When to use enterprise cloud AI platforms (Bedrock, SageMaker, Azure / Microsoft Foundry, Vertex AI) versus running open-weights models locally with Ollama, LM Studio, llama.cpp, or vLLM - with a decision framework and trade-off tables.
Strapi configuration and deployment: environment config, database setup, server settings, production hardening, Docker, PM2, and cloud deployment.
Deploy your Java REST API to a VPS - installing the JDK, running as a systemd service, configuring nginx as a reverse proxy, and HTTPS with Let's Encrypt.
Deploy your website to a Virtual Private Server - server setup, SSH, nginx configuration, HTTPS with Let's Encrypt, and basic security hardening.
Deploying AEM projects varies significantly depending on the hosting model. AEM as a Cloud Service (AEMaaCS) relies on Cloud Manager pipelines with strict immutability rules. Adobe Managed Services (AMS) provides a middle ground with managed infrastructure but more flexibility. On-premises installations give full control but require manual orchestration and operational discipline.
How to run Strapi 5 behind a load balancer: stateless instances, shared state, upload providers, secrets, cron locking, webhooks, cache invalidation, connection pools, migrations, rolling deploys, and the admin panel.
How production LLM serving works, covering prefill, decode, KV cache sizing, batching, prefix reuse, speculative decoding, quantization, parallelism, engines, autoscaling, and benchmarks.