Jan 04, 2026 • 5 min read

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Building a Production-Ready Computer Vision Pipeline on AWS with Terraform

A complete guide to designing a scalable, secure computer vision pipeline on AWS using Terraform, Rekognition, and real-time video analytics.

Rahul Ladumor

Real-time video analytics sounds exciting until you try to build it.

Once you move beyond demos, you quickly run into very real challenges: large video files, live streams, AI inference at scale, strict security requirements, and cloud costs that can spiral if the architecture isn’t right. Most tutorials stop early. This blog doesn’t.

What follows is a clear, production-focused walkthrough of a computer vision pipeline on AWS, provisioned with Terraform. The goal is simple: explain how it works, why each piece exists, and how it fits together, without hype or unnecessary complexity.

Who This Is For

This article is written for people who build real systems:

Cloud and DevOps engineers
ML engineers working with video data
Architects responsible for security, scale, and cost
Anyone looking for a practical reference, not a toy example

You don’t need to know every AWS service in advance. Each section focuses on intent first, then implementation.

The Core Problem

Video analytics combines several hard problems into one system:

Video is heavy and slow to process
Live streams require event-driven design
AI inference must scale automatically
Security and compliance are mandatory
Costs must stay predictable

The purpose of this project is to solve those problems with a clean, understandable architecture that can actually run in production.

System Architecture: One Layer at a Time

Before diving into data flow or code, it helps to understand the structure of the system.

Each layer has a single responsibility:

Ingestion brings video into the system
Processing handles transcoding and AI inference
Storage keeps results queryable and affordable
Alerts react to important events in real time

This separation keeps the system flexible and easy to operate.

Data Flow: Following a Single Video

Now let’s follow one video through the pipeline.

This flow is intentionally event-driven. There’s no polling and no central orchestrator trying to do everything. Each step reacts to the previous one, which makes the system easier to scale and easier to debug.

Why Transcoding Comes Early

Raw video isn’t friendly to AI workloads. Different formats, resolutions, and bitrates slow processing and increase cost.

Using MediaConvert early in the pipeline gives you:

Consistent formats
Predictable frame sizes
Faster inference
Lower compute overhead

It’s not the most visible part of the system, but it’s one of the most important.

AI Processing: Managed Services First, Custom Models When Needed

The pipeline uses two complementary approaches:

Managed computer vision for common tasks
- Object detection
- Face detection and recognition
Custom models for domain-specific logic
- Industry-specific detection
- Specialized inference

Lambda coordinates these calls and keeps the workflow simple. You get flexibility without turning the system into a heavy ML-ops platform.

Storing Results Without Overengineering

AI output is only useful if you can retrieve and analyze it later.

Each datastore is chosen for a clear reason:

DynamoDB for fast, reliable metadata access
OpenSearch for querying and analytics
S3 for low-cost long-term storage

No single database is forced to do everything.

Alerts and Event-Driven Reactions

Real-time systems should react immediately when something important happens.

This makes it easy to send notifications, trigger workflows, or integrate with other systems, without tightly coupling components together.

Network Architecture: Secure by Default

Everything runs inside a private VPC.

The rules are simple:

Compute resources are never public
No inbound internet access to EC2
Outbound traffic is controlled
Least-privilege security groups

This setup meets most enterprise security expectations out of the box.

Deployment Pipeline: Make Changes Safely

All infrastructure changes go through Terraform and CI/CD.

Every change is validated, scanned, cost-checked, and reviewed before it reaches production. This keeps deployments predictable and boring in a good way.

Terraform Structure: Easy to Understand, Easy to Extend

This modular structure makes the codebase easier to maintain, easier to reuse, and easier for new engineers to understand.

Monitoring: Visibility Is Not Optional

You get basic but essential visibility into system health and performance, with alerts when something goes wrong. From here, it’s easy to add logs, tracing, or more advanced metrics.

Cost Awareness

Approximate monthly costs:

Environment	Estimated Cost
Development	$190 – $550
Staging	$500 – $1,000
Production	$900 – $4,000

Costs are controlled through right-sizing, spot instances, storage tiering, and cost estimation during the CI/CD process, before any deployment occurs.

Where This Architecture Fits Well

This setup works across many domains:

Security and surveillance
Retail analytics
Traffic monitoring
Manufacturing quality checks
Healthcare imaging pipelines

It’s flexible enough to adapt without a full redesign.

Final Thoughts

This project isn’t about being clever. It’s about being clear, predictable, and reliable.

The architecture is easy to reason about
Each component has a clear purpose
Security and cost are considered from the start
The system scales without becoming fragile

If you’re building computer vision workloads on AWS, this gives you a solid, realistic foundation, one you can actually run and maintain in production.

👉 GitHub Repository: