Practical engineering articles from the Galacterium Labs team. No filler, no sponsored content — just honest technical writing from practitioners who build these systems every day.
Designing and deploying large language model inference pipelines that are cost-efficient, observable, and reliable at production traffic volumes. We cover model serving strategies, context management, structured output validation, and the operational tooling required to run LLMs responsibly in production.
A practical walkthrough of implementing zero-trust network architecture in a multi-account AWS environment, covering identity federation, service mesh mTLS, workload identity, and the policy-as-code patterns that make the model auditable and maintainable over time.
The lakehouse pattern has reshaped how organisations think about analytical infrastructure, but it is not always the right choice. This article provides a decision framework for selecting between warehouse, lakehouse, and hybrid architectures based on access patterns, team maturity, and total cost of ownership.
Our first articles are in preparation. If you would like to be notified when they go live, send us a note at support@galacteriumlabs.com with the subject line "Blog Updates" and we will add you to the list.