Lambda vs Fargate at Scale

Early in a system’s lifecycle, compute decisions feel interchangeable.

Lambda accelerates delivery.
Fargate offers control.
Both appear sufficient.

Under scale, their failure shapes diverge.

Concurrency limits surface.
Cold starts affect latency.
Long-running workloads strain execution limits.
Idle container capacity increases cost.

The question is not which service is better.
The question is which failure profile aligns with your workload.

We design execution layers that reflect workload characteristics:

• Serverless orchestration for burst handling
• Containerized execution for sustained or heavy workloads
• Explicit separation of control and compute
• Bounded concurrency and execution limits
• Measurable performance and cost modeling
• Clear failure-domain ownership

Execution models are chosen intentionally — not by default.

We align compute behavior with system design.

Serverless handles orchestration and short-lived tasks.

Containers manage long-running or resource-intensive workloads.

Execution layers do not manage workflow state.

Performance and cost behaviors are evaluated before production load increases.

• AI and media processing systems
• Variable or long-running workloads
• Platforms approaching scaling thresholds
• Teams evaluating cost-performance tradeoffs

• Improved performance predictability
• Reduced cost volatility
• Clear execution boundaries
• Controlled scaling behavior
• Infrastructure aligned with workload reality

Execution models define system behavior under stress. At scale, execution behavior is structural.