AI Gateway Series
Phase 3 of 5

AI Gateway Comparison Series

A rolling landing page for the 15-part comparison series across LiteLLM, Kong, Portkey, and TrueFoundry.

⏱ 9 live now ⏳ 6 more coming soon 👤 Platform Engineering · AI Infra

Browse the first 3 comparisons

Blog 01 Link ready

Model Coverage

Universal API, virtual models, self-hosted models

How broadly each gateway spans managed APIs and private inference without forcing apps to change integrations.

  • Universal API
  • Virtual Models
  • Self-hosted Models
Read comparison →
Blog 02 Link ready

Org & Team Management

Multi-tenancy, projects, teams

How teams segment ownership, quotas, credentials, and access across shared enterprise AI platforms.

  • Multi-tenant orgs
  • Projects & Teams
  • Environment isolation
Read comparison →
Blog 03 Link ready

Routing & Load Balancing

Fallbacks, latency, priorities

How requests fail over, shift between providers, and stay available under bursty or degraded conditions.

  • Weight-based routing
  • Latency-aware routing
  • Provider/model fallbacks
Read comparison →
Blog 04 Link ready

Caching & Performance

Simple cache, semantic cache, backends

Where each gateway cuts latency and token spend, and how configurable the cache strategy actually is.

  • Simple caching
  • Semantic caching
  • Cache backends
Read comparison →
Blog 05 Link ready

Cost Control

Budgets, attribution, pricing models

How platforms enforce spend ceilings, allocate costs internally, and build real FinOps controls around AI usage.

  • Team/user budgets
  • Cost attribution
  • Custom pricing
Read comparison →
Blog 06 Link ready

Governance

Rate limiting, policy enforcement

How admins prevent shadow AI, restrict model access, and apply routing or policy decisions on live traffic.

  • Rate limiting
  • Metadata filtering
  • Request policy
Read comparison →
Blog 07 Link ready

Guardrails

Safety, redaction, custom hooks

How safety controls plug into the request path, including prompt checks, redaction, and custom enforcement logic.

  • Partner guardrails
  • Custom hooks
  • PII redaction
Read comparison
Blog 08 Link ready

Observability

Logs, traces, feedback, exports

How operators debug incidents, trace requests end to end, and push telemetry into their existing monitoring stack.

  • Logs & retention
  • OTel traces
  • Alerts & exports
Read comparison
Blog 09 Link ready

MCP Gateway

Tool call governance and MCP coverage

How each gateway handles MCP servers, tool-call auth, and the control plane needed for governed agent tooling.

  • Custom MCP coverage
  • Virtual MCP servers
  • MCP observability
Read comparison

What an enterprise AI gateway should solve

A strong AI gateway does more than normalize APIs. It gives platform teams one control layer for model access, traffic policy, spend, observability, and enterprise deployment constraints.

Standardize model access

Support managed APIs, private inference, and virtual models behind one stable interface so teams can ship faster without hard-coding provider choices into every application.

Control cost and reliability

Route intelligently, fail over safely, cache where it matters, and make spend visible by team, app, and model before production usage turns into operational drag.

Deploy for enterprise reality

Meet security, residency, and infrastructure requirements across SaaS, VPC, on-prem, and air-gapped environments while keeping governance and auditability intact.