Understand the control before you connect a model.
Start with a captioned walkthrough under three minutes, read the plain-language basics, or go directly to an operator workflow. Every resource states what works today and what remains your responsibility.
- Plain-language starting path
- Captioned product walkthroughs
- Operator SOPs with current boundaries
Start at your level. Finish with a working boundary.
Each path moves from an understandable outcome to the exact setup, validation, and responsibility split needed to use it safely.
AI Gateway HQ: the control boundary in under three minutes
Watch the working product turn provider access, routes, failover, policy, hard budgets, managed billing, and evidence into one understandable operating path.
Send your first governed AI request
Follow the shortest safe path from approved model supply to a bounded route, hard budget, one-time workload identity, test request, and reason-coded evidence.
Connect your provider API key safely
Keep one encrypted provider credential at the gateway boundary, verify it without inference, and give applications separate workload identities and approved routes.
Start with prepaid managed Amazon Bedrock
See how purchased credit, a reviewed model catalog, explicit routes, hard budgets, and bounded reloads create a no-provider-key starting path.
Set a hard AI budget before traffic starts
Set a workspace-wide ceiling, reserve exposure before provider dispatch, and keep managed credit reloads independently bounded.
Configure provider failover without bypassing controls
Build fallback from independent approved supply, remove unhealthy targets automatically, and fail closed when nothing qualifies.
Review and approve an AI cost insight
Move from retained metadata to one exact, version-bound routing change with human approval, audit evidence, rollback, and cautious outcome measurement.
Respond to an enterprise trust review safely
See how a bounded inquiry becomes a qualified, owner-approved evidence delivery without exposing sensitive contact or AI content.
Enterprise AI gateway evaluation: 12 production tests
Use a practical scorecard to compare AI gateways on integration, routing, cost control, security, evidence, and operating fit before live traffic.
What Is an AI Gateway? A Plain-Language Guide
Learn how an AI gateway controls access, model routing, spending, and operating evidence between your applications and AI providers.
Run Codex CLI through an AI gateway with Groq
Configure one governed route from Codex CLI through AI Gateway HQ to Groq, then verify model scope, tool compatibility, budget controls, and request evidence.
Automatic AI provider failover without silent policy drift
See how health circuits, quota cooldowns, capacity evidence, and bounded fallback keep an outage from becoming an uncontrolled model change.
AI gateway cost control: stop surprise spend before dispatch
A practical control plan for workload ownership, conservative reservations, provider limits, capped reloads, cost-aware routing, and invoice reconciliation.
Private-equity AI value realization
Connect portfolio-company AI spend, adoption, business results, evidence quality, and operating risk without turning activity into a fictional return.
A measurement system for AI across a private-equity portfolio
Separate independent market context from portfolio-owned spend, controls, adoption, and value evidence so operating partners can act without blending company data.
Govern AI without building a prompt warehouse
Use identity, policy, route, tool, cost, timing, and outcome metadata while minimizing retained content.
LLM cost attribution: reconcile provider bills
Compare provider financial totals with request-level gateway evidence, investigate variance, and allocate cost without uploading the source bill.
Use managed Bedrock without bringing a provider API key
Fund a bounded wallet, activate the approved managed model, create a safe route, and stop automatically when purchased credit is gone.