API Management and Integration: Designing Developer-Friendly SaaS Platforms

Problem Statement

Modern SaaS platforms require robust API management systems to enable third-party integrations, support diverse client applications, and create developer ecosystems. System design interviews at companies like Twilio, Stripe, and MuleSoft frequently test your ability to architect scalable, reliable, and developer-friendly API platforms that handle versioning, rate limiting, authentication, and event delivery while maintaining backward compatibility and clear documentation.

Actual Interview Questions from Major Companies

Twilio: "Design a versioned API system that maintains backward compatibility." (Blind)
Stripe: "How would you implement webhook delivery with guaranteed delivery?" (Glassdoor)
MuleSoft: "Design an API gateway with rate limiting and analytics." (Blind)
SendGrid: "Create a system for API key management and request throttling." (Grapevine)
Postman: "Design a system for API documentation and testing." (Blind)
Kong: "How would you implement an API gateway with plugin architecture?" (Glassdoor)

Solution Overview: API Management Architecture

A comprehensive API management platform consists of several components that work together to provide a secure, scalable, and developer-friendly experience:

This architecture supports:

Secure API access with authentication and authorization
Traffic management with rate limiting and throttling
Request routing and transformation
Comprehensive monitoring and analytics
Developer-friendly documentation and SDKs
Reliable event delivery through webhooks

API Versioning and Compatibility

Twilio: "Design a versioned API system that maintains backward compatibility"

Twilio frequently asks system design questions about API versioning. A staff engineer who received an offer shared their approach:

Key Design Components

Version Identification Strategies
- URL path versioning (/v1/resource)
- Header-based versioning (Api-Version: 1)
- Content negotiation (Accept: application/vnd.company.v1+json)
- Query parameter (?version=1)
Compatibility Mechanisms
- Interface adapters for different versions
- Request/response transformers
- Field deprecation processes
API Evolution Principles
- Additive changes only in minor versions
- Required parameters never removed
- Field types never changed in incompatible ways
- Explicit deprecation with timelines

API Versioning Strategy

Algorithm: API Version Handling
Input: API request with possible version information
Output: Properly formatted response matching the requested version

1. Extract version information:
   a. Check URL path for version segment
   b. Check headers for version information
   c. Check content type for versioned media type
   d. Check query parameters for version
   e. If no version found, use default (latest stable)

2. Route to appropriate handler:
   a. If explicit version handler exists, route to it
   b. If not, use compatibility layer with newer version

3. For request processing:
   a. Apply version-specific validation rules
   b. Transform request format if needed
   c. Pass normalized request to core business logic

4. For response processing:
   a. Get core response from business logic
   b. Apply version-specific transformations
   c. Format according to version requirements
   d. Include deprecation warnings if applicable

5. Handle deprecated versions:
   a. Log usage of deprecated endpoints
   b. Include deprecation notices in responses
   c. Notify developers via warning headers
   d. Collect metrics on deprecated usage

6. Maintain compatibility interfaces:
   a. For each deprecated field, provide mapping
   b. For removed fields, supply default values
   c. For changed field types, apply conversions
   d. For renamed fields, map old names to new

Twilio Follow-up Questions and Solutions

"How would you handle API evolution without creating a new version for every change?"

Twilio interviewers often probe for techniques to evolve APIs without major versioning:

Backward Compatible Additions
- New optional parameters
- New response fields (clients ignore unknown)
- Extended enum values with safe fallbacks
- Optional request/response formats
Compatibility Layer Design
- Request upgraders to newest internal version
- Response downgraders to requested version
- Field expansion and contraction rules
- Default value policies

"How would you manage the transition when deprecating API features?"

Another common Twilio follow-up explores API lifecycle management:

Staged Deprecation Process
- Announcement phase with timeline
- Warning phase with header notices
- Restricted access phase for new clients
- Final sunset with error responses
Developer Migration Support
- Migration guides and code examples
- Automated migration tools
- Active usage notifications
- Direct outreach for high-impact customers

Webhook Delivery System

Stripe: "How would you implement webhook delivery with guaranteed delivery?"

Stripe frequently asks about designing reliable webhook delivery systems. A principal engineer who joined Stripe shared their approach:

Key Design Components

Event Persistence Layer
- Durable event storage
- Event versioning
- Exactly-once delivery semantics
Delivery System
- Exponential backoff retry
- Circuit breaker pattern
- Idempotency mechanisms
Security Features
- Payload signing
- Secret management
- Endpoint verification
Developer Tooling
- Webhook testing console
- Delivery status dashboard
- Event replay functionality

Reliable Webhook Delivery Algorithm

Algorithm: Reliable Webhook Delivery
Input: Event data, subscriber endpoints
Output: Successful delivery to all endpoints or exhausted retry

1. Process and store event:
   a. Validate and normalize event data
   b. Generate unique event ID
   c. Store event in durable storage
   d. Add event to delivery queue

2. For each subscriber endpoint:
   a. Prepare webhook payload:
      i. Format event according to subscription
      ii. Add metadata (timestamp, event type, etc.)
      iii. Generate signature with endpoint's secret
   b. Set initial delivery attempt:
      i. Add idempotency key
      ii. Include signature in headers
      iii. Set appropriate Content-Type

3. Delivery attempt process:
   a. Send HTTP request to endpoint
   b. Record attempt details (timestamp, request, response)
   c. Process response:
      i. Success (2xx): Mark as delivered
      ii. Retriable error (5xx, timeout): Schedule retry
      iii. Permanent error (4xx): Flag for review
   d. Update delivery status

4. Retry strategy:
   a. Calculate next retry time with exponential backoff
   b. Add jitter to prevent thundering herd
   c. Track retry count against maximum attempts
   d. Place in appropriate delay queue

5. Circuit breaker implementation:
   a. Monitor failure rate per endpoint
   b. If failure threshold exceeded, open circuit
   c. Allow periodic test requests to check recovery
   d. Reset circuit on successful delivery

6. Monitoring and alerting:
   a. Track delivery success rates
   b. Alert on persistent failures
   c. Provide delivery status dashboard
   d. Enable manual replay functionality

Stripe Follow-up Questions and Solutions

"How would you ensure webhook security?"

Stripe interviewers often probe for webhook security knowledge:

Payload Signing Approach
- HMAC-based signatures
- Secret rotation mechanisms
- Timestamp protection against replay attacks
- Multiple signature algorithm support
Endpoint Verification Flow
- Challenge-response verification
- Gradually increasing event volume
- Required signature verification
- IP filtering options

Webhook Signature Generation:

1. Collect payload elements:
   - Raw request body
   - Timestamp (t=1612345678)
   - Event ID for replay protection

2. Construct signing string:
   timestamp + '.' + event_id + '.' + raw_body

3. Generate HMAC signature:
   signature = HMAC(secret_key, signing_string)

4. Include in HTTP headers:
   X-Signature: t=1612345678,v1=5257a869e7ecebeda32affa62cdca3fa51cad7e77a0e56ff536d0ce8e108d8bd

"How would you handle high-volume webhook delivery at scale?"

Another key Stripe follow-up explores performance and scaling:

Distributed Delivery Architecture
- Regional webhook workers
- Endpoint-based sharding
- Load-balanced delivery pools
- Distributed event store
Performance Optimization
- Batch delivery for certain endpoints
- Adaptive concurrency control
- Prioritization for critical events
- Connection pooling and reuse

API Gateway and Rate Limiting

MuleSoft: "Design an API gateway with rate limiting and analytics"

MuleSoft frequently asks about designing comprehensive API gateway solutions. A staff engineer who joined MuleSoft shared their approach:

Key Design Components

Gateway Core Functionality
- Request routing and load balancing
- Protocol translation (HTTP, gRPC, etc.)
- Request/response transformation
- Service discovery integration
Security Layer
- Authentication mechanisms
- Authorization rules
- Rate limiting algorithms
- IP filtering
Observability Features
- Request/response logging
- Performance metrics collection
- Error tracking
- Traffic analysis
Configuration Management
- API definitions
- Gateway policies
- Route mappings
- Dynamic configuration updates

Rate Limiting Algorithm

Algorithm: Distributed Rate Limiting
Input: API request with authentication information
Output: Allow/deny decision with rate limit headers

1. Identify the rate limit subject:
   a. Extract API key or token
   b. Determine client IP if no authentication
   c. Identify tenant for multi-tenant systems
   d. Apply additional dimensions (method, endpoint, etc.)

2. Determine applicable rate limits:
   a. Global rate limits
   b. Plan-specific limits
   c. Endpoint-specific limits
   d. Special case overrides

3. Check current usage against limits:
   a. Generate rate limit key(s)
   b. For each applicable limit:
      i. Query current usage from distributed store
      ii. Compare against limit threshold
      iii. If any limit exceeded, prepare rejection

4. Process allowed request:
   a. Increment usage counters
   b. Update last request timestamp
   c. Continue API request processing
   d. Add rate limit headers to response:
      - X-RateLimit-Limit: maximum allowed
      - X-RateLimit-Remaining: requests remaining
      - X-RateLimit-Reset: window reset time

5. Process rejected request:
   a. Generate 429 Too Many Requests response
   b. Add Retry-After header
   c. Include descriptive error message
   d. Add rate limit headers as above
   e. Log rate limit event

6. Handle counter expiration:
   a. Set appropriate TTL for counters
   b. Implement sliding window if required
   c. Handle clock skew in distributed environment
   d. Process cleanup of expired records

MuleSoft Follow-up Questions and Solutions

"How would you implement a token bucket rate limiting algorithm in a distributed system?"

This common MuleSoft follow-up tests understanding of rate limiting approaches:

Distributed Token Bucket Implementation
- Centralized token store with Redis
- Atomic bucket operations
- Lease-based token allocation
- Fault-tolerant token refill
Performance Optimization
- Local caching of token state
- Batch token operations
- Client-side rate limiting
- Hierarchical bucket design

Token Bucket Implementation with Redis:

1. When request arrives:
   bucket_key = "rate_limit:{api_key}:{endpoint}"
   
   # Atomic script to check and consume token
   lua_script = """
   local bucket_key = KEYS[1]
   local max_tokens = tonumber(ARGV[1])
   local refill_rate = tonumber(ARGV[2])
   local now = tonumber(ARGV[3])
   local tokens_to_consume = tonumber(ARGV[4])
   
   -- Get bucket information or create new
   local bucket = redis.call('HMGET', bucket_key, 'tokens', 'last_refill')
   local current_tokens = tonumber(bucket[1]) or max_tokens
   local last_refill = tonumber(bucket[2]) or now
   
   -- Calculate token refill
   local time_passed = now - last_refill
   local new_tokens = math.min(max_tokens, current_tokens + (time_passed * refill_rate))
   
   -- Check if enough tokens
   if new_tokens < tokens_to_consume then
     return 0  -- Not enough tokens
   end
   
   -- Consume tokens
   local remaining_tokens = new_tokens - tokens_to_consume
   redis.call('HMSET', bucket_key, 'tokens', remaining_tokens, 'last_refill', now)
   redis.call('EXPIRE', bucket_key, 86400)  -- 1 day TTL
   
   return remaining_tokens
   """
   
   result = redis.eval(
     lua_script, 
     1, 
     bucket_key, 
     max_tokens, 
     refill_rate, 
     current_time_ms, 
     1  # Tokens to consume
   )
   
   if result > 0:
     # Request allowed
     return True, result
   else:
     # Rate limited
     return False, 0

"How would you design analytics for an API gateway?"

Another important MuleSoft follow-up explores observability:

Real-time Analytics Pipeline
- Event streaming for API calls
- Near-real-time aggregation
- Dimension-based analysis
- Anomaly detection
Data Collection Strategy
- Sampling for high-volume APIs
- Differential processing by importance
- Contextual enrichment
- Tenant-aware partitioning

API Key Management

SendGrid: "Create a system for API key management and request throttling"

SendGrid frequently asks about designing secure API key management systems. A senior architect who joined SendGrid shared their approach:

Key Design Components

Key Management Service
- Secure key generation
- Cryptographic storage
- Rotation and revocation
- Permissions and scopes
Access Control System
- Scope-based permissions
- Resource-level access control
- Environment segregation
- Tenant isolation
Throttling and Quotas
- Usage-based throttling
- Plan-based quota enforcement
- Burst handling
- Overage policies
Security Features
- Key compromise detection
- Abuse monitoring
- Leak prevention
- Audit logging

API Key Management Workflow

Algorithm: API Key Lifecycle Management
Input: API key request with scope and permissions
Output: Generated API key or management action

1. Key generation process:
   a. Validate user permissions
   b. Generate strong random key:
      i. Use cryptographically secure random generator
      ii. Apply appropriate format (Base64, UUID, etc.)
      iii. Ensure sufficient entropy
   c. Generate key ID for reference
   d. Apply requested scopes and permissions
   e. Set expiration if applicable

2. Key storage approach:
   a. Hash key with strong algorithm (Argon2, bcrypt)
   b. Store hash, not plaintext key
   c. Store metadata (owner, scopes, creation date)
   d. Apply encryption at rest
   e. Set up indexed lookups

3. Key validation process:
   a. Extract key from request (header, parameter)
   b. Normalize and format check
   c. Hash and lookup in database
   d. Check key status (active, revoked, expired)
   e. Verify scopes against requested resource
   f. Record usage for analytics

4. Key rotation workflow:
   a. Generate new key with identical permissions
   b. Mark as rotation replacement
   c. Notify user of new key
   d. Maintain old key during grace period
   e. Sunset old key after transition period

5. Key revocation process:
   a. Immediate logical deletion option
   b. Delayed revocation option
   c. Propagation to validation services
   d. Notification to administrators
   e. Audit log creation

6. Security monitoring:
   a. Track usage patterns per key
   b. Detect anomalous behavior
   c. Alert on potential compromise
   d. Implement automatic protections
   e. Maintain comprehensive audit trail

SendGrid Follow-up Questions and Solutions

"How would you design a secure key distribution system?"

This common SendGrid follow-up explores key security:

Secure Delivery Channels
- One-time viewing mechanism
- Secure notification delivery
- Out-of-band verification
- Transport layer security
Key Visibility Control
- Partial key display
- Just-in-time access to full keys
- Administrator oversight
- Access logging and notification

"How would you implement API key permissions and scopes?"

Another important SendGrid follow-up tests access control knowledge:

Granular Permission Model
- Action-based permissions (read, write, delete)
- Resource-specific scopes
- Permission inheritance hierarchies
- Least-privilege defaults
Scope Implementation
- OAuth-style scope strings
- Scope intersection for validation
- Scope expansion for convenience
- Dynamic scope evaluation

Scope Intersection Algorithm:

1. Define key scopes as a set during creation:
   key_scopes = {"mail.send", "mail.read", "contacts.read"}

2. Define endpoint required scopes:
   endpoint_scopes = {"mail.send"}

3. When validating access:
   if any(scope in key_scopes for scope in endpoint_scopes):
     allow_access()
   else:
     deny_access()

4. For hierarchical scopes (e.g., "mail.*"):
   def scope_matches(required, provided):
     if required == provided:
       return True
     if provided.endswith(".*"):
       base = provided[:-2]
       return required.startswith(base + ".")
     return False

API Documentation and Developer Experience

Postman: "Design a system for API documentation and testing"

Postman frequently asks about designing comprehensive API documentation systems. A principal engineer who joined Postman shared their approach:

Key Design Components

API Specification System
- OpenAPI/Swagger support
- Schema validation
- Machine-readable definitions
- Version control integration
Documentation Generation
- Reference documentation
- Interactive API console
- Code sample generation
- SDK documentation
Testing Infrastructure
- Mock server generation
- Test script management
- Automated testing
- Environment management
Developer Portal
- Customizable documentation themes
- Authentication and access control
- Search and navigation
- Feedback mechanisms

Documentation-as-Code Workflow

Algorithm: API Documentation Lifecycle
Input: API specification, code repositories, usage data
Output: Generated documentation, interactive console, code samples

1. API specification management:
   a. Parse OpenAPI/Swagger files
   b. Validate against schema
   c. Store versioned specifications
   d. Track changes between versions

2. Documentation generation process:
   a. Transform specification to documentation:
      i. Convert endpoints to reference documentation
      ii. Generate request/response examples
      iii. Create parameter tables
      iv. Include authentication details
   b. Apply template and styling
   c. Generate interactive elements
   d. Create linkable sections

3. Code sample generation:
   a. For each endpoint:
      i. Extract request parameters
      ii. Determine authentication requirements
      iii. Generate code in multiple languages
   b. Include common use cases
   c. Format code with syntax highlighting
   d. Enable copy-to-clipboard functionality

4. Interactive console implementation:
   a. Generate request builder interface
   b. Pre-populate with example values
   c. Enable parameter manipulation
   d. Provide authentication input
   e. Display raw request and response
   f. Handle errors gracefully

5. Documentation deployment:
   a. Compile all assets
   b. Stage for review if needed
   c. Deploy to hosting infrastructure
   d. Invalidate caches
   e. Notify subscribers of updates

6. Usage analytics processing:
   a. Track documentation section views
   b. Monitor interactive console usage
   c. Analyze common error patterns
   d. Generate improvement suggestions
   e. Highlight popular endpoints

Postman Follow-up Questions and Solutions

"How would you design an API documentation system that stays synchronized with the actual API?"

This common Postman follow-up explores documentation automation:

Specification-First Development
- API specification as source of truth
- Automated code generation from spec
- Contract testing against specification
- Continuous integration validation
Runtime Documentation Validation
- Response schema validation
- Automated documentation testing
- Drift detection mechanisms
- Self-documenting endpoints

"How would you measure and improve API documentation effectiveness?"

Another important Postman follow-up tests user experience knowledge:

Documentation Analytics
- Section-level engagement tracking
- Time spent on documentation
- Documentation bounce rate
- Search query analysis
User Feedback Mechanisms
- Contextual feedback collection
- Documentation ratings
- Issue reporting workflow
- Developer surveys

API Gateway Plugin Architecture

Kong: "How would you implement an API gateway with plugin architecture?"

Kong frequently asks about designing extensible API gateway systems. A staff engineer who joined Kong shared their approach:

Key Design Components

Plugin Framework
- Plugin lifecycle management
- Configuration API
- Dependency resolution
- Versioning support
Request Processing Pipeline
- Phase-based processing
- Plugin ordering
- Short-circuit capability
- Error handling
Extension Points
- Authentication plugins
- Traffic control plugins
- Transformation plugins
- Logging and monitoring plugins
Plugin SDK
- Developer tools
- Testing framework
- Plugin templates
- Documentation

Plugin Architecture Design

Algorithm: Plugin-based Request Processing
Input: HTTP request, gateway configuration, plugin configurations
Output: Processed request or response

1. Initialize plugin framework:
   a. Load plugin registry
   b. Resolve plugin dependencies
   c. Validate plugin configurations
   d. Build execution chains for each phase

2. For incoming request:
   a. Create request context
   b. Initialize plugin instances for request
   c. Execute plugins in phase order

3. Request phase processing:
   a. Execute request plugins in priority order
   b. For each plugin:
      i. Pass request context to plugin
      ii. Apply plugin-specific transformations
      iii. Check for short-circuit conditions
      iv. Update request context with changes
   c. If any plugin indicates rejection:
      i. Skip remaining plugins
      ii. Generate error response
      iii. Execute response phase

4. Access phase processing:
   a. Execute authentication plugins
   b. Execute authorization plugins
   c. Execute rate limiting plugins
   d. If any plugin denies access:
      i. Generate appropriate error response
      ii. Skip to response phase

5. Routing phase processing:
   a. Determine target service
   b. Apply load balancing if configured
   c. Prepare upstream request
   d. Send request to backend service

6. Response phase processing:
   a. Execute response plugins in priority order
   b. Apply response transformations
   c. Add headers, modify body as needed
   d. Apply caching if configured

7. Finalize request processing:
   a. Log request/response details
   b. Emit metrics
   c. Clean up plugin resources
   d. Return final response to client

Kong Follow-up Questions and Solutions

"How would you design a plugin system that maintains performance and reliability?"

This common Kong follow-up explores plugin system performance:

Performance Optimization
- Plugin execution time limits
- Resource isolation techniques
- Compiled vs. interpreted plugins
- Performance profiling tools
Reliability Safeguards
- Plugin sandboxing
- Crash recovery mechanisms
- Circuit breakers for misbehaving plugins
- Plugin version compatibility checks

"How would you implement a custom authentication plugin?"

Another key Kong follow-up tests plugin implementation knowledge:

Authentication Plugin Framework
- Credential extraction
- Validation logic
- Caching strategy
- Failure handling
Integration Capabilities
- External identity provider integration
- Credential store connectivity
- Token validation
- User context enrichment

Performance and Scalability Considerations

Key Performance Challenges

High-volume API processing
- Thousands to millions of requests per second
- Low-latency requirements
- Global distribution of clients
Plugin execution overhead
- Multiple plugins per request
- Varying plugin complexity
- Resource isolation requirements
Authentication and rate limiting performance
- Token validation overhead
- Distributed counter management
- Cache coherency challenges

Optimization Strategies

Twilio-style API Gateway Optimization

Twilio interviewers frequently ask about optimizing API gateway performance:

Connection Management
- Keep-alive connection pooling
- Connection reuse strategies
- Multiplexing protocols (HTTP/2)
- Optimal timeout configuration
Caching Architecture
- Multi-level cache design
- Cache invalidation strategies
- Cache coherency approaches
- Content-aware caching

API Gateway Performance Optimization Hierarchy:

1. Network Optimizations
   - Connection pooling to backend services
   - HTTP/2 for multiplexing
   - Strategic response caching
   - Compression for large payloads

2. Processing Optimizations
   - Asynchronous processing where appropriate
   - Batch processing for high-volume endpoints
   - Minimizing serialization/deserialization
   - Zero-copy data handling where possible

3. Gateway Architecture Optimizations
   - Data locality for frequently accessed data
   - Shard distribution based on access patterns
   - Read replicas for configuration data
   - Regional deployment for reduced latency

4. Plugin Execution Optimizations
   - Short-circuit plugin evaluation
   - Plugin execution ordering by computational cost
   - Shared context to reduce duplicate work
   - Pre-compiled plugin execution paths

Stripe-style Reliability Patterns

Stripe interviews often cover optimizing for reliability:

Fault Isolation
- Circuit breaker implementation
- Bulkhead pattern for resource isolation
- Fallback mechanisms
- Graceful degradation
Distributed Consistency
- Eventually consistent rate limiting
- Consensus algorithms for configuration
- Conflict resolution strategies
- Multi-region resilience

Real-World Implementation Challenges

API Versioning Strategy Decisions

Twilio interviews often include questions about versioning tradeoffs:

Versioning Approach Selection
- URL path vs. header vs. content negotiation
- Global vs. resource-specific versioning
- Date-based vs. semantic versioning
- Granular vs. monolithic versions
Migration Strategy Design
- Sunset scheduling and communication
- Migration tooling development
- Client library versioning
- Backwards compatibility periods

Rate Limiting Implementation Complexity

MuleSoft interviews often explore handling complex rate limiting:

Multi-dimension Rate Limiting
- Endpoint-specific limits
- User/tenant/API key dimensions
- Hierarchical limit inheritance
- Business-value-based prioritization
Global Distributed Rate Limiting
- Eventual consistency approach
- Approximate counting algorithms
- Regional limit allocation
- Synchronization frequency decisions

Webhook Delivery at Scale

Stripe interviews frequently test understanding of webhook scaling:

Webhook Fanout Challenges
- High-cardinality subscriber lists
- Variable endpoint performance
- Regional delivery optimization
- Traffic spikes during events
Delivery Reliability vs. Performance
- Guaranteed vs. best-effort delivery
- Synchronous vs. asynchronous processing
- Storage requirements for retry
- Timeout and expiration policies

Key Takeaways for Interviews

Design for Developer Experience
- Intuitive API patterns
- Comprehensive documentation
- Consistent error handling
- Robust client libraries
Plan for API Evolution
- Versioning strategy
- Compatibility guarantees
- Deprecation processes
- Migration support
Build in Security and Control
- Authentication mechanisms
- Rate limiting and quotas
- Request validation
- Monitoring and analytics
Ensure Reliable Integration
- Webhook delivery guarantees
- Idempotency mechanisms
- Failure handling
- Monitoring and debugging tools
Optimize for Performance
- Request processing efficiency
- Caching strategies
- Distributed architecture
- Global presence

API Management and Integration Framework

Download our comprehensive framework for designing scalable, reliable API management and integration systems for SaaS platforms.

The framework includes:

API versioning strategy decision tree
Webhook delivery reliability patterns
Rate limiting implementation templates
Developer experience optimization techniques
API security best practices

Download Framework →

This article is part of our SaaS Platform Engineering Interview Series:

Multi-tenant Architecture: Data Isolation and Performance Questions
SaaS Authentication and Authorization: Enterprise SSO Integration
Usage-Based Billing Systems: Metering and Invoicing Architecture
SaaS Data Migration: Tenant Onboarding and ETL Challenges
Feature Flagging and A/B Testing: SaaS Experimentation Infrastructure
API Management and Integration: Designing Developer-Friendly SaaS Platforms (this article)
SaaS Platform Engineering Interviews: The Complete Guide

API Management and Integration: Designing Developer-Friendly SaaS Platforms

Table of Contents

Table of Contents

API Management and Integration: Designing Developer-Friendly SaaS Platforms

Problem Statement

Actual Interview Questions from Major Companies

Solution Overview: API Management Architecture

API Versioning and Compatibility

Twilio: "Design a versioned API system that maintains backward compatibility"

Key Design Components

API Versioning Strategy

Twilio Follow-up Questions and Solutions

Webhook Delivery System

Stripe: "How would you implement webhook delivery with guaranteed delivery?"

Key Design Components

Reliable Webhook Delivery Algorithm

Stripe Follow-up Questions and Solutions

API Gateway and Rate Limiting

MuleSoft: "Design an API gateway with rate limiting and analytics"

Key Design Components

Rate Limiting Algorithm

MuleSoft Follow-up Questions and Solutions

API Key Management

SendGrid: "Create a system for API key management and request throttling"

Key Design Components

API Key Management Workflow

SendGrid Follow-up Questions and Solutions

API Documentation and Developer Experience

Postman: "Design a system for API documentation and testing"

Key Design Components

Documentation-as-Code Workflow

Postman Follow-up Questions and Solutions

API Gateway Plugin Architecture

Kong: "How would you implement an API gateway with plugin architecture?"

Key Design Components

Plugin Architecture Design

Kong Follow-up Questions and Solutions

Performance and Scalability Considerations

Key Performance Challenges

Optimization Strategies

Twilio-style API Gateway Optimization

Stripe-style Reliability Patterns

Real-World Implementation Challenges

API Versioning Strategy Decisions

Rate Limiting Implementation Complexity

Webhook Delivery at Scale

Key Takeaways for Interviews

Top 10 API Management Interview Questions

API Management and Integration Framework