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.
Measuring RAG quality: beyond retrieval hit rates
Retrieval hit rate tells you if your search is finding relevant documents. It doesn't tell you if your users are getting correct answers. Here's how to build a complete RAG quality measurement system.
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.
Multi-region LLM routing: data residency, failover chains, and latency trade-offs
How to design LLM request routing that respects EU/US data residency constraints, survives regional provider outages, and does not sacrifice latency to do it.
Running tool-using agents safely in production
How to constrain tool-using LLM agents in production: tool allowlists, HTTP egress policies, human-in-the-loop approvals, per-run spend caps, and auditing tool calls.
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.
Semantic caching pitfalls: when cached answers go stale
Semantic caching cuts LLM costs dramatically — but serving outdated completions can be worse than serving no completion at all. Here's how to tune cache policy so staleness doesn't become a production incident.
MCP in the enterprise: tool calling with guardrails
How to deploy Model Context Protocol agents in enterprise environments without trading off control — covering tool scoping, confirmation policies, audit logging, and injection defense.
What actually breaks when you take AI fully offline
A practical breakdown of every network call that modern AI stacks make — model weights, embeddings, tokenizers, telemetry, license checks — and how to design for zero outbound traffic.
Migrating embedding models without breaking search
A step-by-step approach to swapping embedding models in production: dual-write, background reindexing, model evaluation before cutover, and atomic collection switching.
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.
Choosing between hosted APIs and self-hosted open-weight models
A framework for deciding when to call a hosted model API versus running an open-weight model on your own infrastructure — covering cost, latency, compliance, and capability trade-offs.
Prompt injection defenses that actually work in production
A practical look at the prompt injection threat landscape and the layered gateway controls — input validation, output scanning, and privilege separation — that reduce real risk.
Build vs. buy for LLM gateways: an honest decision framework
What a homegrown LLM proxy reliably gets wrong, a total cost of ownership breakdown, and a checklist for evaluating off-the-shelf gateway solutions.
Zero-trust access for AI: virtual keys, RBAC, and key rotation without downtime
How to apply zero-trust principles to LLM infrastructure: virtual API keys, per-key scopes, team RBAC, model allowlists, audit trails, and rotation that doesn't page your users.
LLM observability: traces, token accounting, and cost attribution
What to instrument in an LLM application, how to attribute cost and latency to features and users, and which signals actually matter for alerting.
Reliably getting structured outputs from LLMs in production
A production engineering guide to JSON mode, schema validation, retry strategies, and gateway-layer output guardrails for structured LLM responses.
Token budgets and chargeback: making AI spend accountable
How to implement per-team token budgets, showback reporting, and real chargeback at the gateway layer — so AI costs stop being a mystery line on the cloud bill.
Fine-tuning vs RAG vs prompting: a decision framework
A practical guide to choosing between prompt engineering, retrieval-augmented generation, and fine-tuning — and how to combine them when none alone is enough.