We last spoke in December 2021, which is
almost three years ago.
And the title of that conversation was
Keep on-call simple
And you check it out at
shipit.show/36
So I'm wondering what is new?
Actually, everything new
except the word "Keep".
Because "Keep" is the name of a new
startup we are speaking today about.
And it's no more on call.
It's still trying to make
it simple, keep it simple.
And actually, if like last time we talked
about Grafana Labs, how Grafana Labs
acquired the startup I built together
with my co-founder,
Ildar, back in the days.
And this startup was dedicated to
help on call engineers
set up on call rotations,
set up on call escalations,
And Keep, the new startup
we're talking about today.
It's not focusing on who to notify,
and how to notify,
it's focusing on what.
So we also have Tal joining us today.
I tell Matvey all the time that I think
he's super humble because he managed to
completely change the
whole market of IRM,
Incident Response Management tools.
I think since they built Amixr, which
was acquired by Grafana Labs and today is
known as Grafana OnCall.
There was a huge shift in that market.
We saw tons of new
players coming into that field.
And I also think that it's somewhat the
grounds for what Keep is building to be.
My last job before I became
a startup founder was
an engineer on call.
So I have a lot of empathy to everything
happening during incidents,
before incidents, after incidents,
and especially with people
who are handling incidents.
And when I think about what
what I want to work on
is the first thing
which comes to my mind.
And I'm lucky I met Tal and
our third co-founder, Shahar, who
we share this experience with.
It's safe to say that I guess
every engineer or someone with
engineering background shares the same,
at least perspective about this world.
Before I met Matvey, what brought me
into this world was the exact same thing
as Matvey just mentioned.
Being an engineer,
being an engineering manager,
facing incidents,
facing alert fatigue,
muting different Slack channels,
all of that.
So if you were to take us through an
incident that had all the
things about it were wrong.
How it started, how it happened, how it
unfolded, how it eventually
ended and the follow through.
Do you have such an example in mind, Tal?
I have a few examples just
from the last couple of months.
CrowdStrike is a very large,
I can say antivirus company.
It has agents installed on endpoints in
different companies.
They released some update to their
software that caused
basically all the computers to crash.
Blue screen, if that
says something to somebody.
And I think what's fascinating about this
incident is that it actually caused
hundreds of millions in dollars in loss
for so many companies.
So that's one thing I have
in mind that was just recent.
And the other thing I have in mind is
there was some incident with a major
cloud provider, Google cloud provider,
where they basically made some mistake.
I think it's not even public what was the
main reason for that, but they made some
mistake and they erased a complete
environment for some very
large insurance company in the US,
which also caused like, I think three
days of downtime to that company.
And that was a big thing as well.
And it's the type of things that you as
an engineer, when you think about
the one guideline you have is
in today's cloud environment is that
major cloud providers, they can fail.
And you learn that they actually can.
So I guess those are my two examples.
Everyone makes mistakes, right?
And systems that are able to handle mistakes
they're hard to design
they're hard to maintain
And the more complexity is added
the more difficult this becomes, for sure.
So CrowdStrike, I think that's a great
example because it, first of all, it was
by far, I think, like the biggest outage
in the history of all
information technology.
It was recent.
It was July.
It was just like, as you mentioned, like
some number of months ago, it wasn't even
a year ago that this happened.
It affected everyone.
Government, hospitals,
this was like really serious.
And even though machines crashed that,
you know, like they shouldn't have
crashed, they couldn't restart.
I think that was the big one.
And a fix was rolled
out, you know, within hours.
Like the fix was out, but the fix
couldn't be applied fast enough.
Everyone was in damage control.
It was messy, really messy.
I was looking on Wikipedia.
I was looking this up because I knew that
we would talk about this.
And apparently, the financial damage was
closer to 10 billion dollars worldwide.
And it's an estimate by the financial
institutions, but that's huge.
This reminds me of something that
Matvey mentioned when he first spoke.
He mentioned about the black swans
and the black swan events.
And I think this
definitely classes as one.
So what are your thoughts there, Matvey?
By the way, I just want to throw a fun
fact, but I think it's definitely
a black swan for CrowdStrike.
Because I'm looking at their stock price
from like six months ago.
And it used to be like
almost 400 US dollars per stock.
And it's like less than 300 right now.
So I think for them, it was a big thing.
Other fun fact about CrowdStrike and it's
a little bit closer to us.
It's like a small story.
Since our startup is relatively young
we are selling to
enterprises, large enterprises.
Practically all of them.
I think all of them were affected.
And one of our customersZZZ,
prospects at the time, like customers now,
they wanted to onboard to Keep actually
to mitigate such things faster,
such events faster, such black swans.
Going back to your
question about black swans.
most black swan for me was
major outage
of one of cloud providers
we used at Grafana.
And I can't disclose a lot about it, but it
was a huge surprise for me that,
you know, when you look at
cloud providers' uptime pages
like status pages
you see a lot of items like
SQL service
like hosted SQL, something else, DNS.
And you assume that if they will go down,
like one of them will go down or maybe
two of them will go down.
It's hard for you to imagine that
actually the whole screen could be red.
I think that's the market we operate in.
Like there is a Murphy's Law that says:
everything that can go wrong, will go wrong.
You know, you try to build walls behind
large scale systems, but eventually,
you know, people make mistakes.
Software is not complete
There will always be something happening.
It can be like from electricity going down
in some, I don't know, a server factory
to a line of code that somebody
changes and breaks everything.
Things go wrong
That's one true phrase
you can always rely on.
Yeah, things are not going to work.
What are you going to about it?
I like that take.
So if we think about CrowdStrike and if
we think about having Keep.
So let's assume that the customer
that you mentioned
that is now customer
if they had Keep at the time
before CrowdStrike happened
how would that have changed
how things unfolded for them?
Customers of that scale and I'm speaking
about companies with probably hundreds
and thousands of physical sites worldwide
and dozens of thousands of people working
in those enterprises.
They have multiple solutions to keep
track of what's going on in their
infrastructure, monitoring systems.
Actually, the phrase which we heard on
one of customers calls is that we usually
ask like which monitoring system you use
and they answered as like, actually,
anything you could you could think of.
We have everything in
somewhere in our org.
I think you have some counter for the
number of monitoring
tools out there, right?
I tried to count, like I found 370 plus,
and I kept finding new ones.
So those large enterprises,
they monitor a lot of things,
and all those monitoring systems, they
generate signals, alerts, alarms,
how some people call them.
And the problem is that if they collect
all those signals in one place,
they can't actually manage this scale.
So we talk with people who receive like
thousands and thousands an hour.
There is one particular customer who is
dealing with 70,000 alerts a day.
And the problem they are facing is a
problem of finding the
needle in the haystack.
Like this is an actual incident.
This is not noise.
And the rest is noise.
This is a problem.
We at Keep, we don't solve the problem of
who to notify, and how to notify,
and what to do
actually during the incident.
This is IRM space.
This is the space for such products as
like incident.io
Grafana on call, PagerDuty.
But we're building the best software
which will help you to look at it,
look at this stream of events and say
like, those five events,
they happened in the past.
And most probably,
this is a real incident.
I think people sometimes underestimate
like, you know, with
the CrowdStrike event,
what you as end user get is the report
that CrowdStrike publishes about the
event that happened.
But people underestimate the chaos that
companies went through
to try to understand what happens before
CrowdStrike even realized
that they released
something that breaks everything.
So just think about it as a point in time
where all the screens start to become red
and there are people sitting in, usually
it's like network
operation centers or, you know,
individuals monitoring their systems and
everything becomes red.
And that's a point in time
where you are just in chaos.
And now you need to start figuring out...
Okay, what's happening?
Where does it start?
What's the single point in time where
everything started to collapse?
Why do you think that companies end up in
a situation where they
have a lot of alerts?
Like, how do you end up in that place?
First of all, how do you end up with
having so many monitoring solutions?
I understand two or three, right?
Because you want the monitoring to check
your other monitoring.
So you would have a
few, but more than that?
I have a lot of answers for that.
But one of the things I want to check is
like the number of, I think
Netflix is famous for that,
but the number of microservices they
have, I think it's more
than a thousand microservices
that they have.
They have this famous graph of Netflix
microservices and like
the connections between them
and the different frameworks they're
running, the different
database systems they're using,
the different queue
systems they're using.
It's a modern company,
but it started a while ago.
So just think about, at least that's a
good example from my perspective.
Just think about a company that starts,
it has some infrastructure,
some complexity of stuff.
People join the company.
It's growing.
It has a major growth such as Netflix.
More complex infrastructure.
Team members who joined from different
companies, they like to work with the
tools that they're used to.
New technology comes in.
It has its own monitoring tool, or it
adopts 10 different monitoring tools.
A new guy comes in, he brings his own
methodology of how you do things.
And as the company grows,
a few years later, you find
yourself with people's legacy,
like the things they brought in, the
software they wrote, the
tools that they were using,
eventually that all
sticks with the company.
So you get to a point in larger
enterprises where you just
see everything of everything.
Technical debt is
something that you always aggregate,
and you try to find a balance to,
you have your company, it has
its goals, it's a business.
Eventually you need to
make the business grow.
It's very hard to wrap up or accumulate
the return on
investment you have when you
need to fix those kinds of stuff.
The things that doesn't necessarily push
the business forward,
but actually they have hidden costs.
So I think that's how you find, at least
from my perspective, companies who use
tens of different tools.
Okay.
And does Keep, in this context, mean
keep everything you have,
we'll help you make sense of it?
You mean the name?
Yes.
No, the name actually has a cuter story.
Actually, Shahar, our co-founder, that is
not here, we met each other 13 years ago.
We served together in the
Israeli Defense Forces
and both of us,
we used to play Age of Empires,
and we just took something like a keep is
something that you
have in Age of Empires,
a building that you can build there, and
it was just a random story
that we chose something from
that game.
Oh wow, we can see it.
So that's a keep.
It's like a tower.
Okay.
Is that for archers?
Is that where archers would be?
I think so.
Yeah, I think so.
Okay.
I remember playing the game too.
That was a while back, but you're right,
that was a fun game.
Can you still play today, by the way?
I think you can already
play that in the browser.
So some people migrated the whole game
into the browser, which is kind of cool,
because you don't
have to install anything.
You remember there used to be CDs, and
you needed to have
license keys and everything
like that.
Now you can play Age of Empires in the
web browser, which is quite cool, but
yeah, you can play it.
What is the significance to the like day to day?
So you have this keep.
What is it protecting?
Or what is it watching over?
It's like a watchtower.
Is that what it's supposed to be?
It's protecting people
who deal with alerts.
One of the examples, we had a call with a
small team of four who
managed alerts in their like
internally built system.
And those people practically looking at
alerts multiple hours a day,
staring at those screens and trying to
see like in a matrix movie, like, is
something happening?
This is not a fun job, I guess.
We want those people to be busy with
something maybe more interesting
to apply some best practices to fix some
core reasons or build
something for future.
So I think that people can try and
imagine what this looks
like when it's all set up
and how it works.
Before we go into the demo part, which
I'm very keen to see how
this works out in practice.
But I'm wondering, why would someone pick
this, which for example,
maybe they don't have that
much volume?
What does that mean?
Even if you have maybe tens or hundreds,
you don't need to have
thousands for this to be useful.
Why would someone pick this?
When we started the way of Keep, what we
used to do to get some
feedback from users was to post
stuff on Hacker News.
You usually get the truth
right to your face over there.
And one of the posts that we wrote that
worked very well for us
actually, and brought in very
meaningful feedback was we posted
something that says GitHub actions for
your monitoring tools.
So one of the main capabilities that you
have within Keep is to
create workflows, automations
that are based, like the triggers are
based on alerts that you get on the
alarms, on the events
that go inside Keep.
I think that was super
interesting for a lot of companies.
We actually saw a lot of other monitoring
tools and even
companies from the IRM space
actually implementing
workflows pretty much the same day.
I'm not saying anybody copied or anything
like that, but we
actually saw them posting
like a few days later that they also
support this type of thing.
And I find a lot of the smaller startups,
smaller companies find a
lot of value with just writing
automation.
So before they become this huge
monitoring beast with this sea of alerts
and sea of signals that
they get, they can actually leverage the
automation capabilities
that we have within Keep.
So they can do from the smallest thing of
creating a ticket
when they get some alert
so they can handle it
and fix it for later.
Or if they want to try to see if they can
automatically fix the
issue, maybe it's just
a matter of restarting
something in their Kubernetes cluster.
That's everything you can do with Keep's
workflow automation.
And I think that's what a lot of the
smaller companies find
interesting in what we're building
within Keep.
Okay, okay.
Now that sounds interesting.
Yeah, happy to share the
Hacker News post as well.
Yes, we'll put it in the show notes.
That's a good idea.
But again, there's nothing better, I
think, than seeing the thing in action
and seeing its limits as well, where it
maybe stops its edges.
And also, yeah, those AHA moments
that you can only have
when you see something.
So I'm looking forward to that.
It's time for the demo.
I hope it's a live demo,
so I'm a little bit nervous.
Things that can go wrong will go wrong.
Exactly.
Tal is here to support you.
Everything will go wrong Matvey,
so don't worry.
Being honest, I prepared a little bit.
If it will go wrong, it
will be a surprise for me.
Well, we can help you fix it.
How about that?
Nice.
We can try.
Well, we can at least try helping.
I don't know if we will
succeed, but we're here for you.
I want to wrap a little bit
before I jump into the demo
because before that moment, we spoke
about possibilities of
how Keep is able to find
incidents in the lake of alerts.
And this feature,
those AI features we have,
those are actually pretty advanced
features we sell to large enterprises.
And those features, they are based in our
open source Keep, which
is pretty well adopted by
large enterprises and small companies who
don't have this amount of alerts.
And I will speak about
this open source part.
So if you are interested in AI, reach out
to us, but we will not spend your time on
advertisement here.
And this part, this open source part,
it's like a Swiss knife.
So Tal mentioned, it has workflows like
GitHub Actions for
alerts, it has deduplication,
it has enrichments from CSV filtering.
And I will show one specific use case
which is covering workflows.
And I will try launching the whole
software from ground up
as typical open source user
to bring more risks to this demo.
Right.
I see.
We love that.
We love because this is closer to what
anyone would experience
when they would start.
This is the real deal.
Thank you.
So let's think about our use case.
Usually, based on my experience, when
people bring Keep into their
infrastructure, they
want as a step one for them is to
integrate it well with
everything else they have.
And the exact ways they want to build
this integration is always different.
And I was thinking about some use case
and the use case I want to cover today
is receiving alerts
from monitoring system.
Once I received an alert, I want to go to
actually application
database and check something.
And if, for example, my job didn't
publish a result to the
database in the last 15 minutes,
I want to shoot this alert
to the on call engineer
to some IRM system.
So I will build this
use case from ground up.
How does it sound?
Yeah, sounds great.
Let's try it!
To see the demo, find the YouTube video
link in the show notes.
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is enabled.
We now rejoin Matvey and Tal
just as they made it work.
Moment of truth.
Okay
It's spinning.
Wow, 200.
And we received our
alert with environment.
Thank you Tal for the help.
It worked.
And that's it.
Let's sum it up.
So what did we do?
We received an alert
from the monitoring system.
We went to...
We built a workflow which is automatically
going to the MySQL
database, making a query.
And based on the results of this query,
it's republishing this
alert to a third party system.
That's actually it.
OK.
So does this need an alert
to be triggered for
the workflow to start?
So I know that we mentioned that
workflows can be triggered
by different types of events.
We mentioned an incident
can trigger a workflow.
We've seen an alert triggering a workflow.
Is there something else
that can trigger workflows?
There is one more option
to trigger them manually.
But if I trigger them
manually, I will not
have alert content here.
So I intentionally, when I was debugging,
I wanted to...
to debug it using alert.
Probably something for
us to improve in the UI.
You can run it manually for the alert.
Right.
Yeah, it's already implemented, yeah.
One of the things we had in mind here
is that one-- another
pain point with alerts
is that you always find
it hard to debug them,
to understand whether
it really works or not.
Like just what Matvey just did.
So sending the alert over and over again,
see if the threshold is right.
Maybe you need to adjust it.
Maybe you need to change something.
Maybe there is some typo in
the query you just did.
And this was one of the things we
wanted to implement within
Keep, is to allow somebody
to test this workflow very easily.
So instead of having to send the
triggering event over and over
again, you can just very
simply either manually execute
the workflow and feed it
with everything that it expects.
So for example, environment in this case,
you could just fill it in manually
and test if the workflow runs properly.
Or you can actually take a past alert
and make it trigger the workflow again
and fill in all the information.
One more thing that I
think is interesting
is that besides events, you can also
have interval, which is
basically also a very common use
case that we see from our users.
Just run this workflow every 30 seconds
to check if the event happened or not,
and then decide to do something or not.
Do you have any sort
of reporting capability
in terms of showing how
many alerts were triggered,
workflows?
And I'm also wondering,
because off of this,
we've seen workflows fail.
So would you trigger an
alert when the workflow fails?
I think it's a cool feature idea.
We actually have emails
sending when workflows fail.
So if Matvey is the one
who uploaded the workflow,
he will get an email saying that the
execution of the workflow failed.
But now that you mentioned it, I think
it's super cool to
maybe have a workflow that
runs when other workflows are failing.
That's a cool idea.
Yeah, because that would be like my worry
when I set the system like this up.
What monitors the system
that's supposed to alert me?
And when there's a failure,
I would want to know about it.
I mean, the fact that you
have emails is a good idea,
because maybe you shouldn't
use the same thing that maybe is
failing, because then you end up in a
constant endless loop.
The thing is failing.
It's triggering other workflows.
Maybe there's like--
I can imagine that being a problem.
So having emails is important, again,
so that it gives you
assurance that when there's a problem,
you will know about it.
Actually, it's a really
good question about metrics
and about observability for
Keep, like how Keep works.
And that's something we
recently started to invest more.
And one of the latest
features is a metrics endpoint,
actually, for primitives.
So you could export to Prometheus
how many alerts you have,
how many alerts per incident you
have, and actually, you
could filter by labels here
from alerts.
So this metric
endpoint, it seems like small,
but it's really powerful in order
to get some usage statistics back to you.
Yeah.
So when it comes to deploying Keep,
we've seen like the
Docker Compose approach.
We've also seen that there
is a way to deploy it in Kubernetes.
I'm wondering what are the
requirements for Keep to run.
So it's sending emails.
I'm assuming there needs to
be some sort of integration
with like an email provider of some sort,
to be able to send those emails, maybe
SMTP, but also a database.
So does it use SQLite?
Do you need MySQL?
How does the Keep installation, like the
simplest Keep installation,
look like?
So yeah, we have a few
different deployment types.
Actually, Matvey, maybe you
can open the specification page
from the documentation once again.
But we have-- I'm just
discussing it with our team
right now, but I think
it's somewhat shooting
ourself in the foot.
But we support various
different dialects of databases.
So both SQLite, MySQL, Postgres, and
Microsoft SQL Server.
Some of our customers have that.
It's an engineering
experience to do that.
But this is what we support.
It very much depends on
the scale of the events
that you're sending into Keep.
So we have a page that describes what
kind of infrastructure
do we expect in different scale settings.
Something else we have
within the infrastructure
is at some scale, you need to
also have some queuing system
for the events to properly digest.
So we also use Redis with something
that is called ARQ, which is basically
an implementation for queues over Redis.
But this page in our documentation
basically describes everything that we
need in terms of CPU
and memory.
And we support, again,
Kubernetes, OpenShift.
Somebody from our community wrote a guide
on how to deploy this to
AWS ECS, which is also cool.
OK, this is great.
That was a cool demo, by the way, Matvey.
Thank you very much.
Oh, thank you.
And the fact that it didn't
fully work the first time, that's how we
know it was real, right?
It wasn't recorded, right?
We did it, you know, we insert like all
the commands and we started from scratch.
And there was like a small little thing
that we had to figure out, but it was
all very smooth, I have to say.
So thank you very much for sharing that.
Thank you.
As we are approaching the end,
I'm wondering what are you thinking for next
year in the context of Keep?
Are there some things that you would like
to get to, some maybe big
challenges that you see ahead?
How do you see 2025 for Keep?
I can share my thoughts about this and
it's generally two
paths that I see for Keep.
So something like we didn't really
discuss but it's a major part of what
Keep is, is that we're open source.
And like we discussed before, there's a
lot of use cases for the smaller
startups, smaller
companies when they use Keep.
And I think we love open source besides
the fact that it opens a business world
for us because also
bigger companies, enterprises,
they look at open source as the big next
thing and they try to migrate some of the
tools that they're already
using today to open source.
So one path in my eyes is that we want to
keep on nurturing this community.
We want to make it, we want to make Keep
the go-to for engineers or for operations
as this Swiss knife of everything that is
alerts or events and the
things they can do with it.
On the other hand, we have huge, huge,
huge things that we still need to figure
out with AI and everything that is
evolving practically every minute.
Like I guess there's a new GPT model that
just released while we
were recording this podcast.
So there's a lot of things we need to
figure out there. There's a lot of work.
One of the things you mentioned a few
minutes ago without maybe even knowing
how important it is for
us is the reliability part.
So how can we make sure we are reliable
being the most crucial part in the
reliability pipeline in companies?
And that's something we need to invest a
lot in. And I think that's the two major,
major things we are
focusing on right now.
Matvey, I don't know if you see the
things the same way, but I guess I can
guess you can agree with that.
Absolutely.
Now you hash it out.
I am like we are very, very much an
engineering team and all of us are
engineering founders.
And when we speak about technical
challenges like reliability, I'm not
worried about them because technical
challenges are not that challenging here.
What's challenging actually is to prove
the market that we can do what we claim
in the AI space, which we almost didn't
touch today, that we can correlate alerts
to incidents at scale on premises
environment with no internet connection.
We could summarize incidents, provide
insights from the past runbooks and
that's a huge scope of work we do.
And that's actually what we sell. And we
see that in the market nowadays, there is
a lot of skepticism about it.
Like there are few products who tried to
do it in the past and
didn't do it that well.
And people who tried them, they're like,
maybe it's not a real, real thing.
Who tried it like many years ago, it
didn't work that well actually before.
But nowadays with what we see in the AI
world with LLMs, with local models, open
source models, it's all getting possible.
And challenge for us, huge challenge for
us for 2025 is to have like a few good
examples how it works at scale for large
customers and make them
published with customer stories to change
the perception of AI ops in general, that
AI ops could actually be AI.
Right. If people want to learn more about
this side of Keep, where should they go
to read or watch
something? What would you recommend?
We will publish more soon.
For now, the best way
is to go to our website.
There is a contact us form, and we
actually just do demos.
That's the best way.
The very similar call, but
mostly focused on the AI part,
we run a huge instance,
which with a lot of alerts
and it's happening on the
fly, sometimes doesn't work.
Like today.
But that's why we have demos.
Cool.
Matvey, thank you very much for the demo,
and thank you very much for reconnecting
after a couple of years.
I really enjoyed that.
Tal, it's been great knowing you and
talking a little bit
about what you're doing next.
I'm excited to see what you do.
You're right.
It is challenging.
The reliability part is challenging.
I deal with that on a daily basis,
and I know how far it goes.
It's all systems, and
people are part of those systems.
So even though tech is the good part,
it's the people that make mistakes.
What you're trying to
prevent, or at least we're
trying to make it more
obvious when they happen.
So that's the hard part.
So I do have an appreciation for that.
But the AI, all the stuff
that's coming, you're right.
It's moving very fast, but the
capabilities are amazing.
So I'm excited about the
intersection of the reliability
part, the AI part, the complexity part,
how we're trying to just
make sense of the sea of alerts,
which I think is a
great way to think of it.
We're just getting
overloaded with signals
from all over the place.
Most of it is noise,
but in that haystack,
there's a needle that's really important.
And if you don't pay attention to it,
CrowdStrike 2 is just around the corner.
That's a placeholder for
whatever the next big thing is,
but we know it's going to happen.
Thank you for having us.
This was amazing.
And probably a sneak peek
for maybe we meet again.
I hope not in three
years, but maybe sooner.
Yes.
It is a teaser.
Sooner, yes.
We have some plans.
We spoke about the AI
today, and we intentionally
didn't show it because we find it
valuable to show open
source part in such podcasts,
something you can jump in.
Indeed.
And if you're working
in large enterprise,
you will reach out to
us and we'll show you AI.
But a sneak peek that
follow us because we
have a strong belief in open source,
and we really, really want
to downstream some of our AI
features to open source once
they will be polished enough.
So stay tuned.
Love that.
All right.
See you next time.
Bye bye.
Thank you.
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