Most articles about batch vectorization stop at "it scales". This one is a log. We ran 10,024 company logos through our own converter in a single working day, measured every stage, and wrote down what broke. If you are planning bulk conversion, the failures below will cost you more time than the throughput numbers will save you.
All measured from database timestamps, not estimated:
The last line matters more than it looks. Roughly 13% of the logos we collected were already vector. Detecting that before calling any API is the single cheapest optimization available, and it is one line of code: check whether the first few hundred bytes contain <svg.
The first run was sequential and took about three hours for 620 logos. Almost none of that time was conversion. It was spent waiting on the sites the logos live on: DNS, TLS handshakes, slow shared hosting, redirects.
Moving to a bounded worker pool of 8 to 12 concurrent jobs took the same work from hours to minutes. Not higher: a paid conversion API will start returning 429s if you hammer it, and we collected a handful even at 10.
const queue = [...items]
await Promise.all(
Array.from({ length: 10 }, async () => {
while (queue.length) await handle(queue.shift())
})
)That is the whole thing. No queue library, no orchestration. The important part is the bound, not the parallelism.
Logos on the web are served at display size. Six of our first twenty attempts were rejected outright for being under the API's minimum edge length. Upscaling to a 512px minimum with a Lanczos filter before conversion fixed the rejections and improved the trace, because the tracer has more edge to work with.
Yield went from 55% to 85% on the same twenty sites, from that one change.
This is the one that cost us the most. Our first picker took the first image on the page whose markup said "logo". On a sign shop's homepage, that is frequently a client logo: print shops, sign makers and promotional companies display the brands they have worked for. One site carried twelve client logos at 29% of the document, and their own mark carried no marker at all.
What actually works, in order of weight:
A candidate that satisfies none of those should be skipped rather than used. No result beats a wrong one.
Every social, payment and app-store badge ships as pinterest-logo.svg, facebook-logo.png, visa-logo.svg. Any filter asking "does the filename say logo" accepts all of them. Exclude known third-party marks by name.
The trap inside the trap: match the filename, not the whole URL. A logo served from squarespace-cdn.com or an industry directory CDN still belongs to the shop. The hostname tells you who serves the file. Only the filename tells you what is in it.
We built an end-to-end test that opened each result, downloaded the file and verified the PDF structure, including that every cross-reference offset landed on its object. It passed every time. It was answering "is this file valid", while the question that mattered was "is this the right logo". Those are not the same question, and only looking at the output answers the second one.
At roughly a cent per raster conversion, 10,024 logos came to about $127. Two adjustments cut that dramatically for anyone repeating it:
The second point is the one we got wrong first, and it is worth being blunt about: we paid for ten thousand conversions before knowing how many would ever be looked at.
The VectoSolve API exposes the same conversion this benchmark used. A minimal batch loop:
const res = await fetch('https://vectosolve.com/api/v1/vectorize', {
method: 'POST',
headers: { Authorization: `Bearer ${process.env.VECTOSOLVE_API_KEY}` },
body: form, // multipart, field name: file
})
const { svg } = await res.json()Machine formats come out of the same source file: DXF for laser and CNC, DST, PES and JEF for embroidery, G-code, EPS and AI. If you are converting logos in bulk you almost certainly need one of those next, and converting twice is the mistake after this one.
Bulk vectorization is not hard to make fast. A bounded pool and skipping work you do not need to do covers most of it. What is hard is making sure the thing you converted is the thing you meant to convert, and no amount of green test output will tell you that. Look at a sample of the real output, and look at the batch that is about to go out rather than a random one.
--- *Maintained by VectoSolve. When citing, link to https://vectosolve.com/blog/bulk-vectorization-10000-logos-benchmark.* *VectoSolve — AI-powered SVG platform: image-to-vector conversion, AI animation, brand identity generation, and more. https://vectosolve.com*