AWS
AWS is the default substrate for the SaaS platforms I build: EC2 and Lambda for compute, RDS and ElastiCache for data, CloudFront for edge delivery, and SQS for decoupled workers. The design question is never which service exists - it is how you compose them so failure in one layer does not become customer-visible downtime.
I focus on patterns that survive real traffic: multi-AZ API platforms with clear read/write routing, secrets rotation without deploy windows, CDN cutovers that preserve cache efficiency, and worker fleets that handle poison messages without stalling the queue.
The posts collected here include highly available API platform design, secrets management for platform teams, and AI API infrastructure where cost and rate limits matter as much as uptime. They reflect Edmingle-scale production work, not reference architecture slides.
- Designing a Highly Available API PlatformHigh availability is explicit failure modes with mitigations-multi-AZ compute, RDS failover, graceful degradation, and a failure matrix you actually game-day.
- Secrets Management for Platform TeamsHardcoded API keys in .env files do not survive a team of ten, three environments, and a compliance audit. Here is the secrets management approach I implement for platform teams on AWS.
- Designing Reliable AI API InfrastructureAI APIs fail differently from CRUD APIs - provider rate limits, token budgets, and unpredictable latency need a dedicated reliability layer. Here is how I design inference infrastructure on AWS that survives real traffic.
Related writing
AI Infrastructure
AI Agent Infrastructure and Failure Modes
AI agents combine LLM unpredictability with tool execution and long-running state - which creates failure modes traditional APIs never had. These are the ones I have seen in production and how I design around them.
- AI Agents
- Reliability
- Security
Benchmarking
API Load Benchmarking for Platform Teams
Load tests that only report RPS miss the point. Platform teams need benchmarks that answer capacity questions, find breaking points before users do, and produce numbers engineering leadership can act on.
- Benchmarking
- APIs
- Performance
Benchmarking
Designing Benchmark Suites for Platform Changes
Green application CI does not catch an 18% P99 regression from a node image swap. How to build layered platform benchmark suites, statistical regression gates, and dedicated environments that validate substrate changes.
- Benchmarking
- Performance
- Platform Engineering
System Design
Designing a Highly Available API Platform
High availability is explicit failure modes with mitigations-multi-AZ compute, RDS failover, graceful degradation, and a failure matrix you actually game-day.
- AWS
- System Design
- APIs
Platform Engineering
Designing an Internal Developer Platform
An IDP is golden paths and self-service guardrails-not just a portal. Map developer journeys, ship templates first, and measure DORA metrics plus developer satisfaction.
- Platform Engineering
- Developer Experience
- CI/CD
Kubernetes
Designing a Production-Ready Kubernetes Platform
A production K8s platform is RBAC, ingress, secrets, observability, and GitOps-not just a cluster. Here is the layered checklist I use before onboarding product teams.
- Kubernetes
- Platform Engineering
- AWS
AI Infrastructure
Designing Reliable AI API Infrastructure
AI APIs fail differently from CRUD APIs - provider rate limits, token budgets, and unpredictable latency need a dedicated reliability layer. Here is how I design inference infrastructure on AWS that survives real traffic.
- AI
- APIs
- AWS
AI Infrastructure
Evaluating AI Coding Agents in CI
HumanEval scores do not predict whether an agent will break your migrations. A fixture-based eval harness, autonomy tiers, and CI gates that treat agent patches with the same distrust as human ones.
- AI Agents
- CI/CD
- GitHub Actions
Observability
How to Debug a Slow API in Production
When P99 spikes but errors stay flat, binary-search the request path with metrics and traces-starting at the edge, through the app, and into MySQL, Redis, and downstream dependencies.
- Observability
- SRE
- APIs
Kubernetes
Cost-Aware Autoscaling on Kubernetes
HPA, VPA, and Cluster Autoscaler fight each other when requests are wrong and metrics lie. A production playbook for scaling signals, instance mix, and scale-down discipline that protects SLOs without inflating the EKS bill.
- Kubernetes
- Cost
- AWS
AI Infrastructure
LLM Gateway Architecture
An LLM gateway is the control plane between your applications and model providers. Here is the architecture I use to centralize routing, rate limiting, cost accounting, and observability for every inference call.
- AI
- Architecture
- APIs
Architecture
Multi-Tenant SaaS Isolation Patterns
White-label platforms like Edmingle sit on an isolation spectrum from shared RLS to dedicated clusters. A production-oriented guide to data boundaries, Kubernetes tenancy, noisy-neighbor controls, and tier migrations.
- SaaS
- Architecture
- Security
Observability
Observability for LLM Applications
Standard APM tells you the request was slow - it does not tell you why the model hallucinated or which prompt version caused a quality regression. Here is the observability stack I use for LLM applications in production.
- Observability
- AI
- OpenTelemetry
Platform Engineering
Platform Engineering in the Age of AI Agents
AI agents change what platform teams must provide - not just CI/CD and observability, but guardrails for autonomous, non-deterministic workloads. Here is how I expect platform engineering to evolve and what I am building toward today.
- Platform Engineering
- AI Agents
- Governance
Platform Engineering
Platform Engineering vs DevOps
DevOps is how teams collaborate on delivery; platform engineering productizes the infra layer so that collaboration scales. One did not replace the other.
- Platform Engineering
- DevOps
Security
Policy as Code for Kubernetes Gatekeeping
Wiki standards do not stop :latest tags and root containers. A Gatekeeper and Kyverno playbook for admission control, audit-then-enforce rollouts, and CI integration that actually changes developer behavior.
- Kubernetes
- Security
- Policy
AI Security
Prompt Injection Defenses for Internal Agents
SSO does not stop an injected Jira ticket from triggering a production runbook. Defense layers for internal AI agents-tool allowlists, context separation, approval gates, and blast radius containment.
- AI Agents
- Security
- Platform Engineering
Event Systems
Redis Streams vs SQS for Event Backbones
Choosing between Redis Streams and SQS is not a benchmark comparison - it is a question about operational model, consumer patterns, and how much Redis you already run. I built a production worker framework on Redis Streams at Edmingle; here is what I learned.
- Redis
- AWS
- Distributed Systems
Security
Secrets Management for Platform Teams
Hardcoded API keys in .env files do not survive a team of ten, three environments, and a compliance audit. Here is the secrets management approach I implement for platform teams on AWS.
- Security
- AWS
- Platform Engineering
Reliability
Webhook Idempotency in Production
Payment providers like Razorpay, Stripe, and PayU will deliver the same webhook more than once. If your handler is not idempotent, you will double-charge, double-enroll, or double-ship. Here is the pattern I use in production.
- APIs
- Reliability
- Payments