For many buyers, this is where the evaluation begins: the part of the stack responsible for controlling, shaping, and observing AI traffic as it moves between applications and AI models. Once the baseline requirements are met, the question then shifts from simple feature coverage to how well the gateway holds up as usage grows, policies get more granular, and when multiple teams begin to rely on it as the central control layer.[](https://assets.prd.mktg.konghq.com/files/2026/05/69fbd672-kong-vs-litellm-comparison.pdf)

👇 [Download the Kong vs LiteLLM comparison chart](https://assets.prd.mktg.konghq.com/files/2026/05/6a023aea-kong-vs-litellm-1.pdf)Download the Kong vs LiteLLM comparison chart

**Why it matters: **Multi-LLM routing is now table stakes for nearly all AI gateways, so the real question is what happens once that routing layer becomes shared infrastructure carrying real production traffic. At that point, throughput and latency translate directly into compute cost. A gateway that handles less traffic per node forces you to run more nodes to absorb the same load.

In a [_public head-to-head performance benchmark_](https://konghq.com/blog/engineering/ai-gateway-benchmark-kong-ai-gateway-portkey-litellm)_public head-to-head performance benchmark_, Kong measured in with 859% higher throughput and 86% lower latency than LiteLLM in the tested environment. Even more notably, LiteLLM hit its own throughput ceiling before the upstream model layer was saturated. 

For lightweight or local-dev workflows, that may not show up right away. But for service-account traffic, agentic workflows, or broader enterprise rollout, it becomes a real overhead problem rather than just a benchmark number.

**Why it matters: **Once a gateway serves multiple teams, policies become a layered system of per-user, per-group, per-model, and per-route controls. When those can't be expressed cleanly in one place, teams could easily duplicate rules, leave coverage gaps, or ship orphan rules that no longer match the paths they're meant to protect. 

LiteLLM supports per-key, per-team, per-user, per-model, and per-customer rate limits and budgets, but those dimensions are configured as separate fields on separate entities rather than composed in a single policy. As rules begin to overlap, the resulting precedence (key vs. team vs. user vs. model) gets reasoned about across multiple config surfaces.

[_Kong’s AI rate-limiting plugin_](https://developer.konghq.com/plugins/ai-rate-limiting-advanced/)_Kong’s AI rate-limiting plugin_ can evaluate an ordered list of policies against attributes like consumer, consumer group, model, provider, header, and path. This allows teams to combine per-user, per-group, and per-model controls on the same route instead of spreading them across more complex route and plugin combinations.

Kong also separates virtual model names from provider model IDs, so accounting and limits stay tied to the model name that developers actually use. This matters when different models behind the same provider need separate budgets, limits, and policy controls.

Security and compliance for an AI gateway shows up in two primary places: how data is protected as it moves through the platform, and how access to the platform fits into the broader enterprise identity model. Both Kong and LiteLLM provide coverage in each area, but the consistency of that coverage at production scale is where they diverge.

**PII Sanitization and DLP**

**Why it matters: **When PII protection is split across multiple guardrail vendors, each integration brings its own behavior, audit detail, and failure modes. Security teams will have to reconcile inconsistent DLP across models and consumers, or accept gaps. In a regulated environment, a single platform-level enforcement point keeps the audit trail consistent.

Kong's AI PII Sanitizer enforces DLP at the gateway across 20+ PII categories on both prompts and responses, with synthetic replacement, optional restoration, and block-on-detect under one audit trail. This provides customers with unified platform-level control and makes it easier to mitigate any compliance gaps.

LiteLLM relies on a catalog of partner guardrails like Aporia, Lakera, Bedrock, and PANW Prisma AIRS, but behavior and audit detail vary by integration. Some of these run through LiteLLM's unified message translation layer, while others run only via direct hooks on the raw request, which means behavior and audit detail vary by integration. Teams will have to reconcile those differences themselves or accept inconsistent DLP coverage across models and consumers.

**Identity and Access Control**

**Why it matters: **Identity and access controls are where AI traffic either fits into the existing enterprise IAM model or becomes a parallel system that security teams have to govern separately. The latter is where compliance drift starts.

LiteLLM supports SSO, SAML, JWT-based authentication, and OAuth 2.0 flows for MCP, with several of these capabilities gated to its enterprise tier. Kong supports a broader gateway-layer auth surface, including OIDC, mTLS, WebSocket OIDC, and mTLS at handshake, ACL enforcement, and multi-cloud IAM integrations. For service accounts, non-human identities, and organizations that need to fit AI traffic into an existing IdP or IAM model with mTLS and IAM-native identities, that platform breadth can often show up as a difference maker.

Kong also keeps more of the safety and governance model in the gateway and platform layer itself, including NeMo Guardrails, ai-prompt-guard, and a custom guardrails framework for third-party APIs. LiteLLM does provide safety controls too, but it leans more on integrations, provider controls, and project or key-level guardrail assignment.

For buyers evaluating security in production, the more useful distinction is not whether a safety feature exists, but whether auth, guardrails, and policies can be enforced centrally across the core traffic patterns of the business.

Full data path governance means securing, governing, and observing more than just LLM traffic between applications and models. In production, AI traffic also includes MCP-based access to tools and data sources, along with agent-to-agent communication. Kong brings these traffic patterns together in one platform, creating a single governance layer across APIs, events, LLM calls, MCP tool access, and A2A communication.

**Why it matters: **Treating agents like users means treating their traffic like authenticated calls. Every A2A message is a privileged action that should pass through the same authorization, observability, and audit layer as the rest of the platform. Without ACLs and OAuth-scope enforcement, agent permissions become opaque, inconsistent, and effectively self-attested.

A2A support is becoming a standard capability across AI gateway vendors, and Kong moved early as the first AI gateway to support the protocol. With [_Kong Agent Gateway_](https://konghq.com/solutions/agent-gateway)_Kong Agent Gateway_, teams can govern LLM, MCP, and agent-to-agent traffic together instead of treating A2A as a separate gap in the stack.

LiteLLM provides A2A support with logging, agent-level cost tracking, per-key and per-team access, and OAuth 2.0 on the transport. However, those controls live inside LiteLLM's own virtual key model rather than a broader gateway policy layer. Kong's A2A governance is built on the same plugin ecosystem as its APIs and LLM traffic, so policies don't need to be rewritten per traffic type.

**Why it matters:** Tool access becomes a governance issue very quickly once MCP is part of the stack. In production, the question is not just whether agents can reach MCP servers. It is whether the platform can control which tools are exposed, how access is scoped, and how those decisions are enforced consistently.

LiteLLM can support MCP-related workflows, but Kong provides a broader governance model. Kong's MCP Tool ACLs apply default-deny rules at both tool discovery and invocation, with audit logging on every call and OAuth 2.0 scope-based authorization built in.

LiteLLM provides MCP server registration, tool-level permissions, and access groups by key, team, and organization, but those controls don't run at the gateway. Without default-deny posture and scope-based authorization at that layer, LiteLLM teams will have to reimplement those controls in application code or accept over-permissioned agents in production.

Kong constrains agents at the platform layer and keeps "Context Rot" out of both application code and production incidents. This allows teams to treat tool access as a first-class control surface.

**Why it matters:** AI systems don't run in isolation. They depend on APIs, event streams, and enterprise context that all need to be discovered, secured, and governed. If the AI gateway only governs model traffic, every other layer becomes a separate stack with its own access model, observability, and audit trail. That's two or more governance systems to keep in sync, and more surface area for things to drift.

LiteLLM is primarily focused on the AI gateway layer, including LLM traffic and MCP-related workflows. Kong goes further by bringing AI, API, Event, and Context Mesh management together in one platform, so teams do not have to manage AI traffic in one stack and the rest of the lifecycle in another.

Enterprise readiness comes down to whether a gateway can operate effectively inside the broader platform and operating model of the business. That means it has to work with existing auth and governance models, fit cleanly into different deployment topologies, and support broader team access without turning operations into a bottleneck.

**Why it matters: **Self-service exists so that platform teams don't become a bottleneck for every new app, agent, or service account. The moment developers have to file a ticket and wait, or platform teams have to write custom code to handle a new permission shape, the platform stops scaling and starts slowing the business down.

Kong combines [_Kong Identity_](https://konghq.com/blog/enterprise/api-management-and-identity)_Kong Identity_, the [_developer portal_](https://konghq.com/products/kong-konnect/features/developer-portal)_developer portal_, application registration, and scoped access controls to support self-service access in an enterprise-oriented fashion. RBAC is applied at per-resource granularity, with Custom Teams, Per-Entity permissions, region scoping, IdP group-to-team mapping, and a separate deny-by-default RBAC layer in the developer portal.

LiteLLM offers per-team, per-key, and per-user role tiers, plus object-level permissions for MCP servers, vector stores, and tools. However, it does not offer region scoping or a separate developer portal with its own independent RBAC layer. As self-service scales across business units and geographies, LiteLLM teams may have to pay for that gap with additional application code and admin queues.

**Why it matters:** Uber [_recently acknowledged_](https://finance.yahoo.com/sectors/technology/articles/ubers-anthropic-ai-push-hits-223109852.html)_recently acknowledged_ running through its entire 2026 AI coding budget before the end of April, and 84% of companies [_report_](https://www.mavvrik.ai/state-of-ai-cost-governance-report/)_report_ more than a 6% hit to gross margin from AI costs. Reactive billing alerts only tell you the damage after it's done. Real cost governance has to act at the gateway layer, before the expensive token is generated.

There are two sides to getting this right. The first is preventing runaway spend: output tokens cost up to ten times more than input tokens, and agentic workflows amplify the risk, as a single runaway agent can burn through an entire month's budget in an afternoon. The second is treating every token generated not just as a cost, but as a billable asset.

On the spend side, LiteLLM supports real-time budget enforcement, TPM and RPM rate limits, per-key, team, user, and model budgets with reset durations, and semantic caching. Kong takes this further by applying policy granularity directly to cost governance: token-aware rate limiting, prompt filtering, semantic caching, and per-consumer or per-agent entitlements that act at the gateway layer before a single expensive output token is generated.

On the monetization side, the distinction is broader between Kong and most of the AI gateway category, not just LiteLLM. Kong lets teams productize AI models, agents, and applications through a product catalog with rate cards, entitlements, credits, and subscription management. Organizations can charge per token, per model tier, per outcome, or per agent run, and make pricing changes in the product catalog instead of in code.

**Why it matters:** When metering, billing, and pricing tiers live in code instead of a product catalog, every new pricing change becomes an engineering project. When they're part of the platform, product teams can ship pricing changes the same way they ship anything else in the catalog, and finance gets a single source of truth for usage across API and AI traffic.

LiteLLM hooks into external billing workflows, which may work for teams that just need lightweight spend controls. Kong provides a more complete built-in answer. [_Metering and billing is part of the Kong platform_](https://konghq.com/products/kong-konnect/features/usage-based-metering-and-billing)_Metering and billing is part of the Kong platform_ and supports token and request metering across API and AI traffic, along with flexible dimensions for pricing and customer identification. Billing and chargeback by token usage can be set up directly within the platform, with usage data connecting to platform-level governance and monetization rather than being treated as a separate afterthought.

An area where Kong's metering layer goes further than most of the AI gateway category, including LiteLLM, is productization. Subscription tiers, entitlements, and rate cards live in the catalog rather than in application code. The same enforcement layer that throttles a key over its monthly compute budget also throttles an agent that exceeds its allocated entitlement, at the point of consumption rather than on next month's invoice.

**Why it matters:** Feature breadth is only part of enterprise readiness. Vendor SLAs determine how fast a critical CVE gets patched and what happens when the gateway goes down. Without published commitments, security and platform teams are left to negotiate those terms one contract at a time, or absorb the risk themselves.

Kong publishes formal vulnerability patching SLAs scaled to CVSS severity and backs Konnect with a 99.9% uptime SLA. Severity 1 incidents receive a 30-minute, 1-hour, or 2-hour initial response depending on support tier. Kong Gateway Enterprise also carries SLSA Level 3 (hardened build) attestation, with signed artifacts in the release pipeline.

LiteLLM does not publish comparable patching SLAs, uptime guarantees, or SLSA attestations in its public documentation. This leaves LiteLLM teams to negotiate those commitments individually or absorb the risk on their own, while Kong customers receive them as standard contractual posture.

**Why it matters:** Supply chain risk is where these commitments can matter most: the [_March 2026 LiteLLM supply chain incident_](https://securitylabs.datadoghq.com/articles/litellm-compromised-pypi-teampcp-supply-chain-campaign/)_March 2026 LiteLLM supply chain incident_ is a clear example. A routine package update can quietly ship malicious code into your environment, and the only thing standing between that payload and production is the vendor's release integrity practices.

Datadog Security Labs reported that two LiteLLM releases on PyPI, 1.82.7 and 1.82.8, were published with malicious code as part of a broader supply-chain campaign. This was not a fake package or typosquat. It was a compromise of the real package, and the payload was designed to harvest secrets and credentials, exfiltrate data, install persistence, and potentially spread in Kubernetes environments. [_Kong was not affected by this incident_](https://konghq.com/blog/news/kong-not-affected-by-the-pypi-distributed-litellm-supply-chain-attack)_Kong was not affected by this incident_. 

With 800+ employees, 900+ customers, $175 million in Series E funding, and six consecutive years as a leader in the [_Gartner Magic Quadrant_](https://konghq.com/resources/reports/gartner-magic-quadrant-full-lifecycle-api-management)_Gartner Magic Quadrant_, Kong offers the established track record enterprises look for when dealing with mission-critical AI workloads.

LiteLLM is a reasonable starting point for teams with baseline AI gateway needs: smaller-scale use cases centered on multi-LLM routing, budgets, and guardrails.

The more meaningful comparison starts once the gateway stops being used as only a lightweight connectivity layer and graduates to shared production infrastructure. That is where the evaluation shifts from baseline feature coverage to the broader enterprise requirements for operating an AI platform with confidence.

If your evaluation has already widened beyond a lightweight proxy, it is time to look at a platform designed for production AI traffic. This is where Kong stands apart. [_Contact us_](https://konghq.com/contact-sales/demo)_Contact us_ to schedule a demo today.

**What is an enterprise AI gateway?**

An enterprise AI gateway is a centralized infrastructure layer that manages, secures, and routes traffic between applications and AI models. Unlike lightweight proxies, an enterprise gateway is built for production workloads, offering advanced capabilities like multi-LLM routing, token-aware rate limiting, centralized PII masking, agent-to-agent (A2A) governance, and built-in cost controls.

**Why is Kong faster than LiteLLM in performance benchmarks?**

In head-to-head performance benchmarks, Kong achieved 859% higher throughput and 86% lower latency than LiteLLM. This is primarily due to architecture: Kong is built on a highly optimized, compiled core designed for massive concurrency and low-latency API management, whereas LiteLLM relies on a Python-based proxy layer, which introduces higher compute overhead under heavy production traffic.

**How do I stop runaway token spend in LLM apps?**

To stop runaway token spend, organizations should implement a centralized AI cost control gateway pattern. Instead of relying on reactive billing alerts that notify you after the budget is blown, an enterprise gateway like Kong uses real-time, token-aware rate limiting. It tracks input and output tokens at the gateway level and can automatically throttle or block requests the moment a user, group, or agent hits their predefined budget limit.

**Can an AI gateway enforce PII masking centrally?**

Yes. An enterprise AI gateway can enforce PII (Personally Identifiable Information) masking centrally so that individual developers don't have to build redaction into every application. Using Kong, security teams can define regex patterns or integrate external masking services at the gateway level, ensuring sensitive data is stripped from prompts before reaching external LLM providers.

**What happened during the March 2026 LiteLLM supply-chain incident?**

In March 2026, Datadog Security Labs discovered that two official LiteLLM releases on PyPI (versions 1.82.7 and 1.82.8) were compromised with malicious code. The payload was designed to harvest credentials, exfiltrate sensitive data, and install persistence in Kubernetes environments. Kong was unaffected by this incident, highlighting the importance of evaluating vendor security maturity and software supply chain defenses when selecting an AI gateway.