Use the same model, payload, region, streaming settings, and connection conditions.<\/p>\n\n\n\n
Run the test with caching, retries, fallbacks, and guardrails disabled first. Then enable each feature separately.<\/p>\n\n\n\n
That gives you the gateway’s actual overhead instead of repeating a vendor benchmark that may have been measured under different conditions.<\/p>\n\n\n\n
Which to Pick<\/h2>\n\n\n\n There is no useful universal winner because these products are optimized for different environments.<\/p>\n\n\n\n
Choose Portkey when<\/h3>\n\n\n\n You want an AI-native gateway with routing, fallbacks, observability, guardrails, and governance already designed around LLM workloads.<\/p>\n\n\n\n
Its configuration model is particularly useful when routing, caching, retries, guardrails, and fallbacks need to be composed into reusable policies. <\/p>\n\n\n\n
One important procurement consideration is its 2026 ownership change. Portkey is now part of Palo Alto Networks and is being integrated into Prisma AIRS, so enterprises evaluating it should assess the current Palo Alto product roadmap and packaging rather than relying on older standalone-Portkey comparisons.<\/p>\n\n\n\n
Choose LiteLLM when<\/h3>\n\n\n\n You want maximum control over the gateway and prefer an open-source, self-hosted architecture.<\/p>\n\n\n\n
It is particularly attractive for platform engineering teams that want one interface across many providers while retaining ownership of infrastructure, keys, logs, and deployment. <\/p>\n\n\n\n
The trade-off is operational responsibility. Free software does not mean free infrastructure.<\/p>\n\n\n\n
Choose Kong when<\/h3>\n\n\n\n Your organization already uses Kong or wants AI traffic managed as part of a broader API platform.<\/p>\n\n\n\n
Kong becomes particularly compelling when AI traffic, traditional APIs, MCP, A2A, authentication, governance, observability, and rate limiting need to sit inside the same infrastructure model.<\/p>\n\n\n\n
It is a stronger fit for an enterprise API platform than for a small team that only wants a lightweight LLM proxy.<\/p>\n\n\n\n
Choose Cloudflare when<\/h3>\n\n\n\n You want a managed gateway with edge infrastructure, dynamic routing, caching, rate limiting, spend controls, and Cloudflare-native security.<\/p>\n\n\n\n
Its current spend-limit implementation is especially useful for organizations that want budgets tied to users, teams, applications, models, or providers. It can also route traffic to a cheaper model after a budget threshold is reached.<\/p>\n\n\n\n
The trade-off is that Cloudflare is fundamentally a managed edge service, not the same self-hosting model as LiteLLM or Kong.<\/p>\n\n\n\n
What about TrueFoundry?<\/h3>\n\n\n\n TrueFoundry is worth evaluating when the requirement goes beyond a simple provider proxy into AI infrastructure, model serving, observability, and governance. It is better treated as an adjacent AI platform option rather than forcing it into a four-way comparison where the architectural models are already different.<\/p>\n\n\n\n
The main decision should come down to the control plane your team actually needs.<\/p>\n\n\n\nIf your priority is…<\/th> Strong starting point<\/th><\/tr><\/thead> Open-source and self-hosting<\/td> LiteLLM<\/td><\/tr> AI-native gateway workflows<\/td> Portkey<\/td><\/tr> Enterprise API infrastructure<\/td> Kong<\/td><\/tr> Managed edge gateway<\/td> Cloudflare<\/td><\/tr> Existing Kong environment<\/td> Kong<\/td><\/tr> Maximum infrastructure control<\/td> LiteLLM<\/td><\/tr> Cloudflare-native architecture<\/td> Cloudflare<\/td><\/tr> Broader Palo Alto AI security strategy<\/td> Portkey \/ Prisma AIRS<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nThe best AI gateway is not necessarily the one with the longest feature list.<\/p>\n\n\n\n
It is the one that gives your team the right control at the right point in the AI request path, without creating another operational system that nobody wants to maintain.<\/p>\n\n\n\n
FAQs<\/h2>\n\n\n\nDo I need an AI gateway?<\/h3>\n\n\n\n Not every AI application needs one. A gateway becomes useful when an organization works with multiple model providers, needs centralized routing or fallbacks, wants shared budgets and access controls, or needs one place to observe AI traffic across applications.<\/p>\n\n\n\n
A single application using one provider with little operational complexity may not gain enough from adding another infrastructure layer.<\/p>\n\n\n\n
Is LiteLLM production ready?<\/h3>\n\n\n\n LiteLLM can be used as a production gateway, including through its self-hosted open-source deployment. Its current Enterprise offering adds features such as SSO, SCIM, audit logs, secret management, multi-region control, and production support. <\/p>\n\n\n\n
Production readiness still depends on how the organization deploys, scales, monitors, secures, and upgrades it.<\/p>\n\n\n\n
Which supports cost limits per team?<\/h3>\n\n\n\n LiteLLM supports spend tracking and budgets by key, user, team, and organization. Portkey supports cost- and token-based budget limits, with provider-level budget limits currently restricted to Enterprise and select Pro customers. Kong provides AI cost and usage controls through its gateway plugins. Cloudflare can scope dollar-based spend limits using metadata such as user, team, or application. <\/p>\n\n\n\n
The important difference is how each gateway enforces the limit, not simply whether a budget feature exists.<\/p>\n\n\n\n
How much latency does an AI gateway add?<\/h3>\n\n\n\n There is no single number that applies to every gateway.<\/p>\n\n\n\n
The overhead depends on network location, connection reuse, routing logic, logging, guardrails, caching, retries, and deployment architecture. Vendor-published numbers should therefore be treated as reference points rather than an independent benchmark.<\/p>\n\n\n\n
For production workloads, measure P50, P95, P99, time to first token, total response time, and error rate against a direct-provider baseline.<\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
An AI gateway becomes useful when an application stops relying on a single model provider. Instead of connecting every application directly to OpenAI, Anthropic, Google, or another provider, the gateway becomes the control point between the application and the models. It can handle routing, fallbacks, authentication, budgets, observability, caching, and, increasingly, security controls. That sounds […]<\/p>\n","protected":false},"author":1,"featured_media":3952,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[123],"tags":[],"class_list":["post-3949","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-security-gateways"],"_links":{"self":[{"href":"https:\/\/www.tooljunction.io\/blog\/wp-json\/wp\/v2\/posts\/3949","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tooljunction.io\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tooljunction.io\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tooljunction.io\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tooljunction.io\/blog\/wp-json\/wp\/v2\/comments?post=3949"}],"version-history":[{"count":2,"href":"https:\/\/www.tooljunction.io\/blog\/wp-json\/wp\/v2\/posts\/3949\/revisions"}],"predecessor-version":[{"id":3954,"href":"https:\/\/www.tooljunction.io\/blog\/wp-json\/wp\/v2\/posts\/3949\/revisions\/3954"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tooljunction.io\/blog\/wp-json\/wp\/v2\/media\/3952"}],"wp:attachment":[{"href":"https:\/\/www.tooljunction.io\/blog\/wp-json\/wp\/v2\/media?parent=3949"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tooljunction.io\/blog\/wp-json\/wp\/v2\/categories?post=3949"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tooljunction.io\/blog\/wp-json\/wp\/v2\/tags?post=3949"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"\"](\"https:\/\/blog.tooljunction.io\/wp-content\/uploads\/2026\/08\/image-25.png\")
<\/figure>\n\n\n\n![\"\"](\"https:\/\/blog.tooljunction.io\/wp-content\/uploads\/2026\/08\/image-26.png\")
<\/figure>\n\n\n\n