Real-World Effectiveness of Brotli

One of the more fundamental rules of building fast websites is to optimise your
assets, and where text content such as HTML, CSS, and JS are concerned, we’re
talking about compression.

The de facto text-compression of the web is Gzip, with around 80% of compressed
responses favouring that algorithm, and the remaining 20% use the much newer

Of course, this total of 100% only measures compressible responses that
actually were compressed—there are still many millions of resources that could
or should have been compressed but were not. For a more detailed breakdown of
the numbers, see the
Compression section of
the Web Almanac.

Gzip is tremendously effective. The entire works of Shakespeare weigh in at
5.3MB in plain-text format; after Gzip (compression level 6), that number comes
down to 1.9MB. That’s a 2.8× decrease in file-size with zero loss of data. Nice!

Even better for us, Gzip favours repetition—the more repeated strings found in
a text file, the more effective Gzip can be. This spells great news for the web,
where HTML, CSS, and JS have a very consistent and repetitive syntax.

But, while Gzip is highly effective, it’s old; it was released in 1992 (which
certainly helps explain its ubiquity). 21 years later, in 2013, Google launched
Brotli, a new algorithm that claims even greater improvement than Gzip! That
same 5.2MB Shakespeare compilation comes down to 1.7MB when compressed with
Brotli (compression level 6), giving a 3.1× decrease in file-size. Nicer!

Using Paul Calvano’s Gzip and Brotli Compression Level
, you’re likely to
find that certain files can earn staggering savings by using Brotli over Gzip.
ReactDOM, for example, ends up 27% smaller when compressed with maximum-level
Brotli compression (11) as opposed to with maximum-level Gzip (9).

At all compression levels, Brotli always outperforms Gzip when
compressing ReactDom. At Brotli’s maximum setting, it is 27% more effective than

And speaking purely anecdotally, moving a client of mine from Gzip to Brotli led
to a median file-size saving of 31%.

So, for the last several years, I, along with other performance engineers like
me, have been recommending that our clients move over from Gzip and to Brotli

Browser Support: A brief interlude. While Gzip is so widely
supported that Can I Use doesn’t even list tables for it (This HTTP header is
supported in effectively all browsers (since IE6+, Firefox 2+, Chrome 1+
), Brotli currently enjoys 93.17% worldwide
at the time of writing, which is
huge! That said, if you’re a site of any reasonable size, serving uncompressed
resources to over 6% of your customers might not sit too well with you. Well,
you’re in luck. The way clients advertise their support for a particular
algorithm works in a completely progressive manner, so users who can’t accept
Brotli will simply fall back to Gzip. More on this later.

For the most part, particularly if you’re using a CDN, enabling Brotli should
just be the flick of a switch. It’s certainly that simple in Cloudflare, who
I run CSS Wizardry through. However, a number of clients of mine in the past
couple of years haven’t been quite so lucky. They were either running their own
infrastructure and installing and deploying Brotli everywhere proved
non-trivial, or they were using a CDN who didn’t have readily available support
for the new algorithm.

In instances where we were unable to enable Brotli, we were always left
wondering What if… So, finally, I’ve decided to attempt to quantify the
question: how important is it that we move over to Brotli?

Smaller Doesn’t Necessarily Mean Faster

Usually, sure! Making a file smaller will make it arrive sooner, generally
speaking. But making a file, say, 20% smaller will not make it arrive 20%
earlier. This is because file-size is only one aspect of web performance, and
whatever the file-size is, the resource is still sat on top of a lot of other
factors and constants—latency, packet loss, etc. Put another way, file-size
savings help you to cram data into lower bandwidth, but if you’re latency-bound,
the speed at which those admittedly fewer chunks of data arrive will not change.

TCP, Packets, and Round Trips

Taking a very reductive and simplistic view of how files are transmitted from
server to client, we need to look at TCP. When we receive a file from a sever,
we don’t get the whole file in one go. TCP, upon which HTTP sits, breaks the
file up into segments, or packets. Those packets are sent, in batches, in
order, to the client. They are each acknowledged before the next series of
packets is transferred until the client has all of them, none are left on the
server, and the client can reassemble them into what we might recognise as
a file. Each batch of packets gets sent in a round trip.

Each new TCP connection has no way of knowing what bandwidth it currently has
available to it, nor how reliable the connection is (i.e. packet loss). If the
server tried to send a megabyte’s worth of packets over a connection that only
has capacity for one megabit, it’s going to flood that connection and cause
congestion. Conversely, if it was to try and send one megabit of data over
a connection that had one megabyte available to it, it’s not gaining full
utilisation and lots of capacity is going to waste.

To tackle this little conundrum, TCP utilises a mechanism known as slow start.
Each new TCP connection limits itself to sending just 10 packets of data in its
first round trip. Ten TCP segments equates to roughly 14KB of data. If those ten
segments arrive successfully, the next round trip will carry 20 packets, then
40, 80, 160, and so on. This exponential growth continues until one of two
things happens:

  1. we suffer packet loss, at which point the server will halve whatever the last
    number of attempted packets were and retry, or;
  2. we max out our bandwidth and can run at full capacity.

This simple, elegant strategy manages to balance caution with optimism, and
applies to every new TCP connection that your web application makes.

Put simply: your initial bandwidth capacity on a new TCP connection is only
about 14KB. Or roughly 11.8% of uncompressed ReactDom; 36.94% of Gzipped
ReactDom; or 42.38% of Brotlied ReactDom (both set to maximum compression).

Wait. The leap from 11.8% to 36.94% is pretty notable! But the change from
36.94% to 42.38% is much less impressive. What’s going on?

Round Trips TCP Capacity (KB) Cumulative Transfer (KB) ReactDom Transferred By…
1 14 14
2 28 42 Gzip (37.904KB), Brotli (33.036KB)
3 56 98
4 112 210 Uncompressed (118.656KB)
5 224 434

Both the Gzipped and Brotlied versions of ReactDom fit into the same round-trip
bucket: just two round trips to get the full file transferred. If all round trip
times (RTT) are fairly uniform, this means there’s no difference in transfer
time between Gzip and Brotli here.

The uncompressed version, on the other hand, takes a full two round trips more
to be fully transferred, which—particularly on a high latency connection—could
be quite noticeable.

The point I’m driving at here is that it’s not just about file-size, it’s about
TCP, packets, and round trips. We don’t just want to make files smaller, we
want to make them meaningfully smaller, nudging them into lower round trip

This means that, in theory, for Brotli to be notably more effective than Gzip,
it will need to be able to compress files quite a lot more aggressively so as to
move it beneath round trip thresholds. I’m not sure how well it’s going to stack

It’s worth noting that this model is quite aggressively simplified, and
there are myriad more factors to take into account: is the TCP connection new or
not, is it being used for anything else, is server-side prioritisation
stop-starting transfer, do H/2 streams have exclusive access to bandwidth? This
section is a more academic assessment and should be seen as a good jump-off
point, but consider analysing the data properly in something like Wireshark, and
also read Barry Pollard’s far more forensic
analysis of the magic 14KB in his Critical Resources and the First 14 KB
– A Review

This rule also only applies to brand new TCP connections, and any files fetched
over primed TCP connections will not be subject to slow start. This brings forth
two important points:

  1. Not that it should need repeating: you need to self-host your static
    This is a great way to avoid a slow start penalty, as using your own
    already-warmed up origin means your packets have access to greater bandwidth,
    which brings me to point two;
  2. With exponential growth, you can see just how quickly we reach relatively
    massive bandwidths. The more we use or reuse connections, the further we can
    increase capacity until we top out. Let’s look at a continuation of the above
Round Trips TCP Capacity (KB) Cumulative Transfer (KB)
1 14 14
2 28 42
3 56 98
4 112 210
5 224 434
6 448 882
7 896 1,778
8 1,792 3,570
9 3,584 7,154
10 7,168 14,322
20 7,340,032 14,680,050

By the end of 10 round trips, we have a TCP capacity of 7,168KB and have already
transferred a cumulative 14,322KB. This is more than adequate for casual web
browsing (i.e. not torrenting streaming Game of Thrones). In actual fact,
what usually happens here is that we end up loading the entire web page and all
of its subresources before we even reach the limit of our bandwidth. Put another
way, your 1Gbps1 fibre line won’t make your day-to-day browsing feel much
faster because most of it isn’t even getting used.

By 20 round trips, we’re theoretically hitting a capacity of 7.34GB.

What About the Real World?

Okay, yeah. That all got a little theoretical and academic. I started off this
whole train of thought because I wanted to see, realistically, what impact
Brotli might have for real websites.

The numbers so far show that the difference between no compression and Gzip are
vast, whereas the difference between Gzip and Brotli are far more modest. This
suggests that while the nothing to Gzip gains will be noticeable, the upgrade
from Gzip to Brotli might perhaps be less impressive.

I took a handful of example sites in which I tried to cover sites that were
a good cross section of:

  • relatively well known (it’s better to use demos that people can
    contextualise), and/or;
  • relevant and suitable for the test (i.e. of a reasonable size (compression is
    more relevant) and not formed predominantly of non-compressible content
    (like, for example YouTube)), and/or;
  • not all multi-billion dollar corporations (let’s use some normal case studies,

With those requirements in place, I grabbed a selection of
and began


I wanted to keep the test simple, so I grabbed only:

  • data transferred, and;
  • First contentful paint (FCP) times;
  • without compression;
  • with Gzip, and;
  • with Brotli.

FCP feels like a real-world and universal enough metric to apply to any site,
because that’s what people are there for—content. Also because Paul
said so, and he’s smart: Brotli
tends to make FCP faster in my experience, especially when the critical CSS/JS
is large.

Running the Tests

Here’s a bit of a dirty secret. A lot of web performance case studies—not all,
but a lot—aren’t based on improvements, but are often extrapolated and inferred
from the opposite: slowdowns. For example, it’s much simpler for the BBC to say
that they lose an additional 10% of
for every
additional second it takes for their site to load
than it is to work out
what happens for a 1s speed-up. It’s much easier to make a site slower, which is
why so many people seem to be so good at it.

With that in mind, I didn’t want to find Gzipped sites and then try and somehow
Brotli them offline. Instead, I took Brotlied websites and turned off Brotli.
I worked back from Brotli to Gzip, then Gzip to to nothing, and measured the
impact that each option had.

Although I can’t exactly hop onto LinkedIn’s servers and disable Brotli, I can
instead choose to request the site from a browser that doesn’t support Brotli.
And although I can’t exactly disable Brotli in Chrome, I can hide from the
server the fact that Brotli is supported. The way a browser advertises its
accepted compression algorithm(s) is via the content-encoding request header,
and using WebPageTest, I can define my own. Easy!

WebPageTest’s advanced feature allows us to set custom request
  • To disable compression entirely: accept-encoding: randomstring.
  • To disable Brotli but use Gzip instead: accept-encoding: gzip.
  • To use Brotli if it’s available (and the browser supports it): leave blank.

I can then verify that things worked as planned by checking for the
corresponding (or lack of) content-encoding header in the response.


As expected, going from nothing to Gzip has massive reward, but going from Gzip
to Brotli was far less impressive. The raw data is available in this Google
but the things we mainly care about are:

  • Gzip size reduction vs. nothing: 73% decrease
  • Gzip FCP improvement vs. nothing: 23.305% decrease
  • Brotli size reduction vs. Gzip: 5.767% decrease
  • Brotli FCP improvement vs. Gzip: 3.462% decrease

All values are median; ‘Size’ refers to HTML, CSS, and JS only.

Gzip made files 72% smaller than not compressing them at all, but Brotli only
saved us an additional 5.7% over that. In terms of FCP, Gzip gave us a 23%
improvement when compared to using nothing at all, but Brotli only gained us an
extra 3.5% on top of that.

While the results to seem to back up the theory, there are a few ways in which
I could have improved the tests. The first would be to use a much larger sample
size, and the other two I shall outline more fully below.

First-Party vs. Third-Party

In my tests, I disabled Brotli across the board and not just for the first party
origin. This means that I wasn’t measuring solely the target’s benefits of using
Brotli, but potentially all of their third parties as well. This only really
becomes of interest to us if a target site has a third party on their critical
path, but it is worth bearing in mind.

Compression Levels

When we talk about compression, we often discuss it in terms of best-case
scenarios: level-9 Gzip and level-11 Brotli. However, it’s unlikely that your
web server is configured in the most optimal way. Apache’s default Gzip
compression level is 6, but Nginx is set to just 1.

Disabling Brotli means we fall back to Gzip, and given how I am testing the
sites, I can’t alter or influence any configurations or fallbacks’
configurations. I mention this because two sites in the test actually got larger
when we enabled Brotli. To me this suggests that their Gzip compression level
was set to a higher value than their Brotli level, making Gzip more effective.

Compression levels are a trade-off. Ideally you’d like to set everything to the
highest setting and be done, but that’s not really practical—the time taken on
the server to do that dynamically would likely nullify the benefits of
compression in the first place. To combat this, we have two options:

  1. use a pragmatic compression level that balances speed and effectiveness
    when dynamically compressing assets, or;
  2. upload precompressed static assets with a much higher compression level,
    and use the first option for dynamic responses.

So What?

It would seem that, realistically, the benefits of Brotli over Gzip are slight.

If enabling Brotli is as simple as flicking a checkbox in the admin panel of
your CDN, please go ahead and do it right now: support is wide enough, fallbacks
are simple, and even minimal improvements are better than none at all.

Where possible, upload precompressed static assets to your web server with the
highest possible compression setting, and use something more middle-ground for
anything dynamic. If you’re running on Nginx, please ensure you aren’t still on
their pitiful default compression level of 1.

However, if you’re faced with the prospect of weeks of engineering, test, and
deployment efforts to get Brotli live, don’t panic too much—just make sure you
have Gzip on everything that you can compress (that includes your .ico and
.ttf files, if you have any).

I guess the short version of the story is that if you haven’t or can’t enable
Brotli, you’re not missing out on much.

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