Why I Cache External API Data Instead of Calling It Every Time

Source: Dev.to

Initial Approach

When I first started integrating external APIs into my projects, my approach was simple:

Need data?
Call the API.

It felt clean and logical—until real usage hit.

The initial setup looked like this:

Client
↓
API Gateway
↓
External API

Every request triggered:

• a fresh API call
• a network round‑trip
• dependency on someone else’s uptime

For low traffic this seemed fine, but real usage quickly exposed the cracks.

Problems with Direct Calls

Cost

Many APIs charge per request. What looks cheap at first becomes expensive once background jobs, retries, and traffic spikes enter the picture.

Rate Limits

Once you hit rate limits:

• features fail
• errors propagate
• defensive code spreads everywhere

Your system starts being shaped by someone else’s rules.

Latency

Even fast external APIs are:

• network‑bound
• unpredictable
• outside your control

Caching Strategy

Instead of calling the external API on demand, I switched to this model:

Worker / Cron
↓
External API
↓
Database

Client
↓
API Gateway
↓
Database

External APIs are called periodically. The application reads from the database — not the API.

Benefits

• API usage becomes measurable and controlled.
• Database reads are faster and easier to optimize.
• If the API goes down, the system continues working with the last known good data.

The real question is not “Is the data fresh?” but “How fresh does it need to be?” Freshness is a business decision, not a reflex.

When Direct API Calls Still Make Sense

• Real‑time financial transactions
• Authentication & authorization
• User‑triggered actions where freshness is critical