Enterprise AI Control Plane

Control, secure, and observe every enterprise AI request.

Infralo sits between microservices and upstream model providers. Virtualize API keys, enforce runtime pre/post guardrails, load balance with sub-4ms cache hits, and capture complete multi-span latency traces.

100% OpenAI Client Drop-in
Sub-4ms Gateway Overhead
Zero Provider Secret Sprawl
AI Applications & Agents Python SDK · Node.js · LangChain
1-Line Drop-in
Infralo Control Plane
Active Proxy · Sub-4ms
Identity & IAM
Virtual Keys ✓
Routing & Mesh
Sub-4ms Cache ✓
Guardrails
Pre/Post Hooks ✓
Observability
Span Traces ✓
OpenAI Anthropic Gemini Ollama
Auto Failover Active
Platform Architecture

Four foundational pillars for production AI infrastructure.

Infralo decouples provider credentials from client code, intercepts requests at runtime with custom security hooks, routes traffic across dynamic fallback clusters, and records full-fidelity telemetry.

PILLAR 01 IAM & Security

Virtual API Keys & Secret Virtualization

Store upstream provider keys securely in encrypted enterprise vaults. Issue scoped Virtual API Keys to microservices with hard token limits, model whitelists, rate limits (RPM), and cost ceilings.

Granular budget caps & per-key auto-revocation
Model-level whitelisting (prevent unauthorized gpt-4 usage)
PILLAR 02 Runtime Execution

Pre & Post Pipeline Runtime Modules

Execute low-latency interceptor hooks before and after LLM inference. Redact customer PII, sanitize prompt injections, enforce schema validation, and route through human-in-the-loop approvals.

In-line PII redaction and regex token masking
JSON schema compliance check before client return
PILLAR 03 Routing & Resilience

Smart Mesh Routing & Sub-4ms Cache

Eliminate provider downtime. Infralo detects upstream 5xx errors or latency spikes in milliseconds and automatically reroutes requests to hot backup providers (OpenAI → Anthropic → Gemini).

Sub-4ms exact and semantic caching layer
Automated cross-provider failover with zero app code changes
PILLAR 04 Telemetry & FinOps

Multi-Span Tracing & Cost Attribution

Complete visibility into every LLM request. Inspect multi-span latency breakdowns, prompt/completion token usage, cache hit ratios, and attribute exact dollar spend down to individual engineering teams.

Waterfall spans for ingress, hooks, upstream, and egress
Real-time streaming log explorer with OpenTelemetry export
Developer Experience

A single line of configuration. Zero SDK lock-in.

Infralo is wire-compatible with the standard OpenAI API specification. Keep your existing client libraries and LangChain agents—just update your base_url to Infralo Control Plane.

# 100% Drop-in with standard OpenAI Python client
from openai import OpenAI

client = OpenAI(
    # Point to your Infralo AI Control Plane gateway
    base_url="https://api.infralo.com/v1",
    
    # Authenticate with your scoped Virtual API Key
    api_key="inf_live_938a0f92b7c1"
)

# Executes runtime guardrails, failover routing, and telemetry automatically
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Analyze quarterly metrics."}]
)

print(response.choices[0].message.content)
Architectural Comparison

Direct vendor sprawl vs. central control plane.

Why leading enterprise engineering teams transition from unmonitored direct API keys to Infralo.

Status Quo: Direct Provider Sprawl

Scattered credentials & blind execution

Microservices embed raw vendor master keys directly in environment variables. When a provider suffers a 503 outage or rate-limit spike, downstream applications crash immediately.

  • Secret sprawl across microservice repos with zero revoke audit
  • Raw customer PII dispatched unredacted to external AI vendors
  • Complete application downtime during upstream provider outages
  • Zero unified cost visibility or per-team budget enforcement
Infralo Enterprise Control Plane

Centralized governance & resilient routing

Master credentials remain locked in your KMS. Applications receive fine-grained Virtual Keys with automated pre/post pipeline guardrails, sub-4ms cache hits, and automatic failovers.

  • Zero master keys in application code; 1-click key revocation
  • In-flight PII redaction and prompt injection sanitization
  • Sub-4ms automated failover mesh across OpenAI, Anthropic, Gemini
  • Multi-span latency waterfalls and exact token spend telemetry
Security & Compliance

Built for enterprise compliance and private VPC deployment.

Infralo is architected with strict Zero Data Retention (ZDR) and runs either as a managed multi-region control plane or fully self-hosted inside your Kubernetes cluster.

ISOLATION

Self-Hosted & VPC Native

Deploy Infralo as a private Helm chart on AWS EKS, GCP GKE, or bare-metal Kubernetes. Sensitive prompts never leave your corporate perimeter.

PRIVACY

Zero Data Retention (ZDR)

Prompts and responses are processed in memory and never persisted to disk unless explicit audit logging is enabled by your security administrator.

ENCRYPTION

AES-256-GCM & CMEK

All credentials and metadata are encrypted with customer-managed encryption keys (CMEK) via AWS KMS, GCP Cloud KMS, or HashiCorp Vault.

Questions & Answers

Technical specifications & architecture details.

Everything you need to know about integrating Infralo into your engineering stack.

Infralo introduces sub-4ms of proxy latency overhead for authentication, Virtual Key validation, and routing. When a semantic or exact cache hit occurs, Infralo returns the cached completion in under 12ms total, bypassing upstream provider latency entirely.
Infralo is 100% wire-compatible with the standard OpenAI API specification (/v1/chat/completions, /v1/responses, /v1/embeddings). You do not need to replace your SDK or rewrite agent logic; simply configure base_url="https://api.infralo.com/v1" and pass your Infralo Virtual Key.
You can configure deployment routing targets with prioritized fallback lists (e.g. Primary: GPT-5.6 Sol, Secondary: Claude 4.8 Sonnet, Tertiary: Gemini 3.1 Pro). When Infralo detects an upstream 5xx response, connection timeout, or 429 rate-limit spike from any provider, it transparently translates the payload and dispatches to your designated backup provider with zero application downtime.
Yes. Infralo is distributed both as a multi-region managed cloud service and as a self-hosted enterprise package. You can run Infralo on your private Kubernetes clusters (AWS EKS, GCP GKE, Azure AKS) via Helm, ensuring zero external telemetry or secret leakage.
Each Virtual API Key is assigned a monthly spending cap in USD, rate limits (RPM/TPM), model whitelists, and expiration timestamps. The Infralo gateway tracks usage in real time and automatically rejects out-of-policy requests with descriptive RFC 7807 error responses before any upstream provider charges occur.

Ready to take control of your enterprise AI infrastructure?

Schedule a technical architecture review with an Infralo solutions engineer, or explore our documentation to test a self-hosted cluster in minutes.

Read Documentation ↗