AI Infrastructure · Spire Digi Solution

Enterprise AI Gateway & Model Router

One control plane for every LLM call in the org — routing, cost, reliability and security in one place.

Delivered at: Spire Digi Solution
Role: Lead architect — routing model, data model, API contracts and delivery blueprint.

The problem

Applications often hard-code a single model and provider, which makes cost control, reliability, security and provider migration painful the moment something changes upstream. This gateway sits in front of every model call so routing, budgets and failover are handled once, centrally, instead of duplicated — or forgotten — in every application.

Architecture

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Enterprise AI Gateway & Model RouterProduction architecture — request flows top → bottom; security & observability span every layerClientslayer 1Client SDK / REST APIAdmin consoleEdge & networklayer 2WAF & DDoS shieldL7 filtering · bot controlAPI gatewayrouting · versioningRate limits & quotasper tenant / per keymTLS · TLS 1.3encrypted in transitIdentity & accesslayer 3OIDC / OAuth2SSO · short-lived tokensRBAC + ABACleast privilegeTenant isolationdata · vectors · toolsSecrets → Vaultno static credentialsAI security & guardrailslayer 4focusNeMo Guardrailstopical & safety railsInjection / jailbreak filterinput inspectionPII & secret redactioninbound & outboundPolicy engine · OPAallow / deny decisionsOutput validationgrounding · citationsGateway & routing corelayer 5Request normalizerprovider-agnostic schemaRoutercost · latency · quality awareProvider adaptersOpenAI · Claude · Gemini · OllamaFallback chainsauto on degrade / failureResponse validationschema · safety · PIIUsage & cost accountingper tenant · per keyExecution & toolslayer 6Semantic cachededupe + reuseQuota & budget enforcementrate limits · token capsData-policy filteregress controlModel layerlayer 7Hosted providers · normalizedSelf-hosted · vLLM / TritonEmbeddings + rerankHealth & degradation probesData & statelayer 8PostgreSQLpgvector / QdrantRedis cacheObject storageImmutable audit logcross-cutting — applied across every layer aboveSECURITY & COMPLIANCE · CROSS-CUTTINGSIEM & threat detectionruntime alertsSupply-chain scanningTrivy · Semgrep · GitleaksSBOM & image signingprovenanceAdversarial mappingOWASP LLM Top 10 · MITRE ATLASComplianceEU AI Act · ISO 42001 · NIST AI RMFOBSERVABILITY & OPS · CROSS-CUTTINGOpenTelemetry tracesprompts · steps · toolsMetricsPrometheus / GrafanaEvaluation & regression gatesblock bad releases in CICost & latency analyticsper tenant / routeAlerting & on-callSLOs · error budgetsCI/CD · IaCKubernetes · GitOpsTEN ARCHITECTURE PILLARS · REVIEWED END TO ENDScalabilitySecurityAI Security / GuardrailsReliability & ResilienceObservabilityGovernanceCost OptimizationData & RAG SecurityPerformanceMaintainability / DevSecOpsLegendSecurityGovernance / accessOrchestrationData / modelProcessing / executionObservability / storageOne control plane in front of every LLM call — routing, budgets, failover and policy handled once, centrally, instead of in every application.

Key capabilities

Cost-, latency- and quality-aware routing across hosted and open-source models

Per-tenant quotas, rate limits and token budgets

Automatic fallback chains when a provider degrades or fails

Response caching and PII / data-policy enforcement at the gateway

Live cost and latency dashboards with a full request audit trail

Architecture pillars

Designed and reviewed against all ten — see how I evaluate every architecture.

ScalabilitySecurityAI Security / GuardrailsReliability & ResilienceObservabilityGovernanceCost OptimizationData & RAG SecurityPerformanceMaintainability / DevSecOps

Technology

PythonFastAPIReactTypeScriptPostgreSQLRedisOpenAI / Claude / Gemini / Ollama adaptersOpenTelemetryDockerKubernetes

Want something like this, built properly?

I design and ship systems like this one — from architecture through to production.

Enterprise Agentic AI Operating Platform Secure Enterprise RAG / Knowledge Intelligence Platform