AWS Lambda is the go-to service when moving from always-on EC2 to serverless, event-driven compute. It’s simple, cost-effective, and scales automatically.
But Lambda isn’t limitless. As workloads grow more complex, compute-heavy, or long-running, Lambda’s hard limits force teams to look elsewhere. The key is to maintain scale-to-zero economics while choosing the right service for the workload.
Use Case: Genomics Data Processing Pipeline
A biotech company started with Lambda to handle DNA sequencing jobs
Ran only a few times per day.
Each job lasted under 10 minutes.
Required modest memory (~2 GB).
As research grew
Some jobs stretched to hours.
Memory demand ballooned to 30 GB.
GPU acceleration became critical for ML models.
Specialized libraries exceeded Lambda’s packaging and storage limits.
Solution
Preprocessing jobs remained on Lambda.
GPU-heavy analysis moved to AWS Batch with Spot Instances.
Containerized intermediate steps ran on Fargate one-off tasks.
This hybrid approach maintained cost efficiency without reverting to idle EC2 instances.
Lambda’s Hard Limits
Constraint | Limit | Impact |
|---|---|---|
Runtime | 15 minutes | Longer jobs fail. |
Memory | 10 GB (~6 vCPUs) | Heavy jobs won’t fit. |
Ephemeral storage | 10 GB max | Limits data-heavy workloads. |
GPU support | None | Blocks ML, rendering, HPC. |
Bursty workloads | Cold starts + concurrency quotas | May affect performance. |
Cost-Optimized Alternatives
Service | Best For | Why It Works |
|---|---|---|
Lambda | Short, light jobs (<15 min, ≤10 GB mem) | Cheapest, simplest serverless option. |
Fargate one-off task | >15 min jobs, higher CPU/memory, custom runtimes | Serverless containers, flexible resources. |
AWS Batch | Long jobs, GPUs, HPC, pipelines | Managed job scheduler, EC2 Spot savings. |
EC2 ASG (min=0) | Legacy or VM-based apps | Scale-to-zero pattern, full OS control. |
Decision Cheat Sheet
Requirement | Best Choice | Why |
|---|---|---|
<15 min, ≤10 GB, no GPU | Lambda | Easiest & lowest cost. |
>15 min, high memory/CPU | Fargate one-off task | Serverless containers. |
GPU, HPC, large job arrays | AWS Batch | Specialized compute with Spot savings. |
Legacy VM workloads | EC2 ASG min=0 | Full control + cost savings. |
AWS Exam Insights
“Runs a few times a day” → Event-driven → Lambda.
“Needs more memory/runtime than Lambda supports” → Fargate or Batch.
“GPU” → AWS Batch with GPU instances (Spot if possible).
“Legacy app” → EC2 with scale-to-zero pattern.
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