5 Common AWS Errors New Developers Hit (And How to Fix Them)

Table of Contents

• Intro
• AccessDenied
• InvalidClientTokenId
• ThrottlingException
• ResourceNotFoundException
• ValidationException
• Patterns Behind the Errors

Intro

You take pride in writing clean code...
You've passed all the technical interviews...
You've shipped features locally without breaking anything...
Then you attempt to deploy in a real AWS environment, and quickly get humbled.

If you’ve read my previous blog post about starting your first developer job in an AWS environment, then you know the moment I’m referring to. You log into the console at work and notice resources that existed long before you arrived, with names that look autogenerated and dashboards that are lighting up.

Then after a few weeks of getting comfortable, you try to deploy a feature or call an API for the first time, and you're stopped in your tracks by an AccessDenied error message.

You feel confused at first because nothing seems obviously wrong. Your code runs, your logic is sound and yet AWS still tells you, "you can't pass through!". That's when you learn the lesson that AWS operates on very strict rules around identity, permissions, configuration, quotas, and validation. It is extremely literal so if something does not meet its expectations exactly, it will reject the request.

Every developer, no matter how experienced, will stumble on AWS error messages so you're not going to avoid them entirely. However, in this blog, we're going to help you understanding what these error messages mean, how to both resolve them and reduce the chances of stumbling into them in the first place.

Now let's walk through these 5 common errors messages, AccessDenied, InvalidClientTokenId, ThrottlingException, ResourceNotFoundException and ValidationException.

AccessDenied (Nope! You're not allowed to do that!)

If there's one error message that will shape your first year working with AWS, it's the AccessDenied error message.

This error does not necessarily mean your code is syntactically wrong, or your logic is flawed. Actually, it means AWS fully understood what you asked it to do but intentionally rejected the request because your identity is not authorized to perform that action.

Most of the time, this traces back to Identity and Access Management. You might be trying to read from an S3 bucket, or invoke a Lambda function, or attach a role to a resource, but the IAM principal you're currently operating as does not have permission to perform that specific API action on that specific resource.

In more subtle cases, you might be in the wrong AWS account entirely, or targeting the wrong region and sometimes the missing permission is not obvious, such as iam:PassRole when a service needs to assume a role on your behalf.

When I first started working in real AWS environments, I thought an AccessDenied error message meant I'd made a personal mistake because I assumed I'd configured something incorrectly. Then, I later learned it was a deliberate guardrail my manager had set to limit what I could do in a specific AWS account, which was a good thing. In case you didn't know, most developers will never have full access to the AWS environment at their jobs.

When resolving an AccessDenied error, your first step is to confirm that you’re actually supposed to have access. Once that’s clear, your next question should be, "who am I right now?" In AWS, access is entirely based on identity, so before you assume the problem is a missing permission, you need to verify which principal is actually making the request. One of the fastest ways to answer that question is to ask AWS directly using the AWS CLI in your terminal:

aws sts get-caller-identity

That command returns the account ID and ARN of the user or role you’re currently authenticated as, immediately telling you whether you’re in the wrong AWS account, assuming an unexpected role, or using a different profile than you thought.

From there, you can ask more specific questions like, "Am I using the correct IAM role?", "Am I authenticated through the expected SSO profile?", or "Am I running this from my local machine, from a CI/CD pipeline role, or from within a Lambda function?" Each of those execution contexts has a completely different permission set, and many AccessDenied errors often result from operating in a context you didn’t intend.

From a reactive standpoint, the fastest way to debug AccessDenied error message is to read the full message carefully. AWS often includes the exact action that was denied and the ARN of the resource involved that tells you precisely which permission is missing. From there, you can inspect the attached IAM policies for your user or role and verify whether that action is allowed on that resource. If you're working in an organization with Service Control Policies, you may also need to check whether a higher-level restriction is blocking the action even if your local role appears correct.

So how can we limit AccessDenied errors so they don't slow you down?

One practical way is to design with least privilege intentionally instead of retroactively. When you're creating a new role for a service, take a few extra minutes to identify exactly which API calls it will need and grant those explicitly. If you're using infrastructure as code, strive to define IAM policies alongside your compute resources so permissions evolve with your application rather than lag behind it.

Another proactive habit is verifying your execution context before running critical operations. Make sure you get comfortable checking which AWS account and region you're currently in and confirming which IAM role your CLI profile or deployment pipeline is using because many AccessDenied issues are a result of operating in an unexpected environment.

InvalidClientTokenId (Yo! I don't know who you are)

InvalidClientTokenId usually shows up when you're working with the CLI, an SDK, or running code from your local environment. At first glance, this error message reads like something is fundamentally broken, but in reality, it's really straight forward.

It means the credentials attached to your request are either invalid, expired, or not the correct identity you think you're using.

Every AWS request is cryptographically signed so when you authenticate, AWS provides credentials that allow you to generate a signature proving who you are. That signature includes your access key and a timestamp and if the token is expired, malformed, revoked, or tied to access a different account than expected, AWS rejects the request immediately before even checking permissions. In other words, this is not about what you're allowed to do but whether AWS trusts who you are.

You’ll often see it surface in even the simplest SDK call:

import boto3

s3 = boto3.client("s3")
response = s3.list_buckets()
print(response)

If your credentials are expired or incorrect, AWS will respond with something like:

An error occurred (InvalidClientTokenId) when calling the ListBuckets operation:
The security token included in the request is invalid.

Notice that this happens before any permission evaluation because the request fails at the identity layer.

Early in my career, I lost more time to this error than I want to admit. I would question my app logic, inspect config files, and re-run deployments but eventually, I realized that whenever AWS complains about tokens, client identity, or security credentials, the problem is almost always contextual.

For a proactive debugging path, first confirm which AWS profile is currently active. If you're using AWS SSO, attempt to refresh your session. If you're using long-term credentials, confirm they haven't been rotated or invalidated and check for environment variables such as AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, or AWS_PROFILE that may be overriding your expected configuration. If you're inside a container or CI/CD pipeline, verify which role is being assumed and whether it is still valid.

Another common scenario, is accidentally switching accounts. You might have authenticated successfully, but into the wrong AWS account. From the perspective of AWS, your token is valid, but not for the resources you're trying to access.

So before running critical commands, know exactly which identity you are operating as, be explicit about profiles instead of relying on defaults and in multi-account environments, make the current account visible in your prompt or logging output. As for CI/CD pipelines, try to surface the assumed role clearly in logs so it's not ambiguous which identity is making requests.

Lastly, always keep in mind that credential lifetimes are temporary credentials designed to expire so you'll intentionally need to build your workflows around refreshing sessions and not assume they'll last indefinitely.

ThrottlingException (Dude! Slow down!)

When you see a ThrottlingException or TooManyRequestsException error message, understand that AWS is enforcing limits to prevent you from overwhelming a system.

Every AWS service operates with defined quotas and internal protection mechanisms and these limits exist to preserve stability, fairness, and predictable performance across millions of customers. When your request rate exceeds what the service can handle at that moment for your account, AWS responds by throttling you.

New developers often hit this error message unintentionally because it's easy to write code that scales requests unintentionally, like a loop that calls an API hundreds of times per second or a Lambda function that scales concurrency faster than expected. In your local development, you rarely think about rate limits because everything runs in-process but in distributed systems, request pacing is an important part of the architecture.

When a ThrottlingException or TooManyRequestsException error message appears, the first question to ask is whether the throttling is burst-based or sustained. If it resolves quickly, you likely experienced a short spike but if it persists consistently, you may be exceeding configured quotas or overloading a particular resource.

The immediate instinct is often to retry immediately, but that can actually make the problem worse. The fix is to control retry logic using exponential backoff so requests slow down progressively instead of hammering the service.

Most AWS SDKs include retry logic automatically, but you should understand how it works and configure it intentionally when necessary. For example, in Python using Boto3, you can define retry behavior like this:

from botocore.config import Config
import boto3

retry_config = Config(
    retries={
        "max_attempts": 10,
        "mode": "standard"  # or "adaptive"
    }
)

dynamodb = boto3.client("dynamodb", config=retry_config)
response = dynamodb.list_tables()
print(response)

The standard mode applies exponential backoff, while adaptive adds client-side rate awareness. In short spikes, this can mean the difference between graceful recovery and cascading failure. When throttling persists consistently, however, it may indicate that you are exceeding a quota or that concurrency needs to be adjusted. Observability will play a critical role here for have visibility into what's actually happening. Some key metrics you'd want to monitor are, request counts, error rates, and service quotas so you understand whether you’re dealing with bursts or sustained pressure.

ResourceNotFoundException (That doesn’t exist. At least not here)

Few things are more confusing than seeing a resource clearly visible in the AWS Console while your application insists it doesn’t exist. When you encounter a ResourceNotFoundException, the instinct is often to assume AWS is contradicting itself. It usually isn’t.

AWS resources are almost always scoped to a specific region and account. If your Lambda function lives in us-east-1 and your CLI or SDK is configured for us-west-2, AWS is absolutely correct when it says the resource does not exist, because it does not exist in that region.

This is easy to miss in code. For example:

import boto3

lambda_client = boto3.client("lambda", region_name="us-west-2")

response = lambda_client.get_function(
    FunctionName="my-production-function"
)

If that function actually lives in us-east-1, this call will fail with ResourceNotFoundException. Simply being explicit about region can eliminate ambiguity:

lambda_client = boto3.client("lambda", region_name="us-east-1")

The same applies to account boundaries. A resource in one AWS account is completely invisible to another unless explicitly shared through mechanisms like cross-account access.

There are other subtle variations of this issue as well, for example, the ARN might be malformed or the resource ID contains a small typo. Other more nuanced causes are, a resource might been deleted and recreated with a slightly different identified or an application might be running in an execution context that doesn't have visibility into the resource even though it exists.

When you encounter the ResourceNotFoundException error message, resist the urge to immediately question AWS but instead, verify your context. Confirm the AWS account you're operating in, confirm the region config in your SDK or CLI, log the exact ARN or resource identifier being used in the request and check environment variables and configuration files to confirm they are pointing to the environment you expect.

One of the fastest debugging tips here is printing out the region and account identity at application startup. When something fails, you want immediate clarity about where you're operating from. Many “missing resource” errors are simply requests being sent to the wrong environment.

Additionally, try not to rely on defaults and ambiguous configurations that changes between development and production. In team environments, document which accounts and regions are responsible for which workloads and name resources in ways that make their environment obvious. When using multiple accounts, consider standardizing environment variables or deployment conventions so region and account targeting are always intentional.

ValidationException (Your input doesn’t meet the rules!)

ValidationException error messages show up across many AWS services and often feels frustrating because it can appear generic at first. However, they mean your request did not satisfy one or more of the service’s defined constraints.

AWS APIs operate on contracts and every operation has required parameters, allowed values, length limits, formatting rules, and structural expectations. If any part of your request violates that contract, the service rejects it immediately.

This could mean a required field is missing or an ARN is malformed, a numeric value exceeds an allowed range or even something as subtle as incorrect casing in an enumerated value. For example, consider creating a DynamoDB table:

import boto3

dynamodb = boto3.client("dynamodb")

response = dynamodb.create_table(
    TableName="Users",
    AttributeDefinitions=[
        {
            "AttributeName": "userId",
            "AttributeType": "STRING"  # Incorrect value
        }
    ],
    KeySchema=[
        {
            "AttributeName": "userId",
            "KeyType": "HASH"
        }
    ],
    BillingMode="PAY_PER_REQUEST"
)

In this case, STRING is invalid. DynamoDB expects S for string types. That small mismatch causes a ValidationException because the input does not match the expected schema.

A common mistake new developers make is not slowing down enough to read what the error message is clearly telling them. AWS is usually very explicit about what went wrong, like whether a required field is missing, a value doesn’t match the expected pattern, a data type is incorrect, or a maximum length constraint was exceeded.

To solve this error, you can examine the exact request being sent and compare it line by line with the official API documentation, confirm that required parameters are present and verify formats for ARNs and resource identifiers, double-check enum values and numeric limits and if you're using an SDK, log the final constructed request object to confirm it matches your expectations.

I believe it's worth checking whether your configuration has been modified by environment variables or defaults that differ across environments. A configuration that works in development may fail in production if a required parameter is missing or structured differently.

You should also treat infrastructure definitions and service configurations as strongly typed artifacts rather than loosely structured text. I strongly recommend using infrastructure-as-code tools that perform validation before deployment and linters or schema validation tools where possible to validate input early so malformed data never reaches AWS APIs.

Patterns Behind the Errors

As a new developer at your job, you might feel like you're constantly unexpectedly running into AWS error messages, but understand that this is all part of becoming more experienced in the cloud.

Overtime, you'll begin to notice that most error messages fall into the predictable categories of identity and authorization issues, permission boundaries, scope mismatches, validation failures, or scale constraints.

As you gain experience, your debugging process will become much calmer and structured instead of reactive and scattered because you'll be able to classify error messages into one of those buckets within the first few seconds.

I am curious to know though, which one of these AWS error messages trip you up the most or is the most frustrating to see?

Lastly, if you're looking to dive deeper in your learning journey, explore these FREE AWS resources:

• AWS Free Tier Sign Up
• AWS Builder Center Sign Up
• AWS re:Post Q&A Community Knowledge Center
• Intro to Identity and Access Management (IAM)
• Intro to AWS Billing Services and Cost Management

Good luck!

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If you've got this far, thanks for reading! I hope it was worthwhile.