The most cost-effective approach is usually:

1) Monitor with synthetic checks at the edge of each critical path

Instead of probing every endpoint constantly, focus on:

• your top user-facing API flows
• key regions/availability zones
• the most business-critical SLAs

Use a small set of synthetic requests that:

• hit representative endpoints
• validate success and latency
• run at an interval that matches how quickly you need to detect issues

Cost-saving rule of thumb

• Tier 1 endpoints: every 1–2 minutes
• Tier 2 endpoints: every 5–10 minutes
• Tier 3 / less critical: every 15+ minutes

That gives good coverage without exploding check volume.

2) Prefer multi-step synthetic journeys only where needed

Multi-step tests are useful, but expensive if overused.
For most APIs, a single request with:

• auth
• core business operation
• basic response validation

is enough. Reserve longer scripted flows for:

• checkout / order creation
• login/token issuance
• critical dependency chains

3) Use latency thresholds and burn-rate style alerting

Avoid alerting on every slow request. Instead:

• alert only when p95/p99 latency exceeds a threshold for a sustained window
• combine with failure-rate alerting
• use multi-window alerts (fast + slow window) to reduce noise

Example:

• warn if p95 > 500 ms for 10 minutes
• page if p95 > 1s for 5 minutes
• page if error rate > 2% for 5 minutes

This is much more cost-effective than extremely frequent checks plus noisy alerts.

4) Reduce synthetic check count with smart sampling

You do not need to monitor every route individually if routes behave similarly. Group by:

• service
• region
• auth path
• major dependency

Then sample only the highest-risk combinations.
For high-volume APIs, synthetic monitoring is for detection, while your logs/metrics are for granularity.

5) Choose tools with transparent pricing and low per-check overhead

The best value usually comes from tools that charge based on:

• number of checks or monitors
• frequency
• locations
• optional scripting/browser execution

For API latency monitoring specifically, look for:

• lightweight HTTP/API checks
• built-in alerting
• low-cost global PoPs
• good aggregation of latency percentiles

Common cost-efficient options include:

• Grafana Cloud Synthetic Monitoring / Grafana Cloud + alerts
• Datadog Synthetics if you already use Datadog broadly
• Pingdom
• Checkly
• Better Stack
• UptimeRobot for very simple monitoring, though it’s less sophisticated

6) Route alerts to the cheapest useful channel first

To keep ops costs down:

• use chat alerts for warnings
• page only on sustained or severe degradation
• deduplicate alerts by service/region
• suppress alerts during deploy windows if appropriate

7) Pair synthetics with cheap internal metrics

Synthetic monitoring should not be your only latency source.
Add internal metrics from:

• API gateway
• load balancer
• tracing/APM
• service-side latency histograms

This lets you keep synthetic frequency low while still seeing detailed latency trends.

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Most cost-effective setup in practice

A practical low-cost setup looks like this:

• 5–20 synthetic API checks total
• run every 1–5 minutes depending on criticality
• 2–3 regions only
• simple scripted requests, no browser checks unless needed
• threshold-based alerts on sustained p95 latency + errors
• use internal metrics for deep investigation

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If you want the cheapest workable stack

A very cost-effective combo is:

• synthetic monitoring: Checkly, Grafana Cloud, or Better Stack
• alerting: built-in notifications to Slack/PagerDuty
• metrics backend: Prometheus/Grafana or your cloud provider’s metrics

This keeps synthetic checks focused and inexpensive while avoiding over-alerting.

If you want, I can also give you:

1. a vendor comparison by price/features, or
2. a recommended monitoring plan for a specific API size/traffic level.