Self-Hosted AI Gateway: Deploy in 7 Steps

NeuralTrust’s October 2026 deployment guidance describes four AI gateway models—SaaS, Private hybrid, fully self-hosted External, and a Central control plane—with the Private Kubernetes data plane presented as the quickest option for organizations that need raw prompts and responses to remain within their own network while using NeuralTrust’s managed control plane. A Private deployment requires a Kubernetes cluster, Redis, PostgreSQL, ingress and DNS, a registry key available through an enterprise offer, outbound access to NeuralTrust services and model providers, and separate TLS-protected LLM and MCP endpoints on ports 8081 and 8082. Installation uses Kubernetes Secrets, a Helm chart, configuration-sync and DataAgent credentials, while production guidance recommends external managed data stores, pinned chart versions, two proxy and MCP replicas by default, a single DataAgent, and pod disruption budgets. Verification includes checking workload readiness, testing allowed and blocked requests, and confirming that raw telemetry remains in PostgreSQL while metadata passes through an in-cluster collector to NeuralTrust. The guidance stresses that self-hosting alone does not mitigate risks such as prompt injection, data disclosure, excessive agency, or uncontrolled spending, recommending encrypted and access-controlled secrets, restricted network paths, per-application keys or identity-provider authentication, and enforced policies, budgets, rate limits, and guardrails. It also notes operational tradeoffs, including responsibility for upgrades, certificates, backups, and recovery, and explains that running pods can use cached configuration during a control-plane outage but restarted pods cannot become ready without reconnecting.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.