Okay, let's unpack this. We are diving into a toolkit today that, well, it seems to
have
really shifted how product engineers think about building software. And actually,
before we really
get started, we absolutely want to thank the supporter of this deep dive. Safe
Server. Safe
Server supports this exploration, handling the hosting side of things for software
like this,
and really aiding in your digital transformation. You can find more info at www.safeserver.de.
So our mission today is to explore PostHog. The goal is, well, pretty simple.
Explain what this
heavily starred open source platform actually is, why it makes this bold claim of
being an all-in-one
solution, and importantly, how it simplifies life for product teams. Especially,
you know,
if you're maybe just starting out in the world of product analytics. And you can
tell it's gaining
serious momentum. I mean, our sources today are straight from their main marketing
pages, sure,
but also their GitHub repo, and that shows, what, 29.8 thousand stars and 2,000 forks.
Wow. Yeah, that kind of traction doesn't just happen by accident. It really signals
they're
solving a, well, a pretty crucial pain point for developers. Absolutely. So the big
picture,
the core idea, is that PostHog aims to be the single source of truth for building
successful
products. It rolls together product analytics, session recording, feature flagging,
A-B testing,
all those separate tools that product engineers usually have to kind of cobble
together.
Right. And one open source bundle.
Exactly. Okay, so here's where it gets really interesting for me. You know, every
tech stack
eventually hits that problem of tool sprawl, right? One service for errors,
another for feature flags, maybe a third for user analytics. Why did PostHog decide
to consolidate
everything? What's the philosophy behind that? Well, what's fascinating here is it
feels like
a core philosophical shift. They actually define PostHog as a product OS, like an
operating system,
but for your product data. A product OS, okay. Yeah, and they recognize that if you're
a developer,
pretty much every decision you make, you know, should I fix this bug? Should we
launch that
feature? It all requires context. The key insight driving this whole thing seems to
be that developers
should operate from the full set of data, not just like a siloed slice of what's
happening inside
their app. Right, we've all felt that pain of disconnected data silos. You know, if
I'm trying
to debug something critical, I need the error log, sure, but I also need to see the
user's click path
and maybe know if they're a paying customer. It all matters. Exactly. So what does
this full set of data
actually mean in PostHog's world? Technically speaking, I mean. It means basically
breaking
down those walls between the different operational bits of the business and the
product engineering
side. So for instance, PostHog is designed to integrate data that happens outside
the application
itself, but still really defines that customer experience. Okay, like what? Things
like financial
data, maybe payments tracked in Stripe, or exceptions captured by a separate error
tracking tool,
or even support tickets logged in something like Zendesk or Salesforce. Okay, wait
a second. If I'm
a developer, that sounds fantastic for making decisions, definitely. But I have to
ask,
isn't consolidating financial data, customer service history, and every single user
click
into one potentially open source platform? Isn't that a pretty significant security
and
maintenance challenge? That's a lot of critical data in one basket. That's a really
important
question, yeah. And it speaks directly to their engineering approach, I think.
Their argument
seems to be that managing one highly secure integrated system is actually less
risky,
potentially, than constantly syncing sensitive data between, say, half a dozen
separate vendors.
Oh, okay. Each with its own authentication, its own compliance quirks. By
integrating these pipes,
they have this data pipelines and warehouse component we can talk about. They
create a kind
of unified identity across the data. I see. Which lets you do things like say, okay,
show me all
users who clicked feature X, had error Y last Tuesday, and submitted a high
priority support
ticket last week. That kind of visibility, well, it allows for much more informed
decisions.
It sounds like the real value isn't just having the tools in one place, but it's
that single,
unfragmented view of the entire user journey from payment to frustration to
actually using a feature.
Precisely. It shifts the focus from just reporting abstract numbers to directly
informing
actual product strategy changes. So we get the philosophy,
single source of truth, unified view. Since it is an all-in-one platform,
let's look at the toolkit itself. But maybe instead of just defining basic terms,
because our listeners probably know what A-B testing is, let's focus on the synergy.
How do these tools actually work together in this unified environment?
That's the critical difference, right? Because in a traditional stack, you might
run an A-B test in,
say, tool A, but then you've got to manually pipe those results over to tool B,
your main analytics
platform, just to see the full impact. Yeah, that's always a pain.
Here, the idea is the tools share the same underlying event data immediately. So
take
product analytics combined with feature flags. Let's say you set up a feature flag.
Maybe you're
rolling out a new checkout button to just 10% of your users. Since the flag is
native to the
platform, that 10% cohort is just automatically tracked by the analytics engine. No
extra setup.
Then, maybe the next day, you decide, hey, let's turn this into a proper experiment,
an A-B test.
You don't have to redefine the cohort. You don't have to re-instrument any code.
The data is already
flowing right into the experiments tool. That allows for immediate statistical
analysis on, say,
conversion rate changes. Okay, that dramatically cuts down on the
operational lag and the engineering overhead, I imagine. What about the tools
focused more on
like product stability, errors and replays? Yeah, look at session replays and error
tracking.
Session replays, for those who haven't used them, are like watching a screen
recording of a real
user session. Right, super useful.
In the traditional model, if a user reports some weird issue, you might see the
error log in tool
A, but you have zero visual context. You're just guessing. With post hog, the idea
is if an error
pops up in the error tracking section, the developer can immediately click and jump
straight to the
session replay that's tied to that exact moment the error occurred. Oh, wow. Okay,
so instead of
trying to reproduce a bug based on just a stack trace, which can be impossible
sometimes, I can
actually watch the user encounter the bug live, see exactly what steps they took,
maybe check the
corresponding network logs right there alongside the replay. That sounds like, well,
like a huge
improvement in debugging time. It absolutely can be. Yeah. And kind of feeding into
all of this
is the data warehouse pipelines capability. This is sort of the heavy lifting
engine behind the
scenes. It's what ingests that external data, we talked about Stripe, HubSpot,
whatever,
and lets you run custom transformations on it. Okay. And critically, it then lets
you send that
enriched unified data stream out to, I think they say 25 plus other downstream
tools. So it ensures
post hoc remains flexible, even if your stack evolves later. That makes sense. Keeps
it from
being too much of a closed box. Finally, we should probably talk about LLM
analytics. You know,
AI powered applications are becoming huge, but they bring this whole new layer of
operational
complexity. How does post hoc handle that? This is where they really seem to be
looking ahead.
For applications using large language models, they're capturing specialized metrics
that are
pretty vital for operations. Things like API call traces, token generation counts,
latency for the model responses, and even the actual cost per query. Oh,
interesting. Tracking
the cost directly. Yeah. It's an essential, quite specialized layer for measuring
the efficiency
and performance of these AI driven products. And it connects that raw engineering
cost
directly to the user behavior metrics you're seeing in the main analytics dashboard.
It feels like a key feature that frankly, a lot of other consolidated platforms are
probably only
just starting to think about. Yeah, that definitely feels ahead of the curve. Okay,
that is a seriously
ambitious suite of tools all bundled together. It almost sounds like the kind of
infrastructure
only, you know, major enterprises could typically afford or manage. So what does
this all mean for
someone just wanting to get started? Is this actually accessible to small teams or
is there
a steep barrier to entry? This is where their open source roots and their pricing
philosophy really
come into play and make a difference. They basically offer two main paths. The
recommended
option is post hog cloud. Right, the hosted version. Exactly. It's engineered for
speed,
reliability, basically zero maintenance for you. And crucially, they offer an
extremely generous
free tier. How generous are we talking here? Like for a startup or maybe just an
individual
developer exploring the platform? We're talking generous to the point where they
state that 98%
of their customers currently use it entirely for free. Wow, 98%. Yep. Every month
you get,
let's see, 1 million events for product analytics, 5,000 session recordings, 1
million feature flag
requests, 100,000 exceptions tracked for errors, and 1,500 survey responses. All
free every month.
Okay. That is a huge operational allowance for a startup or a small team. That's
pretty impressive.
Pricing only kicks in after you hit those limits. Correct. Usage based after the
free tier. Got it.
And what about the pure open source experience? You know, for teams who really
insist on absolute
data sovereignty and want to self-host everything? That option is definitely there.
The ability to
deploy a kind of hobby instance with just a single line of code using Docker on
Linux exists. However,
and they're quite transparent about this too, the source is generally advised that
this setup is
really only recommended up to maybe about 100,000 events per month. Ah, okay. So it
has limitations
at scale. Yeah, if you scale beyond that, they strongly suggest migrating to their
cloud
infrastructure simply because managing the underlying data warehouse click house,
I believe for massive scale, is a really non-trivial engineering task. And well,
they handle that best. Makes sense. Now let's talk about their culture for a second
because this is
often overlooked, but I think it's pretty central to their whole appeal, especially
to developers.
They don't just open source their code. Right. They seem to open source their
entire company.
Wait, what do you mean? Like their internal processes? What does that actually
involve?
Well, it's pretty extreme transparency. They open source their entire company handbook.
It details their strategy, how they make decisions, their ways of working, internal
processes.
Seriously, their strategy docs are public. Yeah. And look, it's probably not just a
gimmick. It
seems to build this profound trust with the developer community who aren't just
users,
but often contributors too. It's like a powerful statement. Look, if we're this
transparent about
how we operate internally, you could probably trust us to handle your mission
critical data
responsibly. That's bold. And I guess that transparency extends directly to their
pricing
philosophy too, which sounds like a pretty sharp contrast to standard sauce
practices.
Absolutely. Their pricing is completely usage based, like we said, and they have an
explicit
no sales call approach. You don't have to talk to anyone to get started or figure
out costs.
Nice. You can calculate your exact cost based on totally transparent per unit
pricing, like that
zero, zero, zero, zero, zero, five per event number after the free tier limit.
There are no hidden
tiers, no complex enterprise negotiations just to figure out what you'll owe. They
explicitly aim
to be the cheapest option at scale. And this radically transparent model is kind of
how they
try to prove it. Okay. That's refreshing. Finally, let's just circle back quickly
to the future.
Beyond the LLM analytics tool they already offer, what are their teams apparently
focused on in
terms of integrating AI within the platform itself? Well, the roadmap seems to
suggest they're
shifting the goal beyond just measurement towards more active automation. They're
apparently working
on AI features to automate some of the more monotonous analysis tasks, maybe
summarize
complex user journey information automatically. Okay. And perhaps more ambitiously
integrating AI
into the actual development workflow. Like having the platform eventually suggest
or maybe even make
code changes to fix bugs, it identifies automatically. Yeah, definitely an
evolution from just being a
data collector to potentially be more like a product co-pilot. That's quite a
vision. So I
guess the key takeaway here is that this isn't really just one tool or even just a
collection
of tools. It's more like a unified, very open and incredibly transparent ecosystem
designed to give
product engineers that single source of truth, hopefully eliminating some of the
guesswork and
maybe even the security risks that come from having all these disparate data
sources. Indeed.
And maybe for a final provocative thought, the sources mentioned their unique
culture,
including this one time they apparently sent customers a floppy disk that turned
out to be
a rickroll. Okay. Which is fun, sure. But underlying that is that transparency we
talked about sharing
their strategy, their sales manual, their inner workings. That's what feels really
foundational.
And the question for you, the listener might be, if a company is that open about
how they operate
internally, how much does that influence your trust when you're deciding whether to
build your
own mission critical product on their platform? That openness, maybe it isn't just
a feature,
maybe it's a kind of security and confidence guarantee in itself. That's a great
question
to leave you with. Thank you for joining us for this deep dive into PostHawk. And
once again,
a big thank you to SafeServer for the support of this deep dive that helps you with
digital
transformation and hosting of software. More information can be found at www.bigsafeserver.de.
We'll see you next time.