AI Backend Engineering
Focused ai backend engineering articles with clear context, practical examples, source links where needed, and honest limits.
15 articles in this section.
AI agent memory: files, databases, embeddings, and task history
AI agent memory is not one feature. Compare files, databases, embeddings, and task history for reliable long-running workflows.
Open-model licenses: the checklist developers should use before shipping
Open weights do not automatically mean unrestricted commercial use. Use this developer checklist before putting an open model into production.
AI model routing: how to balance cost, latency, and quality
A practical guide to routing AI requests across models using workload, latency, quality, and budget signals instead of guesswork.
AI agent observability: the metrics that reveal runaway workflows
AI agents need more than logs. Track turns, tools, retries, approvals, cost, and outcomes to find runaway workflows before users do.
Open models and AI infrastructure: who pays when the model is free?
Open AI models may be free to download, but hosting, fine-tuning, evaluation, and support still cost money. Here is the real economics.
AI inference pricing: why cost per token is not the full bill
Token prices are only one part of an AI bill. Learn how retries, context, latency, storage, and reserved capacity change inference cost.
AI agents and inference economics: why every tool call has a cost
AI agents can multiply inference, tool, and storage costs. Learn how to budget agent runs and keep autonomous workflows reliable.
AI training data provenance: what developers should check before using a dataset
A practical dataset-provenance checklist covering licenses, source records, consent, transformations, and reproducibility for AI developers.
Reserved versus on-demand AI compute: which model fits a growing product?
Reserved AI capacity can improve reliability, while on-demand compute preserves flexibility. Here is how developers should compare both options.
OpenAI's multi-chip strategy: NVIDIA, AMD, Trainium, and Cerebras
OpenAI's mix of NVIDIA, AMD, AWS Trainium, and Cerebras shows why AI companies are diversifying chips by workload instead of betting on one supplier.
OpenAI and Snowflake's $200 million partnership: why enterprise data is the prize
What the OpenAI-Snowflake partnership means for enterprise agents, governed data access, model choice, and the hidden engineering work behind useful AI features.
The new Microsoft-OpenAI agreement: cloud access, revenue share, and developer impact
A simple explanation of the amended Microsoft-OpenAI relationship, including Azure's role, revenue sharing, model access, and what developers should verify.
Why OpenAI is adding Cerebras for low-latency inference
Why OpenAI is adding Cerebras systems to its inference mix, how low latency differs from raw model quality, and what the change means for real-time AI applications.
OpenAI and Amazon's Trainium deal: what the 2-gigawatt commitment means
A plain-English explanation of OpenAI's expanded AWS relationship, Trainium capacity, agent workloads, and the questions worth asking.
AI infrastructure costs explained: GPUs, cloud choices, and inference
A plain-English guide to the costs behind AI applications, including GPU capacity, inference, cloud trade-offs, model routing, and the numbers worth measuring.