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A rolling landing page for the 15-part comparison series across LiteLLM, Kong, Portkey, and TrueFoundry.
Universal API, virtual models, self-hosted models
How broadly each gateway spans managed APIs and private inference without forcing apps to change integrations.
Multi-tenancy, projects, teams
How teams segment ownership, quotas, credentials, and access across shared enterprise AI platforms.
Fallbacks, latency, priorities
How requests fail over, shift between providers, and stay available under bursty or degraded conditions.
Simple cache, semantic cache, backends
Where each gateway cuts latency and token spend, and how configurable the cache strategy actually is.
Budgets, attribution, pricing models
How platforms enforce spend ceilings, allocate costs internally, and build real FinOps controls around AI usage.
Rate limiting, policy enforcement
How admins prevent shadow AI, restrict model access, and apply routing or policy decisions on live traffic.
Safety, redaction, custom hooks
How safety controls plug into the request path, including prompt checks, redaction, and custom enforcement logic.
Logs, traces, feedback, exports
How operators debug incidents, trace requests end to end, and push telemetry into their existing monitoring stack.
Tool call governance and MCP coverage
How each gateway handles MCP servers, tool-call auth, and the control plane needed for governed agent tooling.
Stateful execution patterns
Where the gateway stops being a pass-through and starts supporting longer-lived, stateful agent execution flows.
SaaS, VPC, on-prem, air-gapped
How each product fits enterprise deployment constraints, networking boundaries, and infrastructure operating models.
Sovereignty, exports, buckets
How teams keep logs, traces, and operational data inside the storage and geography boundaries they actually need.
Saved prompts, versioning, GitOps
How product and platform teams version prompts, parameterize them, and turn prompt changes into auditable releases.
Interactive testing and iteration
How fast teams can test prompts, compare outputs, and move from experimentation to controlled deployment.
RBAC, SSO, audit logs, certifications
How each gateway fits zero-trust requirements and enterprise compliance expectations around access and auditability.
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.
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.
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.
Meet security, residency, and infrastructure requirements across SaaS, VPC, on-prem, and air-gapped environments while keeping governance and auditability intact.
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