Welcome back to the deep dive. Our mission, as
always, is pretty simple. We take a whole stack
of complex sources, pull out the really critical
stuff, and basically give you the shortcut to
understanding it all. Today, we're diving into
something that trips up a lot of people, even
tech pros. It's cloud economics. Specifically,
AWS economics and billing. I mean, you can be
a wizard with APIs, right? Build amazing architecture.
But if you don't get the dollars and cents, Well,
your whole cloud strategy could fall apart. OK,
let's really unpack this. This isn't just about
passing an exam. It's about true financial literacy
for the cloud age. That's the bedrock for actually
driving business value. That's absolutely right.
And it's crucial because AWS gives you this incredibly
flexible consumption -based model. I think utility
is amazing for scaling up or down. But like you
said, that flexibility, it's a bit of a double
-edged sword. If you don't really grasp how the
billing works, what actually drives the costs
and what levers you have to pull those costs.
can just spiral completely, and suddenly all
the reasons you moved off -prem are gone. So
yeah, our focus today is really on mastering
the strategies to, well, harness those costs
effectively. Okay, so let's start right at the
top. Cloud economics. It sounds a bit academic,
maybe, but it's really the heart of making good
decisions, isn't it? It really is. You can think
of it as the study of financial impact. How does
using cloud services actually affect your bottom
line? It's the analysis you use to compare, say,
running workloads in AWS versus your own data
center. That's the whole total cost of ownership
piece we'll get into. Right. TCO. Exactly. But
it's also about... forecasting what you're likely
to spend next month, next quarter, and then constantly
looking for ways to optimize that spending. You
want every dollar you spend on IT to really align
with what the organization is trying to achieve.
And the core idea, as you mentioned, is modeled
after utilities. That pays you go thinking. You
only pay for what you actually use, right? Unless
you specifically choose to commit upfront for
a discount. Precisely. Pay only for what you
consume second by second, or hour by hour, or
gigabyte by gigabyte. So if we boil it all down,
what are the absolute main things, like three
big variables that you, the listener, really
need to track? Okay, the three fundamental cost
drivers are compute, storage, and data transfer.
Compute is pretty straightforward. You're paying
per hour, or sometimes per second, depending
on the specific virtual machine, the instance
type. Okay. Storage is typically charged per
gigabyte per month. How much data are you storing,
and for how long? And data transfer. This one
seems to catch people out sometimes. It can,
yeah. The key distinction is direction. Data
going out of AWS, like to the internet, or back
to your own data center, that's generally charged
per gigabyte. But data coming in to AWS, often
free. And data moving between services within
the same AWS region. also often free, understanding
that in versus outflow is really critical for
accurate budgeting. You don't want surprises
there. No kidding. OK, so if those are the drivers,
how do you actually pay? Because obviously, companies
have all sorts of different needs, right? Predictable
stuff, totally unpredictable stuff. Exactly.
And that's why AWS doesn't just have one price.
They offer four main pricing models. Think of
them as different strategies or financial levers
you can pull depending on the workload. All right,
let's start with the default, the baseline. On
-demand. On -demand is exactly what it sounds
like. No commitment needed. You just pay the
standard rate by the second or hour for whatever
you use. It's perfect for spiky, unpredictable
workloads. Maybe you're testing a brand new app.
Or like a short marketing campaign website. Perfect
example. Or some quick data analysis. It offers
the most flexibility, zero strings attached.
But it also comes at the highest price point
per unit. OK, so maximum flexibility, maximum
price. But what happens when that test app becomes
the main company website? Running that on demand
forever would get expensive fast. Right. And
that's where the strategy comes in. Once you
have a workload that's stable, predictable, something
you know you'll be running constantly, that's
when you look at reserved instances, or RIs.
RIs. The commitment model. Exactly. You're telling
AWS, hey, I'm going to run this specific type
of instance in this region. for one year, or
maybe even three years, and because you're giving
them that predictable revenue, they give you
a really significant discount. How significant
are we talking? It can be huge. Up to 72 % off
the on -demand price. Think about that for your
main web servers, your core databases, the things
that are always on. That's serious savings. 72%,
wow. OK, and I remember there are different ways
to pay for that commitment too. Yeah. Right,
it's not just one option. Correct. You've got
flexibility even within the commitment. There
are three payment options. You can pay all upfront,
basically pay for the whole year or three years
at the start. That gets you the absolute biggest
discount. OK. Or you can do partial upfront.
Pay some now and the rest monthly. Gets you a
good discount, maybe not quite as deep as all
upfront. Right, balances cash flow a bit. Mm
-hmm. And then there's no upfront. You still
make the one or three -year commitment, but you
pay for it purely on a monthly basis. The discount
is smaller than the other two, but you don't
have that initial cash outlay. It really depends
on your organization's financial preferences.
That makes sense. So, orias were king for a long
time for predictable workloads. But things have
evolved, haven't they? There's something newer,
more flexible. Yes, absolutely. And this is where
savings plans, or SPs, come in. They're kind
of the modern, more flexible evolution of RISE
in many ways. How so? What's the core difference?
Instead of committing to, say, running a specific
instance type, like an M5 .large in the US East
1 region. Which is what you do with an RI. Exactly.
With a savings plan, you commit to spending a
certain amount of money per hour on compute usage.
For example, you might say, I commit to spending
at least $20 per hour on compute services. Ah.
OK, so it's a dollar commitment, not an instance
commitment. Precisely. And that commitment automatically
applies to eligible compute usage across different
instance families, different sizes, even different
AWS regions, and sometimes even across services
like Fargate or Lambda, depending on the type
of savings plan. OK, so there are different types
of savings plans too. Yes, two main types. Compute
savings plans are the most flexible. That 20
-hour commitment we just talked about It could
apply to an M5 instance today, a C6 instance
tomorrow, maybe running an Oregon one week and
Ohio the next. It even covers Fargate and Lambda,
super flexible. So if you change instance types
or regions, you keep the discount. You keep the
discount as long as you're meeting that hourly
dollar commitment. The other type is an EC2 instance
savings plan. This one is a bit more like an
RI you commit to using a specific instance family,
like M5, within a specific region, like US East
1. Less flexible than the compute SP. Less flexible,
yes. But in return, it generally offers a slightly
deeper discount than the compute savings plan
for that specific family and region. So it's
a trade -off. Maximum flexibility with compute
SPs. Or potentially slightly better savings,
but less flexibility with EC2 instance SPS. Got
it. That flexibility seems really powerful, especially
as architectures evolve. OK, one more major model.
The one with the steepest discount. but also
the biggest catch, spot instances. Spot instances.
Yes, the potential savings here are, well, staggering.
Up to 90 % off the on -demand price. 90%, how
is that even possible? It's because you're essentially
bidding on spare unused compute capacity that
AWS happens to have at that moment. You say,
I'm willing to pay up to this price for this
instance type, and if your bid is above the current
spot price and there's capacity, you get the
instance. Okay, sounds great. What's the catch
you mentioned? The catch is huge. Because it's
spare capacity, AWS can reclaim it whenever they
need it back for on -demand or reserved instance
customers. And they only give you a very short
warning, typically just two minutes. Two minutes.
So your instance could just vanish. Oof. Gone.
Terminated. Which means Spot instances are absolutely
not suitable for workloads that can't handle
interruption. Forget running your main database
on Spot. Right. So what are they good for? They're
fantastic for things that are fault tolerant
and flexible. Think large -scale batch processing
jobs, maybe big data analysis, scientific computing,
rendering farms, tasks where if one instance
gets terminated, the overall job can either restart
that piece of work elsewhere or just carry on
without it. You have to design your applications
specifically for that potential interruption.
Makes sense. High risk, high reward, but only
if your workload fits. OK, and before we move
off pricing, we should probably mention the AWS
free tier, should we? Especially for listeners
just starting out. Oh, absolutely essential.
The free tier is how most people get their hands
dirty with AWS without spending money initially.
There are really two parts to it. Right. There's
the 12 month one. Yes, the 12 -month free tier.
When you first sign up for an AWS account, you
get free usage allowances for a whole range of
popular services for your first year. Things
like 750 hours per month of a small EC2 instance.
Enough to run a small website, basically. Exactly.
And five gigabytes of S3 standard storage, a
certain amount of database usage, and so on.
It's quite generous for learning in small projects.
But it expires after a year. It does. But then
there's the always free tier. These are services
that have a free usage allowance that never expires,
even after your first year. Things like AWS Lambda,
a million free requests per month, DynamoDB,
a certain amount of storage and throughput, SNS
notifications. So even experienced users can
leverage those always free services. Absolutely.
But the key thing, especially when you're in
that first 12 -month period, is to monitor your
usage. It's easy to accidentally go over those
free tier limits, and that's often how people
get their first unexpected AWS bill. So keeping
an eye on those limits is kind of your first
lesson in AWS cost management. Good point. So
we've got the cost drivers, the pricing models.
Let's shift gears a bit. How do organizations
make the big strategic decisions, like deciding
to move to AWS in the first place? You mentioned
TCO earlier. Right. Total cost of ownership,
TCO. This is the big financial framework used
to justify moving from an on -premises data center
to the cloud. It's all about making a fair comparison.
What goes into that comparison? It's not just
the cost of the servers, right? Not at all. That's
a common mistake. A proper TCO analysis includes
the obvious server hardware costs, yes, and software
licenses. But crucially, it also has to include
facilities costs, like the electricity to power
those servers, the industrial air conditioning
to cool them, the physical security for the building,
the actual square footage costs of the data center
space. OK, the hidden infrastructure costs. Exactly.
And then there's storage costs, not just the
disks, but backup system. tape libraries, et
cetera, and maybe the biggest often underestimated
cost? Labor. The people cost. The people cost.
How much time do your IT staff spend racking
servers, replacing failed drives, patching operating
systems, managing network gear, doing backups,
all that operational toil? The argument for AWS
is that by shifting that burden to them, your
team can focus on higher value work. And AWS
has tools to help with this. They do. The AWS
TCO Calculator is a web -based tool where you
can input details about your current on -premise
environment, and it helps generate a comparison
report showing the potential savings with AWS.
It's a key tool for building that initial business
case. Makes sense. Okay, another organizational
thing. What about large companies with lots of
different departments or projects all using AWS?
How do they manage that financially? That's where
AWS organizations and consolidated billing come
in. This is a really powerful feature. AWS Organizations
lets you group multiple AWS accounts. Maybe one
for marketing, one for engineering, one for finance
under a single central management account. Okay,
so brings them all together administratively.
What's the financial benefit? Two main ones.
First, volume discounts. Many AWS services, like
S3 storage or data transfer, have tiered pricing.
The more you use, the cheaper the per unit cost
gets. With consolidated billing, AWS pools the
usage across all the accounts in your organization.
Ah, so even if individual accounts don't use
much, the combined usage might push the whole
organization into a cheaper pricing tier. Precisely.
Everyone benefits from the aggregated volume.
The second big benefit is simplified cost tracking
and governance. The management account gets one
single bill covering all the linked accounts.
And you can easily use things like tags, which
we'll talk about, or just the structure of the
linked accounts themselves to see exactly how
much each department or project is spending.
It makes chargeback or showback much easier for
the finance teams. Right, visibility and potentially
lower costs through volume. Okay, we've got the
strategies, the frameworks, but how do you actually
track this stuff day to day? I mean, you mentioned
that first bill shock, everyone wants to avoid
that. What are the tools? Yeah, knowing the theory
is one thing. Actually managing it requires using
the right tools. The starting point is usually
the billing dashboard in the AWS console. What
do you get there? It's your main overview. You'll
see your current month to date spend, your forecast
for the month, a breakdown of spending by service,
your payment history, access to your actual invoices.
It's the central hub. OK, the high level view.
But what if you see a spike and need to figure
out why? That's when you dive into AWS Cost Explorer.
This is a much more powerful visualization tool.
You can graph your costs over time, daily, monthly.
You can filter that data in countless ways. Show
me just EC2 costs, just costs for the production
account, just costs associated with a specific
Project X tag. So you can really slice and dice
the data to find the source of spending. Exactly.
And a really key feature is its forecasting capability.
Based on your recent usage patterns, Cost Explorer
can project your spending for the next few months,
helping you anticipate future bills and adjust
if needed. OK, so Cost Explorer is for analyzing
past and future spending. What about preventing
overspending before it happens? That's the job
of AWS Budgets. This tool lets you set specific
thresholds for either cost or usage. You could
say, alert me if my toll monthly spend exceeds
$1 ,000 or alert me if my S3 storage goes over
five terabytes. And it just sends you an email
or something? Yep. You configure notifications,
email, SNS topics so that the moment a budget
threshold is breached or even just forecasted
to be breached, you get an alert. It's your early
warning system. Super important for avoiding
those nasty surprises. Definitely sounds useful.
Now I've heard about something called CUR files,
cost and usage reports. What are those? Ah, the
cost and usage reports or CUR. Okay, these are
the most detailed billing data you can possibly
get from AWS. We're talking incredibly granular
information about every single resource usage,
down to the hour, with pricing details, tags,
everything. And intense. It is. They're delivered
as large, complex CSV files to an S3 bucket,
you specify. These are not really meant for casual
browsing in Excel. CUR files are designed for
organizations that want to feed this raw data
into their own sophisticated business intelligence,
BI tools, data warehouses, or third -party cost
management platforms for real - really deep customized
analysis and automation. So power user level
stuff for deep integration. Definitely. For most
day to day tracking, cost exploring budgets are
where you'll spend your time. Got it. And one
more tool maybe for the planning stage. before
you've even deployed anything. Right. The Simple
Monthly Calculator, or now often referred to
as the AWS Pricing Calculator, this is a web
tool you use before you build. You can model
out a potential architecture, say two web servers
of this size, a database of that size, estimate
your data transfer, and it gives you an estimated
monthly cost. Crucial for planning and getting
budget approval before you start incurring charges.
Okay. Great overview of the tools. So using these
tools, understanding the models, it all leads
towards optimization, right? Keeping that bill
under control. What are the key best practices
here? Absolutely. Optimization is an ongoing
process, not a one -time fix. There are probably
six core best practices that every organization
serious about cloud costs follows. Let's hear
them. Number one. Number one is almost always
right sizing. This means constantly evaluating
if the resources you're running are actually
the appropriate size for the workload. Are you
paying for a huge, powerful EC2 instance when
its CPU utilization is only hovering around 10
%? Right. Paying for capacity you don't need.
Exactly. So monitor your utilization metrics.
using CloudWatch, for instance, and downsize
instances that are over -provisioned. Conversely,
make sure instances aren't under -provisioned
and struggling. And using autoscaling is part
of this automatically adding or removing instances
based on demand, so you're only paying for what
you need at any given time. Makes sense. OK,
number two and three probably relate back to
our pricing discussion. You got it. Number two
is using reserved instances or savings plans.
For any workload that has predictable steady
state usage, you should absolutely be leveraging
these commitment based discounts. Leaving predictable
workloads running on demand is just leaving money
on the table. And number three. Leveraging spot
instances. For those workloads that are fault
tolerant and flexible, using spot can drastically
cut costs. It requires careful architecture,
but the savings can be enormous. Okay, so match
the pricing model to the workload characteristics.
What's number four? Diligent monitoring. You
can't optimize what you can't see. This means
regularly checking Cost Explorer, setting up
AWS budgets for alerts, and generally keeping
an eye on your spending trends. Catching anomalies
early is key. That test environment someone forgot
to shut down over the weekend. Monitoring catches
that. Good point. Number five. This one's crucial
for visibility, especially in larger organizations.
Tag resources. AWS allows you to apply metadata
tags, key value pairs like project .phoenix or
department .marketing, to almost all your resources.
And Cost Explorer can filter by these tags. Exactly.
If you consistently tag your resources, you can
then use Cost Explorer or CUR reports to see
exactly how much Project Phoenix cost last month,
or which department is responsible for that spike
in S3 spending. Without tags, it just becomes
one big undifferentiated bill. Tags are essential
for accountability and cost allocation. Got it.
Seems like good hygiene. And the last one, number
six. It sounds simple, but it's often overlooked.
Turning off idle resources. If developers spin
up test environments and forget about them, or
if you have resources running overnight or on
weekends that aren't actually being used, shut
them down. Stop the instance. Delete the unused
EBS volume. Remember, you pay for things while
they're running, so don't pay for resources that
are just sitting idle. Shut down what you're
not using. Makes perfect sense. Okay, so those
are great optimization practices. What about
getting help from AWS when things go wrong, or
even just for advice? Let's talk about support
plans. Right. AWS offers different tiers of paid
support on top of the basic help everyone gets.
There are four main levels. Okay. What's the
baseline? Basic support. This comes free with
every AWS account. It gives you access to documentation,
white papers, support forums, and importantly,
you can open support cases for billing questions
or account issues, but no technical support for
your actual running services. Okay. Free, but
limited to billing and docs. What's the first
paid tier? That's developer support. It starts
at around $29 per month plus a percentage of
usage. This gets you technical support via email,
but generally only during business hours with
response time goals measured in hours. It's aimed
at individual developers or small teams doing
testing. Okay, email support during business
hours. What's the next step up? Business support.
This is probably the most common paid tier for
organizations running production workloads. It
starts at around $100 per month, plus a percentage
of usage. The big step up here is 247 access
to technical support via email, chat, and phone
with much faster response time goals, often under
an hour for critical issues. 247 phone support.
That sounds important for production. Anything
else with business? Yes. A really valuable benefit
is full access to AWS Trusted Advisor checks.
Trusted Advisor automatically scans your AWS
environment and provides recommendations across
several categories, including cost optimization,
security, performance, and fault tolerance. Business
support unlocks all the checks. So proactive
advice, not just reactive support. OK. And the
top tier? The top tier is enterprise support.
This is for large organizations with mission
-critical workloads and significant AWS spend.
Pricing starts at $15 ,000 per month. $15 ,000.
Wow. What do you get for that? You get everything
in business support, plus even faster response
times, like 15 minutes for critical issues. But
the real differentiator is you get assigned a
dedicated technical account manager. or TAM.
A TAM? What do they do? A TAM is basically your
dedicated advocate and strategic advisor within
AWS. They get to know your architecture, your
business goals, they help with infrastructure
planning, operational reviews, coordinating AWS
resources during major events or issues. It's
a very high touch proactive partnership. White
glove service, essentially. Okay, so a dedicated
guide inside AWS. That makes sense for huge deployments.
All right, we've covered a ton. Models, tools,
optimization, support. Let's try to tie it all
together. Can we walk through a hypothetical
example? KV. University cloud lab environment.
How would all these concepts play out there?
Oh, a university is a perfect example because
you have such diverse users and needs. It's like
a mini enterprise. OK, so where would we start?
Students. Right. Your students who are just learning
AWS doing coursework. They'd primarily be using
the free tier, setting up small EC2 instances
using S3 for projects. The university benefits
because the students learn valuable skills at
basically zero cost, assuming they stay within
the limits. It also teaches them cost awareness
early on. Makes sense. What about faculty doing
research, maybe needing a burst of compute for
a few weeks for a specific project? For those
kinds of short -term, potentially unpredictable
research projects, faculty might rely heavily
on on -demand instances. They need the flexibility
to spin up resources quickly for a specific experiment
and then shut them down. The higher on -demand
cost is acceptable because the usage is temporary
and specific. OK. Now, what about the core university
systems, like the student registration portal
or the learning management system like Moodle
or Blackboard? Those need to be always on, right?
Exactly. For those critical steady -state administrative
systems that run 24 -7, the university IT department
would absolutely use reserved instances, or more
likely now, savings plans. They know these systems
will be running constantly, so committing for
one or three years gives them those deep discounts
we talked about, saving significant money compared
to on -demand. All right, so different pricing
models for different user groups and workloads.
How would they manage the budgets across, say,
the physics department versus the Arts Department.
They definitely use AWS organizations to group
all the departmental accounts under a central
university IT management account. This gives
them that consolidated billing benefit pooling
usage for volume discounts. And then within organizations,
they'd set up AWS budgets for each department
or maybe even major research grants. The physics
department might get a $5 ,000 monthly budget.
Arts gets $500. If a department starts to exceed
its budget, alerts go out to the department head
in central IT. It enforces accountability. And
tracking usage patterns, maybe seeing when things
get busy. That's where Cost Explorer comes in.
Central IT would use it to monitor overall trends.
They'd probably see huge usage spikes around
final exam periods, right? When everyone's hitting
the learning system or running simulations. Yeah,
definitely. Seeing that pattern in Cost Explorer
helps them plan capacity for the next exam period.
They might even use tags to see if one specific
course or research project is driving a lot of
cost. And finally, support. What level would
a university likely choose? Given the criticality
of systems like the Learning Platform, especially
during exams, they'd almost certainly need more
than basic support. They'd likely opt for the
Business Support Plan. That 247 phone access
and the quicker response times for critical issues
would be essential to ensure uptime for students
and faculty when it matters most. Okay, that
paints a really clear picture of how all these
different pieces, pricing, tools, structure,
support, come together in a real -world scenario.
So, wrapping things up, what we've really established
today is that Understanding AWS economics isn't
just an add -on skill. It's fundamental. It's
about shifting your mindset from just buying
server capacity to actively managing resource
consumption. Exactly. Being able to explain these
different pricing strategies, knowing how to
use the billing tools effectively, and critically,
being able to align cloud spending with the actual
financial goals of your organization, that's
what defines a truly skilled cloud practitioner
today. It's about understanding the trade -offs.
The flexibility of on -demand versus the savings
of commitment with RIs and SPIs, knowing when
to use Spot, that's where the real value lies.
You have to see the cloud as this constantly
running financial ledger, basically. Okay, so
here is a final thought for you our listener
to chew on building on what we discussed about
AWS organizations Given how powerful consolidated
billing is and the volume discounts it enables
which really push companies towards putting everything
under that one financial umbrella How might the
purely financial incentives baked into AWS organizations
actually start to change the internal operational
structure, maybe even the internal politics,
of a company compared to the old world of separate
siloed IT budgets for each department? That's
the strategic governance question we'll leave
you with.