boto3 Basics

10 examples to get you started with Cloud SDKs - 7 basic and 3 intermediate.

Prerequisites

uv venv && source .venv/bin/activate
uv pip install "boto3>=1.35"
aws configure --profile dev  # or export AWS_PROFILE=dev

• Python 3.14.0 with boto3 installed in a venv.
• AWS credentials via ~/.aws/credentials, environment variables, or instance/task IAM role.

Basic Examples

1. Create a Session

boto3.Session is the entry point for region, profile, and credential resolution.

import boto3
 
session = boto3.Session(profile_name="dev", region_name="us-east-1")
print(session.region_name)

• One session per logical AWS account/region context in scripts.
• profile_name maps to ~/.aws/credentials sections.
• Prefer explicit region_name - global clients still need a signing region.

Related: Credential & Session Patterns - least privilege setups

2. Low-Level Client

Clients expose the raw AWS API - one method per API action.

import boto3
 
s3 = boto3.client("s3")
resp = s3.list_buckets()
print([b["Name"] for b in resp.get("Buckets", [])])

• Clients return plain dicts matching AWS API shapes.
• Pagination is manual unless you use paginators (see intermediate examples).
• Best for full API coverage and newest operations.

Related: S3 - uploads, downloads, presigned URLs

3. Resource Interface

Resources provide an object-oriented layer over clients.

import boto3
 
s3 = boto3.resource("s3")
for bucket in s3.buckets.all():
    print(bucket.name)

• Bucket, Object, Table classes wrap common workflows.
• Not every API operation has a resource equivalent - fall back to clients when needed.
• Resources still use the same credential chain as clients.

Related: S3 - streaming and object operations

4. Region and Endpoint Configuration

Pin region to avoid surprising cross-region latency or wrong partition behavior.

import boto3
 
session = boto3.Session(region_name="eu-west-1")
dynamodb = session.client("dynamodb")
print(dynamodb.meta.region_name)

• meta.region_name confirms where requests sign.
• Use endpoint_url only for LocalStack, VPC endpoints, or custom gateways.
• Multi-region apps create one session per region, not one global client.

Related: Retry, Pagination & Throttling - robust calls at scale

5. Credential Chain Order

boto3 resolves credentials automatically when you do not pass keys in code.

import boto3
 
session = boto3.Session()
creds = session.get_credentials()
print(creds.method if creds else "no credentials")

• Order: env vars → shared config/credentials → IAM role (EC2/ECS/Lambda).
• AWS_PROFILE selects a named profile without hardcoding in source.
• Never commit access keys - rotate immediately if leaked.

Related: Secrets Manager & SSM - runtime secret fetch

6. Error Handling with ClientError

AWS errors arrive as botocore.exceptions.ClientError with structured codes.

import boto3
from botocore.exceptions import ClientError
 
s3 = boto3.client("s3")
try:
    s3.head_bucket(Bucket="definitely-missing-bucket-xyz")
except ClientError as exc:
    code = exc.response["Error"]["Code"]
    print(code)

• Branch on Error.Code (404, NoSuchBucket, AccessDenied) not message strings.
• response dict mirrors AWS XML/JSON error payloads.
• Log request ID from ResponseMetadata for AWS support tickets.

Related: Cloud SDK Best Practices - safe error handling rules

7. Reuse Clients in Workers

Create clients once per process, not per request in hot loops.

import boto3
 
_SESSION = boto3.Session()
_SQS = _SESSION.client("sqs")
 
def send_message(queue_url: str, body: str) -> None:
    _SQS.send_message(QueueUrl=queue_url, MessageBody=body)

• Client creation is relatively expensive; module-level reuse is fine for scripts and workers.
• Thread safety: clients are generally thread-safe; resources are not always.
• In Lambda, create clients outside the handler for connection reuse across invocations.

Related: SQS & SNS - queue and topic patterns

Intermediate Examples

8. Paginators for Large Result Sets

Stop hand-rolling NextToken loops - use built-in paginators.

import boto3
 
s3 = boto3.client("s3")
paginator = s3.get_paginator("list_objects_v2")
for page in paginator.paginate(Bucket="my-bucket", Prefix="logs/"):
    for obj in page.get("Contents", []):
        print(obj["Key"], obj["Size"])

• Paginators handle tokens and page sizes consistently.
• Set PaginationConfig={"MaxItems": 100} to cap dev scripts.
• Pair with Retry, Pagination & Throttling for throttled APIs.

Related: DynamoDB - query pagination

9. Waiters for Resource Convergence

Wait until AWS finishes asynchronous creation before dependent steps.

import boto3
 
ec2 = boto3.client("ec2")
waiter = ec2.get_waiter("instance_running")
instance_id = "i-0123456789abcdef0"
waiter.wait(InstanceIds=[instance_id])

• Waiters poll with backoff until success or max attempts.
• Prefer waiters over time.sleep guesses in provisioning scripts.
• Configure WaiterConfig={"Delay": 5, "MaxAttempts": 40} for long-running resources.

Related: Provisioning Cloud Resources - bootstrap workflows

10. botocore Config for Retries and Timeouts

Tune retries and timeouts for production traffic patterns.

import boto3
from botocore.config import Config
 
config = Config(
    retries={"max_attempts": 10, "mode": "standard"},
    connect_timeout=5,
    read_timeout=60,
)
s3 = boto3.client("s3", config=config)

• mode: adaptive helps under sustained throttling; standard is predictable default.
• Separate connect vs read timeouts - large S3 downloads need higher read timeout.
• Apply the same Config object to every client in a service class.

Related: Retry, Pagination & Throttling - throttling strategies

Stack versions: This page was written for Python 3.14.0 (stable 3.14, maintenance 3.13), FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+. WTI5a1pXZDFhV1JsY3k1cGIzeGpaMmx2TmpVMWZESXdNall3Tnc9PQ==