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Systems & infrastructure

tech platforms & systems architecture

Architectural evaluation of modern cloud platforms, AI inference clusters, serverless edge runtimes, and distributed systems. Evaluate latency, portability, unit economics, and lock-in risks before you build.

Infrastructure Pillars

Cloud Platforms

Platform Adoption Guide

Comprehensive architectural evaluation across serverless edge runtimes, GPU clusters, and modern cloud deployment models.

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Silicon Clusters

AI Inference Hardware

Profiling LPUs, unified-memory clusters, and GPU tensor parallelism for low-latency autoregressive token generation.

Hardware Benchmarks
Edge Computing

V8 Isolates & MicroVMs

Comparing lightweight JavaScript isolate execution models with Firecracker micro-virtual machines for multi-tenant code isolation.

Systems Analysis
Architectural Evaluation

Five Hard Questions Before Choosing a Cloud or Inference Platform

A platform tier is more than marketing claims and initial free credits. Before building your engineering infrastructure on any provider, evaluate these foundational operational boundaries:

⚡ Latency vs. Throughput Curves

Profile cold-start behavior, Time-To-First-Token under high concurrency, global points of presence, and HTTP/2 Server-Sent Events (SSE) streaming stability.

🔓 Vendor Lock-In Defense

Quantify the migration effort. Prioritize providers running standard OCI Docker containers over proprietary edge isolates with closed database bindings.

💸 Egress & Unit Economics

Audit network bandwidth egress penalties, per-token billing multipliers, and cost curves when request traffic scales by an order of magnitude.

2026 Systems Architecture Deep-Dives

Analysis of production platform choices, silicon shifts, and state management patterns.

Silicon Innovation

Custom LPUs & SRAM Processing

Architectures shifting from high-latency HBM memory to on-chip SRAM LPUs like Groq, enabling sub-20ms generation speeds for conversational AI.

Explore Silicon Analysis
Distributed Compute

Edge WebAssembly Micro-Runtimes

WASM and WebGPU bringing lightweight, zero-cold-start sandboxed compute directly to distributed points of presence worldwide.

Explore Edge Runtimes
State Management

Embedded SQLite & Vector Storage

Why relational databases with pgvector and embedded SQLite (Turso libSQL) frequently outperform complex standalone vector databases in production.

Explore Data Storage
Agent Isolation

Firecracker MicroVM Sandboxing

Sub-5ms ephemeral virtual machine provisioning on Fly.io and AWS for safe execution of untrusted code and autonomous agent tool loops.

Explore Sandboxing

Frequently asked questions

Architectural decisions on cloud lock-in, serverless tradeoffs, and inference costs.

How do I avoid cloud vendor lock-in when building modern web applications?
Standardize on open container formats (Docker/OCI) and runtime-agnostic protocols like standard Web APIs (fetch, Request, Response). Avoid building deep dependencies on proprietary cloud vendor APIs when standard open-source equivalents (e.g. PostgreSQL over proprietary document stores) exist.
What is the latency difference between edge inference and centralized GPU clusters?
Centralized GPU clusters (like AWS us-east-1) typically introduce 70ms-150ms of network transit latency depending on client location. Edge inference runs models within 10ms-30ms of users at global points of presence, dramatically improving Time-to-First-Token for interactive user experiences.
Why are custom inference LPUs (like Groq) faster than standard GPUs for LLMs?
Standard GPUs are optimized for parallel graphics and matrix batching with high memory latency. Language Processing Units (LPUs) utilize on-chip SRAM with instantaneous memory bandwidth, eliminating memory bus bottlenecks for sequential autoregressive token generation.
When should an engineering team migrate from serverless to dedicated compute?
Serverless is ideal for spiky, unpredictable traffic and rapid iteration. When baseline request concurrency becomes continuous and monthly cloud bills exceed the cost of dedicated instances (often around $2,000-$5,000/month in serverless execution and egress), migrating core services to dedicated containers or Kubernetes yields significant margin savings.

Systems are decisions, not checklists

A platform is more than a marketing tier. The crucial question is whether it can carry your operational workload safely, predictably, and without creating an insurmountable maintenance or billing debt your team cannot afford.