Why product integration infrastructure is harder than you think

Maintaining a robust infrastructure to handle multiple integrations at scale is harder than you think. This article breaks it down in detail.

Table of contents

Example H2

Introduction

Integrations seem simple—just a few API calls, right? In reality, maintaining them can be a harder infrastructure challenge than you think.

Previously, we discussed the hidden costs of building integrations in-house. This post dives into one of the biggest: infrastructure.

Data syncing challenges

Syncing data requires handling:

Task orchestration: Cron jobs don’t cut it. Tasks may overlap, get stuck, or require prioritization.
Resilient long-running tasks: Syncs can take hours. Systems must handle failures, retries, and visibility.
High-frequency or real-time syncing: Some use cases require minute-level syncs or webhooks.
Two-way syncing: Reads and writes must be ordered correctly to avoid conflicts.
Incremental syncing: Fetch only modified records to avoid full resyncs.
Change & deletion detection: Many APIs don’t expose deletions, requiring manual tracking.

Rate limit challenges

Exceeding API rate limits can disrupt integrations. Key challenges include:

Detection: Rate limits vary widely across APIs, and some don’t return explicit errors.
Retries: Naive retries worsen issues. A centralized prioritization system is essential.
Avoiding penalties: Some APIs slow responses or revoke access for excessive requests. Limits may apply per user, app, or endpoint and must be managed centrally.

Data quality challenges

APIs structure data differently, requiring validation to prevent integration failures.

Run-time & compile-time validation: Many APIs lack type definitions, requiring custom validation.
Custom object schemas: APIs like Jira allow user-defined fields, requiring schema introspection.
Inevitable inconsistencies: Real-world data varies. Iteration is key.
Schema migrations: As integrations evolve, schemas must be updated without breaking existing data.

Scalability challenges

Growth increases complexity. Scaling requires:

Resource management: CPU and memory spikes can crash services.
Storage optimization: Large datasets need efficient storage solutions to prevent slow syncs.
Concurrency control: Parallel processes can conflict (e.g., token refreshes, config updates).
Failure handling: Proactive monitoring reduces costly outages and customer complaints.
Performance tuning: Task orchestration and data storage become bottlenecks over time.

Observability challenges

Reliable integrations require strong monitoring:

Comprehensive failure tracking: APIs, credentials, data validation, and orchestration all need visibility.
Performance monitoring: Track queue times, execution speed, and storage usage.
Rate-limit monitoring: APIs impose unique limits requiring tailored observability.
Network traffic logging: Debugging requires detailed HTTP request logs without excessive storage costs.
Data quality monitoring: Actionable insights help avoid manual debugging.‍
Customer-specific debugging: Filtering logs by integration, customer, execution run, or data type fetched speeds up issue resolution.

Conclusion

Building integrations isn’t just about API calls—it demands robust infrastructure. Without these capabilities, teams face scaling pains, wasted engineering effort, and poor user experiences.

By addressing these challenges early, you can prevent costly surprises down the line.