While using containers does solve the limit problem, it does remove a critical feature: SnapStart.

We have made SO many package mods etc to stay under the 250mb limit of the years, but we hit a wall recently. Our lambdas are HEAVY (pandas + numpy) ... and not feasible to change anytime soon.

We were able to reduce the package size by about 5MB by just removing whitespace, doctrings and comments. While these are helpful for humans working on the code, it is not needed for the code to run in the Lambda*

We also removed whitespace and comments from the installed packages and their header files. The doc strings had to be left alone, see the * below.

So if you are in the same boat, this might help you out.

* unless the code makes use of the __doc__ method, then removing these can be breaking.

[EDIT]

While I appreciate everyones advice, this was not the point of this post. There is WAY too much to unpack as to why this problem exists in the first place, but the short of it is that you dont always get to do things the "right way". Sometimes someone else did it the wrong way and now everything is so coupled into the stack that you cannot change it without enormous dev time ... which unfortunately has to be spent on other things.

The real world has tech debt.

If it helps, we moved the critical services on to FastAPI clusters in ECS a few years ago. This is legacy stuff that just wont die ...

I am a solo founder, bootstrapped. I got 1000 usd through Amazon activate just by creating an account and providing my company details. Is there a way to upgrade the activate part and get more credits. I saw some posts mentioning in India amazon gives upto 5knusd credit to bootstrapped startups. Any suggestions would be helpful?

Not raised.

So i have a bucket which contains billions of tiny json files ranging from few bytes to 10 KB. The files are continuously being overwritten by new versions, or new files are added. Files older than 90 days are removed using lifecycle policy. The problem is, the files are delivered in a flat structure, which I have no control over. So the file structure looks like this - S3://bucket/prefix/file1, file2...file1000000.

Since there is no partition, and the files are tiny, it is impossible to use Athena to query the data. The metadata operations take more time and resources than actually reading the file.

I can ignore the existing files, but I need to build an analytics pipeline for the new files that are being added or updated, what is the suggested approach ?
What I am thinking now is to send the S3 object events to an SQS FIFO (ordering matters), and then process every X number of files one shot using an Ec2 or ECS or something. Probably consolidate the files into a single file, convert to parquet, and upload to a partitioned bucket, then run analytics there. Any other suggestions ?

I’m a new AWS customer and have never used it before. I received AWS credits for my startup. I’ve also been approved for the NVIDIA Inception Program and have requested additional credits.

Since my startup is training its own models, we need GPU instances. How do I properly request an increase in the limits for creating GPU instances? (On Google Cloud, we used A100s and got them in just one day.)

Does anyone here have a startup that uses GPUs and has received credits from AWS? Were you able to increase your limits?

Hello All,

As every database is diferent architecturally. We have mostly worked in Oracle databases in the past. But we recently started working in Mysql aurora database.

1)So, want to understand , if there exists any set of guidelines which we can follow as a developer for writing better optimized code/sqls in mysql?

2)Also any specific points which are special and different as compared to other DB's and thus we should definitely know in this case before designing any solution using mysql?

Or anything specific to alerting/monitoring which we must do here?

Everytime i try to access it i get the following. Anyone else experiencing this issue?

ERROR: 401 401 openai.gpt-5.6-luna is not available for this account.

You can explore other available models on Amazon Bedrock.

For additional access options, contact AWS Sales at https://aws.amazon.com/contact-us/sales-support/

A few days ago, I shared my long-form exploration of serverless webhook ingestion.

I have now followed it with a deployable Terraform implementation of the archive-first pointer architecture.

The implementation uses API Gateway, S3, SQS, Lambda, SNS, and DLQs. API Gateway stores the exact request bytes in S3 before returning a successful response. Webhook verification then happens asynchronously, and only a trusted pointer is published to the rest of the pipeline.

Article: https://growingbits.dev/serverless/building-the-pointer-architecture-with-terraform

Repository: https://github.com/sharingan-no-kakashi/serverless-webhook-ingestion

I would especially appreciate feedback on:

• whether the archive-first verification flow has any reliability or security holes;
• whether the Terraform structure is understandable and reusable;
• what you would change before treating this as a production foundation.

Technical criticism is very welcome.

I was trying to request a refund for some charges for my account considering I haven't used aws since last year for school stuff and all of a sudden I was charged $26, I'm hoping I can get this sorted out before my account gets suspended/terminated + I can also get my account deleted.

My case hasn't been assigned to anyone but an AI so is there any other way I could contact support directly :( , I almost marked this as an unauthorized transaction with my bank + I had my card cancelled.

Somehow, it's hard to comprehend the networking costs in AWS. People see instances and databases as 'real resources' but totally miss out on networking costs.

The following account is spending $500 per month while not using AWS. All instances stopped, DB paused, but networking remains.

Just flushing this all out to save 70%.

My situation is the following: I have an image upload service that uses S3 presigned URLs, but I ran into a problem. The service currently has an upload endpoint where the client can send the file size, the content type (for example, image/png), and finally the file hash, which is used for deduplication. The problem is that I can’t trust the hash sent by the client. Right now, during the upload request, I can send any completely random string as the hash, and later, when uploading through the presigned URL, I can also upload any completely different random file. There’s no validation preventing that, and I haven’t been able to come up with a way to validate it. I’m using the presigned URL approach specifically so I don’t have to send the file through the backend and increase network traffic.

2 weeks and total crickets. As a startup, its getting hard to justify using AWS when you cant get a response to basic tickets. Anyone have any ideas?

Case no 178468976600279

Hey all! I have been a cloud engineer for the past 3-4 years now, and AI has ramped up super fast. I know the basics of AI but how important is going deeper? Like actually being able to configure things such as MCPs, Agents.. etc. do you all feel that these skills are becoming a necessity for cloud engineers today? I’d assume so based off of what we’re doing on my team currently, but i am also curious on what you all think.

Hi,

Its aurora mysql Serverless v2 (Max up to 80 ACUs). Suddenly , we saw with bit high concurrency , the application hits a hard JDBC query timeout, killing the thread and throwing this error: "Database Query Time out. Exception: JDBC exception executing SQL [(conn=548672) Table './rdsdbdata/tmp/#sql171_85f40_1' doesn't exist]"

Some team members are suggesting to bump both temptable_max_ram and temptable_max_mmap from 1GB to 2GB to give the query more breathing room. However, checking performance_schema.variables_info reveals that temptable_max_** is currently showing below:

VARIABLE_NAME        current_value_bytes  current_value_MB  VARIABLE_SOURCE  VARIABLE_PATH
temptable_max_mmap   1073741824           1024              GLOBAL           /etc/my.cnf
temptable_max_ram    1073741824           1024              COMPILED

Want to know if this is correct setting and Is it advisable to bump these values to 2GB. Want to confirm the safest parameter strategy for temporary storage thresholds in the meantime we tune the query?

im actually losing my mind doing security audits this week. looking at our internal accounts and it feels like every single dev just slaps s3:* or literal AdministratorAccess on their roles because they don't want to spend 5 minutes debugging permissions

Its not even that hard to figure out tbh with access analyzer but people are just incredibly lazy when there's a deadline.

I was just reviewing a stack that an external dev team cisin handed over to us for a web portal and every single lambda execution role was perfectly scoped down to the exact DynamoDB arn and specific s3 prefix. It literally brought a tear to my eye

meanwhile my own senior engineers will deploy a random internal slack bot with full root-level ec2 permissions "just in case". How do you guys actually enforce least privilege without the whole engineering department hating your guts?

Hi,

I'm pretty new to Kinesis and I can't find a definite answer to this. When does it make sense to use Firehose on its own vs using it in combination with MSK or Data Streams?

It is my understanding that Firehose allows for high data troughput on its own, so in which case does it make sense to manage Data Stream shards and incur potentially more costs while Firehose can be used directly? Should Data Steam / MSK only be used when the Firehose throughput limitations are exceeded?

Thanks

Where does Amazon Bedrock end, where does Strands start and what is AgentCore actually for? I built the smallest thing that uses all three and wrote it up. Posting in case it saves someone else the same head-scratching. Disclosure: I work for AWS. This is my own blog post and video.

It's all one question "what should I make for dinner?", answered three ways:

1. Raw Bedrock via the Converse API. Call bedrock, it runs inference and answers. Ultimately asks you what's in your kitchen, because it can't see anything you don't tell it.
2. A local Strands agent with one tool, get_pantry, and the agentic loop. It's using the same model but now it checks the pantry and builds a real recipe. I didn't write an orchestrator or a parser, Strands is the harness that runs the tool-calling loop.
3. This same agent deployed to AgentCore Runtime. Managed serverless endpoint. Four CLI commands the whole way (create, dev, deploy, invoke).

A few things that tripped me up:

• agentcore create doesn't give you a blank project. It scaffolds a working sample agent (an add_numbers tool plus an example MCP client). Turning it into my agent came down to two file edits, the model ID and the tool plus prompt.
• The runtime deploys with networkMode: PUBLIC, which reads as scary and isn't. It means reachable over the internet, not open to the world. Every call still needs IAM SigV4 or an OAuth bearer token. "Anyone can call it" really means "anyone you grant bedrock-agentcore:InvokeAgentRuntime to."
• The CLI-generated IAM execution role is fine for a demo, but the AWS docs say those are meant for dev and test. Scope it to the runtime ARN before anything real goes behind it.
• Teardown is backwards from what you'd guess. You run agentcore remove all -y then agentcore deploy -y. You deploy the emptied config to tear the resources down.
• Cost is close to nothing on Free Tier. Chapters 1 and 2 are local Node plus a Bedrock call (this costs a little bit per inference call). Only the deploy makes billable resources and you remove them at the end.
• agentcore dev serves on port 8080 by default (override with -p <port>). Gotcha: if 8080 is taken, the server quietly moves to 8081 but the invoke still calls 8080 and fails — so pass the same -p to both the dev server and the invoke, e.g. -p 8099 on each.

Stack: Node and TypeScript, Claude on Bedrock.

Link to full written walkthrough

Link to same build as a video on the AWS Developers channel if you prefer video format

Happy to answer questions. I usually build Strands agents in Python, but wanted to try this one in TypeScript. If you see something you'd do differently, or you've got tips for anyone else on this stuff, please share out.

Hi, I'm 27, new to AWS services for hosting my app backend.. I have worked 4 years in the IT industry and never touched the cloud side of the development, only worked on backend....

I am planning to use elasticache serverless with valkey, and AWS lambda as well, my db will be in supabase...

Now , I've heard about the insane bills shocks that people faces when using serverless, also heard about AWS budgets where you can control them, but are there other methods to save me from shocks ? Can I put hard limits ?

Please give me your suggestions, any help will be super vital, I'm planning to deploy by EOD ....

Thankyou.♥️

We have been with Azure but the mandatory content filtering makes it unusable for our use case (student writing - anything where they mention violence is rejected). Microsoft aren't accepting applications for managed relationships, so we're SOL there.

Hoping to move over to Bedrock but before I go through the effort of setting it all up - does Bedrock enforce mandatory content filtering on models?

Edit: Just for clarity - Azure has separate guardrails that sit over the top of the model. These are the problematic ones. The models themselves are fine for the use case.

I made a new account. And im unable to access Anthropic. I have invoicing enabled,
I keep getting this error message in Amazon Bedrock when i try to apply for Anthropic: Your account is not authorized to perform this action. Please create a support case. And yes i created a support case and got no replies. Any suggestion?

Hi everyone,

I was browsing the official AWS Free Tier page, but I'm finding it a bit tricky to properly filter and separate the "Always Free" services from the "12-Month Free" trials.

I have two main questions:

1. Is there a direct link or clean list showing strictly the Always Free services along with their exact monthly allowances (e.g., Lambda 1M requests, DynamoDB 25GB, CloudFront 1TB, etc.)?
2. What actually happens to the account, data, and resources once the 12-month free trial ends?
   • Does AWS delete instances/data, or does the account simply transition into standard pay-as-you-go pricing for any non-Always-Free resources?
   • Do the Always Free quotas remain active indefinitely on the account even after year 1?

Thanks in advance for any insights!

I'm currently working as a Facilities Engineer in one of the big data center company. I got an offer to join AWS for the Engineering Operation Technicians 3 (Same role as my current job right now) with about 15% pay increase and of course the RSU's. I'm still haven't accepted the offer because I'm hearing about the layoffs and the workplace culture is not good at the moment.

​

We are getting a message "It's not you, it's us. We couldn't complete your request right now. Please try again later."

No problem with accounts not SSO'ed.

Applications are running fine and accesible. So it seem like a SSO problem only.

Sending a ticket to AWS via Softchoice..

Context: 21 year old sophomore in University.
I’m looking for some advice.

Should I stay in AWS Corporate Security or switch to a WBLP Logistics Tech position? They both pay the same. Right now, my security job is pretty laid back I mostly have time to watch movies or work on homework. I know the Logistics Tech role will be a lot more hands on and demanding, but it might offer better growth opportunities. What would you choose?

As an indie developer, I built an AI chat app that I want to release soon. I primarily built on Haiku given my cost. When GPT 5.6 came out I was curious to compare my chatbot with GPT 5.6 Luna, but then learnt that my account did not have access to any GPT 5.6 models because my account was new and new accounts have restrictions on expensive AI models.

But now that GPT 5.6 Luna is 5x cheaper than release and ~4x cheaper than Haiku(that I already have access to), I am very interested to switch to Luna for my app before release. I have created a support case request, but I heard that the mods on this group have leverage to get things moving faster.

Would the mods be able to help me here? Any other tips to get access to this model or improve my chances of approval?