Blog
Practical, vendor-neutral notes on running AI in production — how to verify what you're getting, govern cost, keep access stable, and keep your data yours. Hands-on, not hand-wavy.
How to Tell If You're Getting the Real Model
When you call an LLM through someone else's endpoint, how do you confirm you're getting the full model you asked for — not a diluted or degraded stand-in? Six vendor-neutral checks you can run on any provider — including us.
How to Keep LLM Access Stable for Mission-Critical Work
Access that stalls at peak and rate-limits on a deadline isn't reliable — it's lucky. Stability is engineered: dedicated capacity, around-the-clock monitoring and alerting, fast recovery, plus caller-side timeouts, retries, and a fallback path.
Where Your Data Goes When You Call an LLM
Every call ships your text to someone else's machine. Trace the hops, ask the four questions — retained, trained on, sold, accountable — and get the answers into a contract.
How to Govern LLM Token Cost Across a Team
LLM cost is usage-based and opaque. Six levers to turn token spend from a month-end surprise into a dial you control: per-key and per-team budgets, attribution, hard limits, right-sizing, caching, and near-real-time visibility.
AI Gateway vs Direct API: When to Use a Gateway
Direct is the simplest start, but it stalls as models and teams multiply and you need to govern cost and stability. When direct is fine, the signs you've outgrown it, what you don't give up, and a quick decision checklist.