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.

👇 [Download the Kong vs LiteLLM comparison guide ](https://assets.prd.mktg.konghq.com/files/2026/05/69fbd672-kong-vs-litellm-comparison.pdf)Download the Kong vs LiteLLM comparison guide

Both LiteLLM and Kong support multi-LLM routing. That is now table stakes for nearly all AI gateways. The more useful question is what happens once that routing layer becomes shared infrastructure and starts carrying real production traffic across multiple teams and workloads.

This is where performance starts to matter more than simple provider coverage. 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.

Because Kong is built on a compiled, high-throughput gateway runtime rather than a Python-based proxy layer, it can handle concurrent connections and high-volume routing with significantly less compute overhead.

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.

Both LiteLLM and Kong can apply rate limits, quotas, and caching, but the more meaningful difference is how cleanly those controls can be expressed once multiple teams, groups, and models are involved.

LiteLLM supports basic rate-limiting and budget-control use cases, but it has less of a clean answer when teams need per-user, per-group, and per-model controls to work together in a single policy model. In practice, as those rules start to overlap, it can become harder to determine which limits apply and how they interact.

[_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.

Both LiteLLM and Kong provide baseline AI security controls, including prompt guardrails, filtering, and PII-related protections.

Kong's AI PII Sanitizer enforces DLP at the gateway across 20+ PII categories and 12 languages, 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 Presidio plus a catalog of partner guardrails like Aporia, Lakera, Bedrock, and PANW Prisma AIRS, but behavior and audit detail vary by integration. This means LiteLLM teams have to reconcile those differences or accept inconsistent DLP across models and consumers. 

Even more differences start to show up once the gateway becomes shared infrastructure, including how the platform handles identity, access, and policy across production AI traffic.

Kong supports a broader enterprise auth surface, including OIDC, mTLS, WebSocket OIDC and mTLS at handshake, ACL enforcement, and multi-cloud IAM integrations across AWS, Azure, and GCP. LiteLLM is more centered on API key and bearer-token access. That distinction becomes notable for service accounts, non-human identities, and organizations that already need to fit AI traffic into an existing IdP or IAM model.

Finally, Kong 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.

Agent-to-agent governance starts to matter once AI systems move beyond application-to-model traffic and begin coordinating work between agents. At that point, the question is no longer just how requests reach an LLM. It is how agent-to-agent traffic is controlled, observed, and governed as part of the larger system. 

We expect A2A support to become a standard capability across AI gateway vendors over time. Kong moved early here as the first AI gateway to support the A2A protocol. With [_Kong Agent Gateway_](https://konghq.com/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 also provides A2A protocol support with logging, load balancing, streaming, agent permissions, and iteration budgets, but does not extend the same depth of governance as Kong: no ACL framework or OAuth-scope authorization for agent traffic, and no structured agentic-analytics layer tied to broader governance.

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. With OAuth-based access, OAuth 2.0 token exchange, and MCP registry integration, Kong makes MCP part of the same governed runtime as LLM and A2A traffic.

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 covers the basics, including MCP server registration, tool-level permissions, and access groups by key, team, and organization, but stops short of default-deny enforcement, gateway-level OAuth scope authorization, and OAuth 2.0 token exchange.

The result is that LiteLLM teams have to reimplement those controls in application code or accept over-permissioned agents in production. Meanwhile, 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.

Full AI data path governance does not stop at model traffic, tool access, or agent-to-agent communication. In production, there is a lot of overlap between traditional API management and what AI rollouts need in practice: self-service catalogs, access controls, service discovery, and runtime governance.

LiteLLM is primarily centered 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. That matters because AI systems do not run in isolation: they depend on APIs, event streams, and enterprise context that also need to be discovered, secured, and governed. Kong’s broader platform brings those systems into the same governance model, 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.

Once the gateway is shared across teams, self-service access becomes more than a key-minting workflow. Buyers need a model that can support developers and service accounts, fit into broader approval and access patterns, and scope usage cleanly across the platform.

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's role tiers cover per-team and per-key access, but not per-resource RBAC, region scoping, or a built-in developer portal with its own RBAC. 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.

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. The companies that win with AI will not just win because they have the best models. They will win because they have the best unit economics. Cost governance is now a top reason teams stand up an AI gateway.

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.

To establish a centralized AI cost control gateway pattern, teams must move away from reactive billing alerts. LiteLLM supports basic budget tracking and lightweight spend controls, which report on overages after the fact. 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.

The second side is treating every token generated not just as a cost, but as a billable asset. This is a notable distinction 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.

Organizations need to be able to meter usage across teams and products, understand who is consuming what, and support chargeback or monetization without relying on a patchwork of separate systems.

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. This makes it simple to set up billing and chargeback by token usage directly within the platform. Usage data also connects directly to platform-level governance, chargeback, and monetization, rather than being treated as a separate afterthought.

The result is that, with Kong, entitlements and pricing become real platform capabilities rather than custom engineering work. Product teams are able to ship new pricing tiers faster because plans and rate cards live in the catalog rather than in application code.

Entitlements are also enforced in real time: meaning when an AI agent exhausts its prepaid credit limit, or a team hits its monthly compute budget, Kong can stop or throttle access at the point of consumption rather than simply surface the overrun on next month’s invoice. This is a capability that most of the AI gateway category, including LiteLLM, do not provide. 

Feature breadth is only part of enterprise readiness. 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.

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.

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_](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.