Your cloud migration was supposed to result in smooth deployment and better scalability. Instead, your app loads like it’s stuck in molasses. In cloud application development, this isn’t a surprising pattern. But everything is possible to fix.
In this article, we will figure out what slows down cloud apps and how to fix it.
Database Chattiness Kills Performance
When your app communicates to the database, every conversation now goes across a network instead of happening on the same machine.
The N+1 query problem causes substantial issues in the cloud:
- Your code fetches a list of records with one query.
- Then loops through each record to grab related data with separate queries.
- On-premises, each query took microseconds.
- In the cloud, every database round trip adds 1-2ms of network latency.
PlanetScale’s analysis showed that N+1 queries can be 10 times slower than properly optimized JOINs.
How to fix it:
- Replace loops with JOINs or batch queries using IN clauses to fetch related data in one trip.
- Enable eager loading in your ORM.
- Set up distributed tracing with tools like Datadog or New Relic to visualize query waterfalls and spot patterns.
- Monitor query execution time and flag any endpoint making more than 5-10 database calls.
One JOIN that returns 800 rows beats 18 separate queries by 10x in execution time.
Serverless Cold Starts Add Seconds
Serverless sounds great until you stumble upon a cold start. When a function hasn’t run recently, the app needs to spin up a new execution environment before it can handle your request.
The cold start penalty varies by platform:
- AWS Lambda: 100ms to 1 second (affects less than 1% of requests).
- Azure Functions: 1 to 10 seconds typical, occasionally 30 seconds.
- Traffic pattern impact: Steady traffic stays warm, idle functions cold start repeatedly.
This kind of latency will easily make your users think your app crashed.
How to fix it:
- Use provisioned concurrency for AWS Lambda functions that serve user-facing APIs.
- Switch to the Azure Functions Premium Plan for pre-warmed instances that eliminate cold starts.
- Minimize your dependency tree; larger packages lead to longer cold starts across all apps.
- Allocate more memory to serverless functions.
- Background jobs and async processes can tolerate cold starts; synchronous user requests can’t – clearly distinguish them.
For functions that must stay responsive, provisioned concurrency costs an additional fee but delivers sub-100ms response times.
Under-Resourced Instances Throttle Your App
Cloud providers offer numerous instance types. Professionals mostly prefer the smallest one that appears “close enough” to their local hardware.
That is a mistake. Compare what you had versus what you’re receiving:
- On-prem server: Dedicated CPU cores, fast local storage, generous memory.
- Default cloud instance: Shared CPU, network-attached storage, just-enough memory.
- The gap: According to a 2024 IDC study, 55% of businesses report ineffective performance monitoring after migration because of the lack of a baseline understanding.
Memory-constrained apps start swapping to disk. CPU-bound platforms queue requests. Both result in slowdowns that soar under load.
How to deal with it:
- Baseline your current performance before migrating. Monitor CPU and memory usage, disk I/O, and network throughput under normal and peak load.
- Run synthetic load tests in your cloud environment with realistic traffic patterns before cutting over.
- Consider burstable instances for variable workloads, but watch for CPU credit exhaustion.
- Use CloudWatch (AWS), Azure Monitor, or Google Cloud Monitoring to track resource utilization.
- Right-size instances based on real metrics; start one tier higher than you think you need, then optimize down.
Research indicates that nearly 65% of companies state that revisiting app architecture leads to key enhancements in throughput and a reduction in latency. Sometimes you need a bigger instance. Sometimes you need better code. Performance testing shows you which.
No Caching = Repeated Database Hits
Assuming your on-premises app had caching built in (local memory caches, shared cache servers in the same rack, in-memory databases). Those don’t migrate automatically.
Now every request travels across the network instead of reaching a cache:
- Cache hit on-prem: Sub-millisecond response.
- Same request in cloud without cache: 50-100ms to query database.
- Geographic distance penalty: Database in a different region adds another 50-500ms.
- Impact multiplier: Every uncached query in a page load compounds the delay.
When your cache was serving requests in 0.5ms locally, but now the round trip to your cloud database takes 100ms, you’ve made every cache-worthy request 200x slower.
How to address it:
- Deploy Redis or Memcached for application-level caching of database queries and API responses.
- Set appropriate TTL values for cached data; cache substantially for rarely-changing data, reasonably for frequently-updated data.
- Use CDN services (CloudFront, Azure CDN, Google Cloud CDN) to cache static assets at edge locations near users.
- Enable query result caching in your database, where supported. PostgreSQL, MySQL, and cloud-native databases offer this.
- Place your cache layer in the same region as your compute instances to cut round-trip latency.
Implementing correct caching layers often translates into 10x improvements.
Cross-Region Latency Compounds Delays
Everything was seated in the same datacenter on-premises. In the cloud, the same region doesn’t guarantee the same datacenter:
- Cross-region penalty: App in us-west-2 + database in us-east-1 = 70ms+ per query.
- Multi-service amplification: Each service call across regions adds 50-500ms depending on distance.
- Edge performance comparison: Content at edge locations travels in <100ms versus 500ms+ from origin servers.
- Common mistake: You accidentally spread services across 3-4 regions during migration.
How to solve it:
- Implement read replicas in areas where you have huge user pools.
- Use connection pooling to amortize connection setup costs across several queries.
- Co-locate your app and database in the same region as a base architecture.
- Use edge compute services (AWS Lambda@Edge, Cloudflare Workers) for global platforms that need low latency everywhere.
- Analyze cross-region traffic to detect services requiring geographic consolidation.
For global apps, prioritize a multi-region active-active architecture. But only after you’ve handled single-region performance. Complexity often decreases speed.
On a Final Note
These five issues explain most post-migration performance problems. They’re technical, measurable, and fixable. Cloud performance requires cloud-native patterns. So do not apply lift-and-shift practices. Test performance before full migration. Monitor it after. Your users will notice the difference.
