API Development Challenges

A sluggish API isn't just a technical hiccup – it's the difference between retaining and losing users to competitors. Let me share some battle-tested strategies that have helped many  achieve 10x performance improvements: 1. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Not just any caching – but strategic implementation. Think Redis or Memcached for frequently accessed data. The key is identifying what to cache and for how long. We've seen response times drop from seconds to milliseconds by implementing smart cache invalidation patterns and cache-aside strategies. 2. 𝗦𝗺𝗮𝗿𝘁 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Large datasets need careful handling. Whether you're using cursor-based or offset pagination, the secret lies in optimizing page sizes and implementing infinite scroll efficiently. Pro tip: Always include total count and metadata in your pagination response for better frontend handling. 3. 𝗝𝗦𝗢𝗡 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 This is often overlooked, but crucial. Using efficient serializers (like MessagePack or Protocol Buffers as alternatives), removing unnecessary fields, and implementing partial response patterns can significantly reduce payload size. I've seen API response sizes shrink by 60% through careful serialization optimization. 4. 𝗧𝗵𝗲 𝗡+𝟭 𝗤𝘂𝗲𝗿𝘆 𝗞𝗶𝗹𝗹𝗲𝗿 This is the silent performance killer in many APIs. Using eager loading, implementing GraphQL for flexible data fetching, or utilizing batch loading techniques (like DataLoader pattern) can transform your API's database interaction patterns. 5. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 GZIP or Brotli compression isn't just about smaller payloads – it's about finding the right balance between CPU usage and transfer size. Modern compression algorithms can reduce payload size by up to 70% with minimal CPU overhead. 6. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗼𝗹 A well-configured connection pool is your API's best friend. Whether it's database connections or HTTP clients, maintaining an optimal pool size based on your infrastructure capabilities can prevent connection bottlenecks and reduce latency spikes. 7. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗟𝗼𝗮𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 Beyond simple round-robin – implement adaptive load balancing that considers server health, current load, and geographical proximity. Tools like Kubernetes horizontal pod autoscaling can help automatically adjust resources based on real-time demand. In my experience, implementing these techniques reduces average response times from 800ms to under 100ms and helps handle 10x more traffic with the same infrastructure. Which of these techniques made the most significant impact on your API optimization journey?

I’ve noticed a recurring theme in my recent discussions with large organisations.   API friction is a hidden cost centre. And it compounds quietly, every single day.   In most enterprises, developers spend around 3 hours each week dealing with: inconsistent API contracts unclear or custom authentication flows documentation that no longer matches the implementation duplicated services that nobody realised already existed   That’s 20 workdays per developer, per year — before even considering partners, integrators or external ecosystems.   At that point, it’s no longer simply a technical inefficiency. It’s a business and ROI issue. It impacts delivery timelines, onboarding speed, incident recovery, compliance, and customer experience.   During these conversations, leaders often ask: “Okay, but how does standardisation actually help?”   My answer is usually along the following lines: Start with contract-first API design (OpenAPI / AsyncAPI), so design, tests, SDKs and docs all come from the same source of truth. Move to one authentication model (OAuth2 + OIDC) instead of several slightly different ones — it reduces support and integration friction. Generate documentation automatically as part of the build pipeline (if docs can drift, they will drift). Define a few clear conventions for naming, pagination, error structures and versioning — predictability is a performance multiplier. Maintain a shared API catalogue so teams can discover what already exists (otherwise they rebuild it again). And when possible, align with recognised open standards like the work carried out in ETSI TC DATA, which focuses on interoperable data architectures and API patterns for distributed data ecosystems.   This isn’t about adding control or bureaucracy. It’s about removing friction — the kind that slows everything down without anyone noticing it directly.   The outcomes are very tangible: ✅ Faster onboarding of internal teams and partners ✅ Lower long-term integration & maintenance costs ✅ Fewer incidents + smoother change management ✅ Stronger compliance posture ✅ Predictability at scale   If this resonates, comment ROI — I’ll share a simple API Friction Cost Calculator that makes this visible in under 2 minutes.

My first API caused outages. My tenth didn’t. The 10 API principles that survive contact with production: 1. Ship business truth, not database columns Design your contracts around real domain actions and entities. Internal schemas evolve. Your API is the promise you can’t break. 2. Consistency beats cleverness Pick one naming style, one error format, one approach to pagination, one authentication strategy. Your consumers shouldn’t need a decoder ring. 3. Don’t expose implementation details Hide the storage model, hide job orchestration, hide temporary hacks. Clients should never notice your system changes. 4. Errors must teach, not confuse Include a clear message, machine-readable code, and actionable guidance. A great error cuts support tickets in half. 5. Version on breaking change only Expect change. Plan for it. V1, V2, sunset plans, and adapters. Consumers should upgrade because they want improvements, not because you broke them. 6. Rate limits are product decisions Define limits based on behavior you want. Reward good usage patterns. Protect yourself from abuse. Make thresholds visible and predictable. 7. Idempotency everywhere Clients retry. Networks glitch. Duplicate requests happen. Use idempotency keys on write operations so your business rules stay correct. 8. Validate at the edges Everything that crosses the boundary gets validated: shape, type, length, enums, security. Trust nothing at runtime except what you check. 9. Performance is part of the contract Fast responses turn your API into a dependency people love. Measure latency. Optimize the hot paths. 10. Observability isn’t optional Trace every call. Log context. Surface meaningful metrics. When something fails, you must see the “why” within minutes. Key takeaways • Treat APIs as long-term promises • Make behavior obvious, errors useful, and change safe • Control misuse with clear rules, not hidden traps • Build the level of visibility you’ll want at 3am when things break What did I miss?

APIs aren't just endpoints for data engineers - they're the lifelines of your entire data ecosystem. Choosing the Right API Architecture Can Make or Break Your Data Pipeline. As data engineers, we often obsess over storage formats, orchestration tools, and query performance—but overlook one critical piece: API architecture. APIs are the arteries of modern data systems. From real-time streaming to batch processing - every data flow depends on how well your APIs handle the load, latency, and reliability demands. 🔧 Here are 6 API styles and where they shine in data engineering: 𝗦𝗢𝗔𝗣 – Rigid but reliable. Still used in legacy financial and healthcare systems where strict contracts matter. 𝗥𝗘𝗦𝗧 – Clean and resource-oriented. Great for exposing data services and integrating with modern web apps. 𝗚𝗿𝗮𝗽𝗵𝗤𝗟 – Precise data fetching. Ideal for analytics dashboards or mobile apps where over-fetching is costly. 𝗴𝗥𝗣𝗖 – Blazing fast and compact. Perfect for internal microservices and real-time data processing. 𝗪𝗲𝗯𝗦𝗼𝗰𝗸𝗲𝘁 – Bi-directional. A must for streaming data, live metrics, or collaborative tools. 𝗪𝗲𝗯𝗵𝗼𝗼𝗸 – Event-driven. Lightweight and powerful for triggering ETL jobs or syncing systems asynchronously. 💡 The right API architecture = faster pipelines, lower latency, and happier downstream consumers. As a data engineer, your API decisions don’t just affect developers—they shape the entire data ecosystem. 🎯 Real Data Engineering Scenarios to explore: Scenario 1: 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Challenge: Process 100K+ transactions/second with <10ms latency Solution: gRPC for model serving + WebSocket for alerts Impact: 95% faster than REST-based approach Scenario 2: 𝗠𝘂𝗹𝘁𝗶-𝘁𝗲𝗻𝗮𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 Challenge: Different customers need different data subsets Solution: GraphQL with smart caching and query optimization Impact: 70% reduction in database load, 3x faster dashboard loads Scenario 3: 𝗟𝗲𝗴𝗮𝗰𝘆 𝗘𝗥𝗣 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Challenge: Extract financial data from 20-year-old SAP system Solution: SOAP with robust error handling and transaction management Impact: 99.9% data consistency vs. 85% with custom REST wrapper Image Credits: Hasnain Ahmed Shaikh Which API style powers your pipelines today? #data #engineering #bigdata #API #datamining

Dear Testers, this is your periodic reminder that a demonstration is not a test. Today I'll talk about that in terms of API testing. You (and/or your developers) may have a suite of "contract tests", intended to show that *these inputs* sent to an API endpoint return *those outputs*. This is a demonstration. It shows that what you believe or hope to be true about the output is true, or hasn't changed. However, "contract tests" don't represent much of a challenge, neither to the product nor to our beliefs about it. I put "contract tests" in quotes because that's the widely-known term for them. However, we don't think of a person's resilience or character to have been tested by sitting a multiple choice exam or filling out a form correctly. Instead, we say that someone's resilience or character has been tested when they've been exposed to challenging, varied circumstances and experiences, typically over time. It's that sense of "test" that I'm referring to here. Why does software development take so long? One reason is that APIs are tested from the insider's perspective, and not the outsider's. This often causes hours of confusion and detective work for those using the API. The point of an API is to provide a useful, usable interface to a product or service so that an application programmer can use it smoothly and easily. It's easy for the builder of an API, an insider, to assume all kinds of things that an outsider won't know. Such assumptions must be challenged. To test those assumptions, try using the API from an outsider's perspective to accomplish a programming task, to build something, to learn something, or to obtain information *not* expressed in the "contract tests". Is the documentation accurate? Up to date? Helpful? Are there examples for each endpoint? Are they vivid and clear, or do they simply replicate the format of the request and the response? Does the documentation helpfully guide the API programmer through prerequisites or setup steps that might be needed before using a particular endpoint? When you make a request, don't stop at the happy 200 response code. Does the returned data make sense? Are there extra elements in the data? Is there data missing? What are oracles might you use to identify problems in the data? When a response includes an error message, is it helpful for someone trying to diagnose the problem? Does the error message fit the actual problem condition? What happens to responsiveness when the API is used at a high volume? Remember that the *average* load can be a lot different from the *peak* load. (Don't wade across a river that is *on average* one metre deep.) It's a fine thing to check the output from an API. To *test* an API, try building something with it. Note every feeling of confusion, frustration, or annoyance that you experience. Revisit the API and its documentation from time to time, and *test* it from an outsider's perspective.

I’ve looked at thousands of APIs in my career. Here are the top 7 Ways To Screw Up Your API Product. Bad Docs didn’t even make the cut! 🔥 Don’t Explain What It Does Your landing page doesn’t clearly explain who this is for and what problem it solves. You’re missing use cases and are not clear about the value prop of your product. Prospects have to dive into your API Reference to figure out if this is for them. Make it clear up-front and focus on only the top benefits! 🔥 Make It Hard To Sign Up Don't tell them how to start, impose delays and have gatekeepers. This is often done in the name of qualifying the customer, but your content and marketing should do that. When a prospect is ready to hit the CTA, just make it painless. No long forms, no approvals, no waiting - just let them access the API. This becomes even more critical if you want AI Agents to consume your product - which you should! 🔥 Complex Pricing Have difficult to understand pricing with multiple data points. Even worse, make them get on a call to get pricing. You should have 3 easy tiers, with credit card billing. It’s ok to have an “Enterprise” tier that is customizable - large accounts will want that. But make it easy to wrap their heads around your value metrics and how you approach pricing - ideally on a single page, above the fold. 🔥 Don’t Pay Attention To Security There’s nothing that scares away customers faster than security and privacy issues. They’ll stop consuming the API and will churn away. Design for security and stay on top of vulnerabilities. Developers are wary of breaches and there’s just been too many of them in the news. 🔥 Spotty Availability Downtime kills adoption. If customers can’t rely on your solution, they will find an alternative - and quickly! Your API is often built into their customer experience, especially for B2C, DTC and Last-Mile Delivery products. Business folks will not tolerate a degraded experience and will seek your competition instead. 🔥 Inconsistent Data Updates If you break the promise of regular, accurate updates, you are making your API useless. Your customers will deliver packages to the wrong address, they’ll underwrite a bad loan or they’ll ruin business partnerships. Stay on top of data updates and algorithms to keep your customers happy and retain them. 🔥 Surprise Developers With API Changes You change your API often, unannounced and break your customers’ apps. Even if you do everything else right, this is enough to drive customers away. Developers don’t want code to break and organizations should not be surprised by your API changes. Make your APIs evolvable, give your users time to absorb changes and be lenient with supporting existing workflows. Let me know in the comments if these resonate with you. What else have you experienced? And how does the Top-7 change with AI and Agent proliferation? And if you are suffering from any of these and want help improving your API business, hit me up for a chat.

Back when I was a platform product manager, I use to quibble with my engineering teams about API design. They wanted to expose every feature of our products via generic APIs which often resulted in dozens of calls for even the simplest of operations (which we were hand-coding back then!). I wanted to craft APIs based on common developer use cases. At the time they thought I was being precious about it. But fast forward to 2026 and coding agents are choosing APIs. They’re gravitating toward the ones that were designed with intention from the start. Poorly designed APIs don’t work for autonomous systems. They need clarity. Consistency. Predictability. For legacy SaaS companies, this is urgent. Sure, you’ve got technical debt to overcome. But the bigger problem is organizational friction. Making the pivot to agent-ready API development might come at the cost of feature velocity to rebuild your APIs and add command line interfaces. It’ll hurt in the short term. It requires leadership conviction. But the companies that do it now will be the ones agents choose to work with. The ones that don’t will become increasingly difficult for autonomous systems to integrate with. And in an agentic future, it’s a competitive problem. SaaS is not going away. But legacy SaaS companies that don’t modernize will. You’re not competing against legacy peers anymore. You’re competing against new entrants building agent-ready platforms from scratch, moving at lightning speed, unencumbered by technical debt or organizational inertia. If you work for one of these older SaaS companies you have something the newcomers don’t. Existing customers and revenue. If you can move quickly to modernize, you have a real advantage in keeping those customers and winning new ones. No time to waste here.

RESTful API Design: Build APIs Developers Love 🔥 Every developer builds APIs, but not every API is built right. A poorly designed API leads to frustrated clients, brittle integrations, and endless bug reports. How do you avoid that? Here are key principles and common pitfalls to watch out for: 🔹 1. Resource-Oriented Design (Beyond Simple Nouns) ❌ Bad: /createOrder 🔴 ✅ Good: POST /orders ✅ 💡 Key Insight: APIs should focus on resources, not actions. CRUD operations should use standard HTTP methods, but some complex actions may need action-oriented endpoints (e.g., /processBatchOrders). 🔹 2. Use HTTP Methods Correctly (Don’t Break the Rules) ❌ Bad: GET /deleteOrder?id=5 🔴 ✅ Good: DELETE /orders/5 ✅ 📌 Guidelines: 🔹 GET → Read data (should be safe & idempotent) 🔹 POST → Create data (not idempotent) 🔹 PUT → Replace an entire resource (idempotent) 🔹 PATCH → Partially update a resource 🔹 DELETE → Remove a resource (idempotent) ⚠️Tip: Idempotency matters! Making the same PUT or DELETE request multiple times should produce the same result. 🔹 3. Smart API Versioning (Beyond Just URIs) ❌ Breaking changes without warning 🚨 ✅ Implement versioning: /v1/orders → /v2/orders ✅ 🛠 Versioning Strategies: 🔹 URI versioning: /v1/orders 🔹 Header-based versioning: Accept-Version: v1 🔹 Media type versioning: Accept: application/vnd.myapp.v1+json 🔎 Best Practice: Prioritize backward compatibility & document changes for API consumers. 🔹 4. HATEOAS: Dynamic API Navigation 💡 What is it? A technique where APIs embed hypermedia links to guide clients dynamically. ✅ Benefits: 🔹 Makes APIs more self-descriptive 🔹 Enables better decoupling between client & server 🔹 Supports evolution without breaking clients ⚠️ When to Use It: For highly dynamic or long-lived APIs, but beware—it adds complexity. 🔥 Takeaway: API design isn’t just about endpoints—it’s about creating a smooth, scalable, and predictable developer experience. 💬 What’s the worst API design mistake you’ve seen? Drop it below! 👇

In the race to develop Agentic AI, a risky and often overly reliant (or lazy) approach has gained traction: using "one-click" converters like FastMCP's OpenAPI wrappers to directly "flat dump" 10's and 100's of raw APIs into MCP tools for dynamic LLM-driven reasoning and orchestration to achieve complex goals without required eval or understanding. While using Claude or GPT-5 to probabilistically navigate modern APIs for vibe prototyping is likely, integrating them with legacy and third-party APIs will inevitably zoom the technical debt and operational failures. 📉 The Risks of the "flat dump" Approach Consider an MCP usage for the standard Order Remediation Workflow (CRM + Warehouse + Shipping) as an example. Here is why 1:1 API dumping fails. ❌ Probabilistic Math vs. Deterministic Logic: Asking an LLM to handle Inventory Split (calculating stock across locations to minimize shipping) offloads a math problem to a probability model. One slight hallucination leads to "ghost inventory" that is hard to debug. ❌ The "Token Tax": Dumping 100s of endpoints bloats the context window with irrelevant documentation. You pay for every api description in every single turn. ❌ Orchestration Entropy: Without a fixed path, an LLM might trigger a "Refund" before verifying "Inventory" on one run, then flip them on the next run. ❌ Over-Privileged NHI: To make 100s of tools work, the Non-Human Identity (NHI) account often gets "God Mode" access. One prompt injection could compromise your entire API fleet. ❌ Infinite Self-Healing Loops: Similar attributes (like cust_id vs customer_id) can cause the LLM to enter a recursive loop, repeatedly calling APIs to "fix" documentation discrepancies. 🛡️ The Mitigation: Intent-Based Engineering To build production-grade agents, move from stitching to intentional design: 🏗️ Build "Workhorse" APIs: If a task requires n+ sequential calls or math, don't offload it. Build a single Composite API (e.g., POST /execute-split-fulfillment) and expose that as the mcp tool versus overreliant on LLM orchestrations 🎯 Implement a semantic Tool Router: Instead of providing all local and remote execution mcp tools at once, implement semantic retrieval for relevant tools selection and injecting into the agent context for dynamic routing and optimizations 🔐 Least-Privilege Identity: Avoid shared agent identities and implement Non human agent identities (NHI) per agent role. If an mcp tool identity is scoped for "Orders" API it must reject any attempt on "Refunds" 🛑 Integration Guardrails: Apply Camel Patterns like circuit Breakers. If an agent calls the same update_status API too frequently, the system should automatically escalate to a human. 💡 Final Takeaway The goal of APIs to MCP abstraction is not the abdication of engineering responsibility. Use converters for your PoC, but move to Intent-Based APIs & routing with guardrails for your production workflows. #MCP #OpenAPI #APISecurity

𝗧𝗼𝗽 𝟲 𝗖𝗼𝗺𝗺𝗼𝗻 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗶𝗻 𝗥𝗘𝗦𝗧𝗳𝘂𝗹 𝗔𝗣𝗜 𝗗𝗲𝘀𝗶𝗴𝗻 Here are the six most consequential mistakes engineers make when designing RESTful APIs. 𝟭. 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗜𝗻𝘀𝗶𝗱𝗲-𝗢𝘂𝘁 𝗜𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗢𝘂𝘁𝘀𝗶𝗱𝗲-𝗜𝗻 Many engineers approach API design by directly exposing their internal data models or service functions, creating what API experts call "implementation leakage." This inside-out approach produces interfaces that force API consumers to understand their internal architecture rather than efficiently solving their use cases. 👉 Example: Stripe's early API versions exposed endpoints like `/accounts/create` that reflected their internal service methods. Their improved API now uses resource-focused endpoints like `/v1/accounts` with POST requests. 𝟮. 𝗜𝗺𝗽𝗿𝗼𝗽𝗲𝗿 𝗨𝗥𝗜 𝗗𝗲𝘀𝗶𝗴𝗻 URIs should identify resources through nouns, not actions or operations. This fundamental REST principle separates the "what" (resources) from the "how" (HTTP methods). ❌ Poor design: `/api/getUsers` or `/api/deleteUser/123`   ✅ Better design: `GET /api/users` or `DELETE /api/users/123` Well-designed resource naming creates intuitive hierarchies: `/organizations/{id}/teams/{id}/members` clearly expresses the relationship between resources. 𝟯. 𝗠𝗶𝘀𝘂𝘀𝗶𝗻𝗴 𝗛𝗧𝗧𝗣 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 HTTP methods have specific semantics that should be respected to benefit from web infrastructure: 🔹 GET: Retrieves resources (must be idempotent, cacheable, and free of side effects) 🔹 POST: Creates new resources or triggers processes 🔹 PUT: Replaces resources ultimately (must be idempotent) 🔹 DELETE: Removes resources Tools like RESTful Linters can automatically check API specifications for method usage violations, potentially saving up to 15 hours of debugging time per project. 𝟰. 𝗡𝗲𝗴𝗹𝗲𝗰𝘁𝗶𝗻𝗴 𝗘𝗿𝗿𝗼𝗿 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 Studies show that 29% of API integration time is spent handling errors and edge cases. Implementing a consistent error model improves the developer experience. Stripe's approach to error handling sets the industry standard: {  "error": {   "code": "resource_missing",   "message": "The requested resource doesn't exist",   "param": "id",   "type": "invalid_request_error"  } } Their structured errors include the following: 🔸 HTTP status codes that accurately reflect the error category 🔸 Machine-readable error codes for automated handling 🔸 Human-readable messages for debugging 🔸 References to the specific fields or parameters causing issues 𝟱. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻𝗶𝗻𝗴 𝗼𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 Practical versioning approaches include: 🔹 URI path versioning: `/api/v1/users` (used by Stripe, easy to understand) 🔹 Custom header versioning: `Accept-version: 1.0` (used by AWS, cleaner URIs) 🔹 Content negotiation: `Accept: application/vnd.github.v3+json` (used by GitHub, most technically correct) Each strategy has tradeoffs, but the absence of any versioning guarantees future pain.