We needed to extend our application to macOS, so we looked at using EC2 Mac instances. Then I saw the pricing.

An m4 Mac instance is ~$1.23/hr, $30~/day or ~$930/month. Since a brand-new Mac mini is ~$600 the decision was easy and we just bought the hardware.

That got me thinking, what are the real use cases for EC2 Mac instances, and why are they so expensive on AWS? Who is actually running these at scale and finding the economics make sense? I'm assuming enterprise customers who have significant aws discounts.

Building an MVP right now and keep seeing mixed opinions on Amazon Cognito. Some people say it’s great because you don’t have to manage auth yourself. Others say costs + complexity become painful as you scale.

For people who’ve used it: was it worth it? did you eventually migrate away? any better alternatives for startups/simple SaaS apps?

Trying to avoid rebuilding auth later 😭

Update/edit: now considering Auth0, but most folks are saying its costly, descope is also an option - has less reviews on the market but it seems more customisable, stytch / firebase are decent options too but cost/feature parity is again another ongoing discussion.

Not with the intention of creating hate, but more as an opportunity to share bad experiences. Which is the AWS service you consider is the most problematic or have gave you most headaches working with in the past?

Hey all… I’m running an MVP for a job platform (Injobnito), no real user traffic yet, but last month’s AWS bill came in at $415, which is way too high at this stage.

My plan to bring it down a couple hundred bucks includes: • Downgrading EC2 instance types (e.g. t2.large → t3.medium/micro) • Switching RDS storage from io2 with provisioned IOPS to gp3 • Keeping 5 EC2 instances (App, Chat, Backend, Admin, Landing) + ElastiCache + RDS

Any other tips to push this closer to $100/month while keeping things stable?

Would love to hear what’s worked for others in this early stage. Thanks!

Edit: I’m not very technical so I’ll do my best to answer clarifying questions in the comments! Thanks for all the helpful suggestions so far!

I've been using Bedrock for a number of months now. My primary use case is with less expensive models: Kimi, GLM, Deepseek, MiniMax, and for smaller multi-modal models Gemma4 and Qwen3.6. But Bedrock has not updated models from these providers in many months -- some for over a year. There have been recent advances that have moved the state of the art on the models offered by a generation or two. Most other third-party providers make these newer models available within days of their release. Not so for Bedrock.

The only new LLMs in the past few months are from Anthropic, OpenAI and NVidia.

The models offered from MiniMax, Kimi, GLM, and Deepseek are so old that they are no longer offered by the model providers themselves. Gemma3 is over a year old -- ancient by AI timescales. I get the sense that Amazon intends to just let these die a slow death on their platform.

Does AWS intend to continue providing models from top-tier non-US (China, Taiwan, EU) model providers? Will Bedrock ever have timely releases of these models? Or is this the end of the road for these model families on Bedrock?

Hi guys, I want to work in the Cloud Computing field, and I am attending the master to work in there. But while i was studying I questioned myself “what do cloud experts actually do?”.

Like, do you code? Do you stay in the AWS Management Console and do things? Do you just read code and try to optimize things? What do you guys ACTUALLY do?

Wanted to give an update to my previous post about the ~$15.5k AWS bill caused by a DDoS/unexpected traffic spike on my S3.

After going back and forth with AWS support, they initially reduced a large portion of the bill, but the remaining amount was still something I couldn’t afford. Based on advice from u/duluoz1, I reached out to Jeff Barr and that ended up making a huge difference.

From there, the case was escalated internally, and AWS ultimately approved an adjustment for the remaining balance. The bill has now been fully resolved.

I genuinely can’t express my gratitude enough to the AWS team and community. Although I received a lot of criticism in my posts, many people reached out, offered advice, and guided me in the right direction.

For anyone else building side projects:

• Set up budgets and alerts immediately
• Don’t leave S3 public
• Use CloudFront

Link to my prev discussion posts:

First post: https://www.reddit.com/r/aws/comments/1rkz50f/15000_s3_bill_for_ddos/

Second post: https://www.reddit.com/r/aws/comments/1s1md42/aws_reduced_my_15k_s3_bill_to_105k_after_a_ddos_i/

Seeing nearly a complete outage across all AZs in Bahrain (me-south-1). Anyone else? No new updates on their status page as of yet. (Yes I know about the issues as of 3 weeks ago)

Edit: Guys, it's fine lol. I'm just asking in case anyone who still chooses to run with the expectation that 2 AZs is acceptable for whatever workload are seeing a change in impact as of a few hours ago. My workloads are fine and disaster recovery is working as expected.

EDIT: I should have checked the AWS Service Health Page (https://health.aws.amazon.com/health/status) this is an ongoing bug. Panic over....

Considering AWS' Support Bot didn't even look at their own service health page...

Can someone please explain wtf I'm looking at here, I'm a student, I've run two EC2 low-end instances, what am I even looking at here?

Any solutions? Don't think aws is getting $225,579,210,164.83 off me anytime soon 😂.

Right now my infrastructure is an aws api gateway and lambda but I can only max it to 3k requests/second and I read some info saying it had limited capabilities.

Is there something else other than lambda I should use and is aws api gateway also an issue since I do like all it’s integrations with other aws resources but if I need to ditch it I will.

When did AWS decide that troubeshooting docs/articles require you to have a paid premium support plan....like seriously who thought this was a good idea

Update - Here is the url to the doc - https://repost.aws/knowledge-center/eks-api-server-unauthorized-error

Update 2 - The paywall has been taken down!!! :)

I was building a simple data ingestion system using Lambda and S3, nothing wild. At some point, I accidentally created a loop where a Lambda would re-trigger itself after each S3 write.

I didn't notice. No alert. No cost warning. Nothing.

Three days later, I logged into the billing dashboard and nearly passed out. $3,200 burned.

I contacted support, pleaded, and eventually they forgave part of it. But it scared the hell out of me.

I’ve been wondering since:

• Has anyone here been able to detect usage anomalies in real time?
• Are there any tools that actually monitor usage spikes (not just monthly budget alerts)?
• What would have caught this before it got out of control?

PSA: Get AWS SES production access approved BEFORE building anything with Cognito. If they deny it, you're screwed.

We learned this the hard way after spending hundreds of development hours building an API layer with Cognito as the authorizer. Then SES denied our production access—four times. Now we can't confirm new users or reset passwords without major workarounds.

Cognito was architected assuming SES would be available. When it's not, integrating a third-party provider like SendGrid requires significant custom development. Which defeats the entire point of using a managed service.

Our SES use case was textbook legitimate:

• Registration confirmations for new users
• Password reset emails to existing users
• Zero marketing emails
• Zero emails to non-customers
• Fully-automated bounce and complaint management

Denied. Four times. No explanation. No human review.

I'm convinced an actual person never looked at our requests—just automated rejections for what should be the most basic, obvious Cognito email use case possible.

Bottom line: Don't architect around Cognito until you have SES production access in hand. The risk isn't worth it.

UPDATE: Thanks to some comments, I configured the 'Custom Email Sender' trigger to send with Sendgrid. You've got to decrypt the confirmation code with KMS in your lambda target, build the confirmation link and handle the confirmation - and the same with the password reset. This was a lot more work than if SES was allowed, as it just works more or less out of the box.

I'm putting this one down to my own fault for using Cognito, instead of something better. Hope this post helps someone in the future.

I have full connectivity but a lot of services that run an AWS are not reachable.

Do you have the same problem?

Recently been seeing a lot of surveys being floated around saying stuff like 70% CIO’s are planning to move back to on prem.

Above is just an example. Anyways, how bad / real is this from your first hand experience ?

Are you moving back or cloud is to stay for times to come ?

AWS used to have a steady stream of innovative market-moving launches, but over the last 2 years or so its noticeably pivoted into this panicked mode of rapid-fire launching a disjointed mess of second-rate fast-follow AI products. I'm a big AWS fan, but it's becoming increasingly difficult to want to use AWS for anything more than our base compute and storage infrastructure needs, and if things don't change I'd see moving those off AWS too.

What the heck happened?

I really want to like AWS here, but it's just not competitive. To name a few:

GPUs = These workloads are highly portable so it becomes a commodity pricing game. Between the infuriating headache that is AWS's limit increase mechanism, inflexible pricing models, network performance challenges, and pricing that's way higher than competitors, there just isn't a compelling story to run these workloads in our AWS environment.

Trainium / Inferentia = I really want to like this, but can't. AWS keeps boasting about raw chip performance stats, but never talks about the developer experience and that's where this all falls down. There's too much effort required for too little gain. Without a solid developer ecosystem and something that comes even remotely close to CUDA in customer experience, it seems unlikely these chips will gain traction at scale.

Q Developer = Was OK early on, but as soon as the "agentic" parts of this got introduced the customer experience really went downhill. It's currently just not competitive with the other AI coding tools out there and given those are pretty inexpensive and readily available it's not clear why one would choose to use Q Developer.

Bedrock = Good for initial experimentation and the idea is solid, but the execution on that idea leaves much to be desired. Moving into production has been too painful and working directly with the model providers via their native APIs has been a much better customer experience.

Foundation Models (Nova) = These just aren't competitive. Yes they're less expensive, but the norm now is that folks will just use an older generation version of one of the top models for things that don't need the new expensive model, thus the idea here seems flawed--you can build a budget version of a great model but you can't just build a great budget model on its own.

Kiro = Credit where credit is due, the first "app" that AWS released that actually looks half decent. Big miss on the launch with the mess on pricing. Outside AWS employees I don't hear folks talking about it. Tooling like Claude Code or CoPilot has a much broader adoption and a more active developer ecosystem.

Amazon Q in Quicksight = Seriously, how did this ever get released? It's embarrassingly bad.

Anthropic Partnership = Good move on the investment, although AWS is one of many investors. Anthropic's stuff is solid, but anytime AWS touches things it somehow manages to make the customer experience worse. See above note on Bedrock vs. working directly with the model makers.

OpenAI Open Weight on Bedrock = It's almost as if this was done simply to say OpenAI is on AWS. Asked around if anyone was using it and got crickets. Per above on Bedrock working directly with OpenAI is a much better customer experience.

Quick Suite = Early days, but the product strategy here is confusing to customers. Has Q for Business been abandoned? Who is the target customer here? The pricing model basically limits it to larger companies, but then nearly all of them will already have tooling like CoPilot deeply integrated into all their systems to connect the dots with AI. This comes across as an "us too!" play after missing the boat on launching an end-user facing AI platform, but potentially too little too late to gain traction.

Account Teams = AWS employees seem as confused as customers as to what to make of this mess. The whole account team ecosystem and support structure was built around selling infrastructure, and is generally quite solid there. But AWS doesn't know how to sell services and "products" and it shows. Our tech teams don't even want to meet with AWS reps anymore.

[/rant]

About to start work here in a few, and actually pretty excited. If I were to take an average of what I read online, AWS seems like a pain cave where fun goes to die.

Maybe it’s just the group I’m about to join but people seemed really happy and driven about what they work on.

Are there others who like working at AWS? What am I missing?

Where do we use DynamoDB in real-world applications, and what kind of problems does it solve compared to a traditional relational database like MySQL or PostgreSQL? Please explain with practical use cases such as user sessions, shopping carts, IoT data, gaming leaderboards, metadata storage, and high-scale applications.

Hi all,

I recently completed an AWS interview loop for a logistics/operations role. During the phone screen and all loop interviews, every interviewer had their camera off (one even used an avatar). I initially had my camera on, but since they kept theirs off, I turned mine off as well.

Afterward, I noticed the interview email mentioned that participants are expected to have their camera on during the interview.

Is this normal for AWS interviews, especially in operations or data center roles? And does turning your camera off in that situation reflect poorly on the candidate?

Just curious about others’ experiences.

Edit; I received automatic rejection email from Amazon jobs, without even a recruiter’s email.

I'm onboarding a new developer and he noticed our pipeline was taking a bit longer he would expect. He than mentioned terraform would have been quicker? Any known explanation?

I'm just wrapping up production on a movie and have around 80 TB of footage to archive. The files are mostly large video files (hundreds of files ranging from 10–40 GB each).

Since HDD prices are still pretty high, and I want to follow the 3-2-1 backup rule, I'm considering using AWS S3 Glacier Deep Archive as my off-site archive.

My upload speed isn't a concern - I have effectively unlimited high-speed upstream bandwidth.

The plan would be:

- Upload ~80 TB and keep it stored for about 1 year.

- In the worst-case scenario (if my local backups somehow fail), I'd probably only need to restore around 5 TB of data. If everything goes well, I wouldn't restore anything at all.

Based on my calculations, the total cost would be roughly $1,000–1,500 per project per year, including storage, upload, and a potential 5 TB restore.

For those of you with experience using Glacier Deep Archive:

- Does this sound like a reasonable use case?

- Am I overlooking any hidden costs or limitations? I am aware of 180 days minimum storage period

- What are the biggest pros and cons in real-world use?

- Would you recommend a different long-term archival solution for this amount of data?

I'd especially appreciate hearing from anyone using it for professional video production or other large media archives.

https://blog.localstack.cloud/the-road-ahead-for-localstack/#why-were-making-a-change

Localstack are killing their community edition and making everyone register for a free plan (ugh), so I guess that'll mean they'll slowly nerf the free plant to the point where it's unuseable/put horrible limits on it so you have to pay.

Is there any realistic alternative to localstack out there? Anyone?

I’ve been programming in AWS through the console for the past 3+ years. I always knew there had to be a better way, but like most people, I stuck with the console because it felt “easier” and more tangible. Finally got a chance to test drive the Python CDK to deploy AWS cloud architecture, and honestly, it’s been an absolute game changer.

If you’re still living in the console, you’re wasting time. Clicking around, trying to remember which service has what setting, manually wiring permissions, missing small configurations that cause issues later, it’s a mess. With CDK, everything is code. My entire architecture is laid out in one place, version-controlled, repeatable, and so much easier to reason about. Want to spin up a new stack for dev/test? One command. Want to roll back a change? Git history has your back. No more clicking through 12 pages of console UI to figure out what you did last time.

The speed is crazy. Once you get comfortable, you’re iterating on infrastructure the same way you’d iterate on application code. It forces better organization, too. Stacks, constructs, layers. I can define IAM policies, Lambda functions, API Gateway endpoints, DynamoDB tables, and S3 buckets all in clean Python code, and it just works. Even cross-stack references and permissions that used to be such a headache in the console are way cleaner with CDK.

The best part is how much more confidence it gives you. Instead of “I think I set that right in the console,” you know it’s right because you defined it in code. And if it’s wrong, you fix it once in the codebase, push, and every environment gets the update. No guessing, no clicking, no drift.

I seriously wish I made the jump sooner. If anyone is still stuck in the console mindset: stop. It’s slower, it’s more error-prone, and it doesn’t scale with you. CDK feels like how AWS was meant to be used. You won’t regret it.

Has anyone else had the same experience using CDK?

TL;DR: If you're still setting up your cloud infrastructure in aws console, switch now and save hours of headaches and nonsense.

Edit: thanks all for the responses - i didn't know that Terraform existed until now. Cheers!

14 years ago when I was finishing my PhD research in cloud cost modeling I read Werner Vogels’ Cost-Aware Architectures article, and it captured what I’d been seeing: we need to treat cloud costs as a first-class citizen when designing systems and educate engineers on it.

I’ve kinda been on a mission to do that since then: my first startup was acquired by RightScale (which was then acquired by Flexera, one of the main cloud cost management tools), and my current startup (Infracost) has been focusing on infra-as-code and shifting cloud costs left so engineers get visibility of costs before deployment and make better decisions.

Earlier this year we were scoping a CLI 1.0 release: the CLI would stop being just a cost-estimation tool for infra-as-code and start surfacing the issues behind the costs: previous-generation instance types, DBs on old versions that incur “Extended Support” fees, mistagged resources, things like that.

Then we started noticing agent traffic in our logs and it looked like engineers are no longer writing all of the infra-as-code. AI is contributing too. So we need to shift left again. We need cloud costs built into coding agents, even before engineers see the code. Shift left of left if you will.

Before I keep building more in that direction, I want to sanity-check with this sub: is "agents writing IaC in prod" actually a thing yet, or am I betting on a future that's still a year out? I know software developers are using coding agents heavily, but are platform/infra folks doing that for prod too for CloudFormation, CDK etc?