Practical API guides, model comparisons, integration patterns and product updates.
A practical guide to designing an AI API usage reporting API that downstream customers can trust: tenant-scoped dimensions, provisional and finalized costs, cursor pagination, adjustments, reconciliation, and privacy-safe access control.
Read article →Runtime logs explain model calls, but incident response often depends on admin history: who created a key, changed a spend limit, promoted a routing profile, or revoked access. Build an append-only audit trail that joins gateway, Partner API, identity, provider, and usage events without storing raw prompts by default.
Read article →A Responses API gateway is not just a Chat Completions proxy with a new route. Preserve response items, state, tool calls, streams, reasoning continuity, usage attribution, and downgrade behavior with a first-class compatibility layer.
Read article →SaaS products, agencies, and reseller platforms need customer-level AI access without exposing upstream provider credentials. Use gateway-issued virtual keys as policy handles for tenant attribution, model access, budgets, rate limits, revocation, rotation, and usage ledgers.
Read article →Remote MCP servers and service connectors make agent workflows useful, but they also move authorization, retention, and incident response outside the model call. Treat connector authorization as a gateway control-plane object: token lifecycle, scope policy, approval checks, revocation state, and joined audit logs.
Read article →Provider price cards change by model, token category, cache behavior, tool usage, deployment type, region, and committed-capacity plan. A gateway needs a versioned pricing catalog so quotes, reservations, ledgers, budgets, and chargeback stay explainable when those prices drift.
Read article →Changing models through a multi-model API gateway should require evidence, not hope. Build eval datasets from real traces, grade candidates with deterministic and judge-based checks, and make promotion decisions part of the gateway control plane.
Read article →An AI API gateway can centralize runtime routing and billing while provider projects, workspaces, service accounts, API keys, limits, and reports still drift. Reconcile those upstream control planes against tenant policy before attribution, spend controls, and emergency actions diverge.
Read article →Partner API automation fails most often after the first request: timeouts, duplicate webhook events, concurrent workers, and money parsing mistakes. Build provisioning and credit workflows around durable operations, stable idempotency keys, exact decimal handling, and reconciliation.
Read article →Use SCIM and SSO as lifecycle inputs, then let the gateway enforce explicit roles, model profiles, spend authority, key ownership, and service-account transfer rules. The goal is fast offboarding without breaking production applications.
Read article →Shared upstream provider credentials can make prompt-cache behavior follow the gateway account instead of the tenant. Treat cache scope as a security boundary, test it explicitly, and choose credential modes that match workload sensitivity.
Read article →Reasoning-capable models expose different controls for thinking depth, token budgets, billing, and latency. Treat reasoning effort as a governed runtime policy in the gateway, not as a loose model setting inside each application.
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