4 Serverless Computing Challenges and Solutions: What I'd Do Differently"
Serverless computing promises scalability and cost savings, but real-world implementation often reveals unexpected obstacles. This article examines four common serverless challenges and their practical solutions, drawing on insights from developers who have tackled these issues in production environments. Learn how to address checkout lag, function limits, latency inconsistencies, and timeout problems with proven technical approaches.
Adopt Containers to Eliminate Checkout Lag
The biggest challenge that I faced with serverless computing (AWS Lambda) was cold starts. When my API sat idle for a few minutes, the next person to click "checkout" had to wait 3 to 5 seconds for the server to wake up. This lag was a silent killer. My customer conversions dropped by 22% because people simply got tired of waiting. To stop the delay, I implemented a feature called Provisioned Concurrency. I stabilised the system using a few steps. I paid about $12/month to keep 10 instances "awake" at all times. This tactic brought my response time down to a consistent 180ms. I also switched to a lightweight Node.js setup and removed unnecessary code to make the functions load even faster. The improvement was massive. My high-end latency dropped from 3.2 seconds to just 240ms.
Knowing what I know now, I wouldn't use Lambda for a customer-facing checkout page. I would use containers like ECS Fargate from the start.
Decentralize With Queues to Break Limits
We built a centralized scheduling feature to stop and start EC2 and RDS instances across hundreds of accounts for cost savings. It had to run from a central account, scale independently from the rest of our platform, handle any number of accounts and resources, keep costs under control, and monitor every stop/start action through completion.
We evaluated AWS Batch and Lambda early on. Batch would have required scaling underlying compute based on the number of resources per account, plus building and maintaining a Docker image. That added cost and operational overhead we didn't need. Lambda was cleaner, but the 15-minute timeout became a constraint. If we processed large numbers of resources sequentially and waited for each action to complete, we risked timing out or burning compute cycles just waiting.
Our first Lambda design batched resources by account and executed them together, but that didn't hold up for large environments. The shift came when we stopped grouping by account and treated each resource as the same unit of work. We moved to a distributed model using multiple Lambda functions connected with SQS.
One Lambda gathers scheduled resources and sends them to a processing queue.
A second Lambda consumes messages in batches of 10, executes stop/start actions in parallel threads, and pushes results to a status queue.
A third Lambda checks status. If a resource is still transitioning, it re-queues the message with an exponential backoff using SQS visibility timeouts.
The key insight was letting SQS handle the waiting period instead of holding a Lambda invocation open. That allowed us to scale to thousands of resources, avoid Lambda timeout limits, and eliminate the need to manage container images or pay for idle compute time.
If we were to do it again, we would skip designing around a single automation endpoint and jump straight into a decentralized approach that's more event driven.
Prioritize Consistent Latency With Tiered Policy
A specific serverless challenge I hit was cold start latency variance on a user facing API. P95 was fine most of the day, then after quiet periods or sudden bursts we would see a noticeable spike tied to the Lambda initialization phase (container provisioning, runtime init, code loading, dependency resolution). That made the product feel randomly slow even though average latency looked healthy.
How we overcame it
Proved it was cold starts, not "the code" We instrumented and watched the INIT duration in logs and correlated it with the latency spikes. AWS calls out INIT duration as the signal to monitor when diagnosing cold starts.
Reduced initialization work We moved heavy imports and framework bootstrapping out of the hot path where possible, trimmed dependency size, and removed anything that forced large downloads or slow startup.
Used the right AWS feature for the requirement For strict latency endpoints, we used Provisioned Concurrency to keep a small number of execution environments ready so requests did not pay the initialization penalty. AWS describes Provisioned Concurrency as pre initializing function environments and keeping them warm for consistent performance.
For functions with long one time initialization (common with heavier runtimes), we evaluated SnapStart, which snapshots the initialized execution environment at publish time and restores from that snapshot on invoke to reduce cold start time without provisioning resources.
We learned with SnapStart SnapStart can copy initialization state across many execution environments, so anything that assumes uniqueness created during init can bite you. AWS explicitly flags "uniqueness" and connection state as compatibility considerations, and recommends handling uniqueness after initialization.
What I would do differently now
- Start with a tiered latency policy on day one
- Not every function needs the same guarantees. I would classify endpoints into "interactive user facing" vs "background" and decide up front which ones justify Provisioned Concurrency costs vs which can tolerate occasional cold starts. Source
- Design initialization to be snapshot safe even if not using SnapStart yet
- I would avoid generating IDs, tokens, or pseudo random seeds during init, and I would treat network connections created during init as "must be validated and possibly re established" on invoke. That makes later adoption of SnapStart less risky. Source
- Make cold start observability a first class SLO
Process Images Individually to Avoid Timeouts
We built a client's image processing system using AWS Lambda and hit the 15-minute execution timeout when processing large batches of high-res images. The function would timeout halfway through and we'd lose all progress because serverless functions are stateless.
Fixed it by breaking the work into smaller chunks and using SQS queues to process images individually instead of in batches. Each Lambda handled one image, finished in under a minute, and failures only affected single images instead of entire batches.
What I'd do differently is design for Lambda's constraints from day one instead of building like it's a traditional server then scrambling to refactor when timeouts started happening in production.
