AI infrastructure engineering
Ranking models with ELO: evals your team will trust
Why pairwise ELO scoring produces more actionable model comparisons than aggregate benchmarks — and how to run it against your real gateway traffic.
Rolling out SSO and SCIM for your AI platform
A practical guide to connecting your identity provider to ManyLayers so user provisioning, team budgets, and RBAC stay in sync automatically.
What belongs in your AI audit log
The specific fields, retention requirements, and export patterns that make an AI audit log useful for compliance, incident investigation, and cost attribution.
The economics of semantic caching for LLM traffic
How semantic caching cuts redundant provider spend — and what to measure to know it's working.
Hybrid deployments: control plane vs data plane
How ManyLayers separates the control plane from the data plane to let enterprises keep sensitive traffic on-premises while using cloud services for management and orchestration.
Connector-driven RAG: keeping your knowledge base fresh
How to design a connector sync strategy that keeps your AI knowledge base current — without degrading retrieval quality or overwhelming your embedding pipeline.
Canary routing for LLM traffic: safe model upgrades without downtime
How to use ManyLayers Gateway's weighted routing to roll out a new model version to 5% of traffic before committing — and roll back in seconds if quality drops.
Building a PII firewall for enterprise AI
A practical guide to detecting and redacting personally identifiable information before it reaches any model provider — using ManyLayers Gateway guardrails.
Self-hosting open-weight models with minimal operational footprint
How ManyLayers Deploy lets you run Llama, Mistral, Qwen, and other open-weight models on your own hardware — Kubernetes or dstack — with the same OpenAI-compatible API your existing code already uses.