagainst your rates
  5. Fix listcosted, ordered
A representative shape — abstract by design; we build it in your stack and your conventions.

// Deliverables

  • Measured latency budget, end to end
  • Failure and load findings
  • Cost-per-minute breakdown
  • Prioritised, costed fix list

// 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

    $2–4kfixed · 1–2 wks

    1–2 weeks

    De-risk before you build — measured latency budget, architecture, and a costed fix list. Credited against the build.

  • Build

    from$12kfixed scope or pod

    typically 1–3 months

    Ship it end-to-end and take it to production, in your repo and conventions.

  • Run & Scale

    from$1.5k/ month

    ongoing

    Keep it working after launch — evals, latency and cost tracking, and incident response.

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

$2–4k fixed, one to two weeks, credited against the build if you go on to one. We publish the figure because nobody else in this category does — every comparable studio quotes a flat fee without naming it and routes you to a discovery call first. The scope is fixed too: the four deliverables above, whatever we find.

Then you have the numbers, which is a result rather than a wasted engagement — a measured latency budget and a real cost-per-minute are what let you say no to the next optimisation someone proposes. It is also uncommon: the systems that get audited are the ones already showing a shape, and a shape is usually a number.

Yes, and the deliverable is built for that — the fix list names the change, the file or setting, and what it costs against what it saves. We would rather be the people whose audit you acted on than the people you had to retain to understand it.

// 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 real-time ai audit?

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