Per-title encoding audit

You're probably paying
to deliver bits nobody
can see.

If you're using the same ladder across very different content, you may be overspending on delivery. We benchmark your current ladder against your own content and show you exactly how many kbps — and dollars — you can remove without sacrificing measured quality.

Free · one sample video · report back in 48 hours

The idea isn't new. Making it accessible is. Netflix demonstrated per-title encoding publicly in 2015. StreamSlim brings the same basic optimization principle to teams that don't have a dedicated video-encoding research group.

Example audit Current → Optimized
RungCurrentOptimized
1080p5.0 Mbps3.2 Mbps
720p2.5 Mbps1.6 Mbps
480p1.2 Mbps0.8 Mbps
VMAF 93.193.0
Monthly delivery $18,400$12,700
Potential saving $5,700 / month
Illustrative example
Measured, not guessed — your % we calculate the saving on your own content before you change anything
48h turnaround on the free audit report
$0 cost to find out what you're leaving on the table
How it works

Three steps. About five minutes from your team.

No migration, no new video platform, no touching your existing pipeline. The audit runs entirely on our end.

01

Send one video

A representative sample plus your current bitrate ladder — or just a link to a public HLS stream. We can pull the ladder ourselves.

02

We run the sweep

A resolution × quality grid, measured with VMAF, mapped to a convex hull — the same approach Netflix uses internally, run on your content.

Rate–distortion sample VMAF vs kbps
low bitrate high bitrate high Q low Q −34% at equal quality
Per-title optimal Your current ladder

Illustrative example

03

You get the numbers

The optimal ladder for your content, the bitrate gap in your current setup, and a dollar estimate based on your egress. Free, no obligation.

Who this is for

Built for teams that control their own pipeline.

This isn't for everyone — here's how to tell fast.

Built for teams that

  • deliver 50k+ hours of VOD per month
  • control FFmpeg / MediaConvert / their transcoding pipeline
  • run HLS or DASH
  • have different content types sharing similar ladders
  • spend meaningful money on CDN delivery

Probably not useful if

  • you're fully hosted on Mux / Vimeo OTT / Uscreen
  • you only have a few thousand viewing hours
  • you don't control encoding
  • you're live-only
Where it fits

Works with your existing stack.

We generate encoder settings. You keep your encoder. No new platform, no migration — StreamSlim slots in front of what you already run.

Encoder
FFmpeg · AWS MediaConvert
Output
HLS · DASH
Delivery
CDN-agnostic

Works regardless of whether you deliver through CloudFront, Fastly, Akamai or another CDN.

Sample finding

What a real audit rung looks like.

Illustrative example from a mid-size VOD ladder — your numbers depend on your content mix.

720p rung

Current bitrate2,500 kbps
Measured VMAF93.1
Optimal at same VMAF1,640 kbps
−34% bitrate, same quality

What that means at scale

One rung, one title. Multiply by every rung in your ladder and every title in active rotation, weighted by view count, and the gap compounds fast — most of it invisible until someone runs the numbers.

The free audit shows you this exact table for your own content, plus a dollar figure based on your actual egress bill.

After the audit

Turn the findings into your production ladder.

If there is a meaningful saving opportunity, StreamSlim can generate production-ready encoding settings for every new title.

Upload You
StreamSlim analysis Us
optimized ladder.json Output
Your FFmpeg / MediaConvert You
CDN You
Output
{
  "1080p": "2300 kbps",
  "720p": "1250 kbps",
  "480p": "650 kbps"
}
No player change. No CDN migration. No encoder replacement.
Data handling

Your video stays yours.

Why this exists

Built by someone who tunes this for a living.

TZ

Built by a video streaming engineer with 14+ years in backend systems, video pipelines and performance optimization. Per-title encoding isn't new — Netflix published the approach in 2015.

Most teams still don't use it, not because it's hard, but because nobody owns "go audit our encoding ladder" as a job. That's the gap this fills — without asking you to migrate anything.

Currently researching multimodal ML as a CS PhD student.

Find out what your ladder is costing you.

One video. One email. A report in 48 hours.

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