Vercel has added Amazon S3, together with S3 Tables and S3 Vectors, to its Marketplace and v0 offering, allowing developers to create and attach storage resources without leaving the Vercel dashboard. For AI engineers, cloud/platform engineers, DevOps/SRE staff, and security practitioners, the change means a single provisioning flow, automatic injection of short‑lived credentials, and a consolidated AWS bill for the entire stack.
Direct Provisioning of S3 Resources
From the Vercel UI, users navigate to the Storage tab, select AWS, choose the desired bucket type (general purpose, table, or vector), pick a region, and confirm creation. The platform provisions the bucket in seconds and automatically populates environment variables such as AWS_REGION, AWS_ROLE_ARN, AWS_RESOURCE_ARN, and a bucket‑specific name (S3_BUCKET_NAME, S3_TABLE_BUCKET_NAME, or S3_VECTOR_BUCKET_NAME). These variables are pulled into the project with vercel env pull .env.local, ensuring that code can reference them without hard‑coding secrets.
Using S3 Buckets from Vercel Functions
Vercel Functions can access the bucket through the AWS SDK, using the awsCredentialsProvider from @vercel/functions/oidc to obtain temporary STS credentials. The following snippet shows a minimal POST handler that stores an uploaded file in a general purpose bucket:
import { S3Client, PutObjectCommand } from "@aws-sdk/client-s3";
import { awsCredentialsProvider } from "@vercel/functions/oidc";
const s3 = new S3Client({
region: process.env.AWS_REGION,
credentials: awsCredentialsProvider({ roleArn: process.env.AWS_ROLE_ARN })
});
export async function POST(request) {
const formData = await request.formData();
const file = formData.get("file");
await s3.send(new PutObjectCommand({
Bucket: process.env.S3_BUCKET_NAME,
Key: file.name,
Body: Buffer.from(await file.arrayBuffer()),
ContentType: file.type
}));
return Response.json({ success: true });
}
Listing and retrieving objects uses the same client with ListObjectsV2Command and GetObjectCommand.
Analytics and Vector Stores via S3 Tables and Vectors
S3 Tables expose fully managed Apache Iceberg tables that automatically handle compaction and maintenance, making them suitable for large‑scale analytics. Because they conform to the Iceberg REST catalog API, any compatible engine—such as DuckDB—can query them directly from a Vercel Function. S3 Vectors provide a purpose‑built store for high‑dimensional embeddings used in retrieval‑augmented generation or semantic search, with a cost claim of up to 90 % reduction compared to generic object storage.
Operational and Security Considerations
The integration relies on an OIDC token exchange with AWS Security Token Service to produce short‑lived credentials scoped to the specific resource ARN. No static access keys are persisted, reducing the attack surface associated with credential leakage. All resources are created under a single AWS account, simplifying billing and cost attribution, but also concentrating permissions; practitioners should verify that the role defined by AWS_ROLE_ARN follows the principle of least privilege for the bucket or table it accesses.
Related CloudNinjas coverage: AWS.
What This Means For Practitioners
Adopt the Marketplace flow for any new S3 requirement to eliminate manual console steps and to keep credential handling within Vercel’s OIDC pipeline. Review the automatically generated IAM role to ensure it only permits the actions needed by your functions (e.g., s3:PutObject for upload endpoints). For analytics workloads, prototype DuckDB queries against S3 Tables directly from Vercel Functions to validate performance before scaling. Finally, monitor the unified AWS bill for unexpected growth, especially when enabling S3 Vectors, and adjust retention policies as needed.
