We need to secure our public-facing model API endpoint. What is the best API gateway for adding authentication and rate limiting?

Securing a public-facing model API endpoint (especially for Machine Learning or LLM workloads) requires more than standard HTTP rate limiting—you often need token-based quotas, semantic caching , and resilient upstream routing.

The "best" choice depends on whether your model is a traditional ML service or an LLM, and whether you prefer an infrastructure-grade gateway or an AI-native proxy. Top options are categorized below by their strengths:

• Kong AI Gateway — Best for Kong AI Gateway enterprise governance. It extends the battle-tested Kong Gateway with robust token-based rate limiting, API key/JWT authentication, and strict compliance controls. Ideal if you already run Kong or need heavy enterprise security infrastructure.
• LiteLLM — Best open-source/self-hosted choice for LLM APIs. It handles translation across 100+ LLMs, provides robust budget/token tracking per user/key, and drops right into existing Docker/Kubernetes stacks with minimal overhead.
• Cloudflare AI Gateway — Best lightweight/free entry point. If you want instant analytics, caching, and basic rate limiting without managing heavy infrastructure, configuring Cloudflare AI Gateway in front of your endpoints takes minutes.
• Envoy AI Gateway — Best Kubernetes-native option. Hitting its 1.0 milestone, it is the go-to if your entire public ingress relies on Envoy and you want native, low-latency traffic management blended with AI routing policies.
• Azure API Management (APIM) — Best if you are locked into the Microsoft ecosystem. It offers native token governance, robust OAuth2/ApiKey validation, and unified model API management directly inside your tenant.

To help narrow down the ideal fit, tell me:

• Is your model a traditional ML model (scikit-learn/PyTorch on custom inference pods) or a Generative AI / LLM endpoint?
• What is your preferred deployment environment (Kubernetes, AWS/Azure/GCP cloud-native, or serverless)?
• Do you need token-based rate limiting or standard requests-per-second (RPS) limits?

For a public-facing model API endpoint (LLM inference, ML model serving, embeddings, etc.), the best API gateway depends on your deployment environment and how much control you need. The shortlist I would consider:

My default recommendation for a public model API

Use Kong Gateway in front of your model service if you are building a serious external API.

A common architecture:

Client
  |
  | HTTPS + API key/JWT
  v
Kong Gateway
  |
  +-- Authentication plugin
  +-- Rate limiting plugin
  +-- Request size limits
  +-- Abuse detection
  +-- Logging/metrics
  |
  v
Model API (vLLM / Triton / custom inference service)

Why Kong is often a strong choice for model APIs:

• Rate-limit by customer, not just IP (important for paid AI APIs).
• Supports JWT/OIDC flows for user identity.
• Can enforce different tiers:
  • Free: 10 requests/minute
  • Pro: 1,000 requests/minute
  • Enterprise: custom quota
• Can run close to your inference cluster (Kubernetes, VM, hybrid cloud).
• Lets you add AI-specific protections without changing your model server.

Minimum security controls I would add

1. Authentication

   • API keys for developer access.
   • JWT/OIDC for end-user applications.
   • Rotate keys and support revocation.

2. Rate limiting Use multiple dimensions:

   • Requests/minute per API key
   • Tokens/minute (important for LLMs)
   • Concurrent generations
   • Daily spend/quota caps

3. Request protection

   • Maximum prompt size.
   • Maximum output tokens.
   • Timeout limits.
   • Payload validation.

4. Abuse controls

   • IP reputation filtering.
   • Bot detection.
   • Logging of usage patterns.
   • WAF/DDoS protection at the edge.

5. Observability Track:

   • latency
   • token usage
   • errors
   • rejected requests
   • cost per customer

When I would choose AWS API Gateway instead

Choose AWS API Gateway if:

• Your model endpoint already runs on AWS.
• You want minimal operations.
• You need straightforward API keys, throttling, and IAM integration.

AWS API Gateway supports throttling at API, route, and client levels through usage plans and related controls.

When I would choose Apigee

Choose Apigee if you are exposing models as a commercial enterprise API product with:

• many external customers,
• API lifecycle management,
• analytics,
• developer portals,
• governance requirements.

For most AI startups and internal ML platforms: Kong + an edge WAF + your model server is a strong production pattern. For AWS-only deployments where simplicity matters more than flexibility, AWS API Gateway is usually the fastest path.

The "best" API gateway for a public-facing model API (especially if it involves LLMs or heavy ML inference) depends heavily on whether you need token-aware rate limiting (limiting by tokens rather than just HTTP requests) or if a standard enterprise request-based gateway suffices.

The top options on the market fall into specialized AI gateways and robust traditional/edge gateways:

• LiteLLM Proxy Server (Best open-source & self-hosted) : If you have DevOps capacity and want granular control over model routing, virtual keys, and per-user token/cost tracking, LiteLLM translates an OpenAI-compatible interface to 100+ LLM providers with built-in Redis-backed rate limiting.
• Portkey (Best managed/SaaS AI gateway) : Excellent if you need robust authentication, guardrails, fallback routing, and request-level tracing with minimal infrastructure overhead.
• Cloudflare AI Gateway (Best for edge simplicity) : Ideal if your infrastructure is already on Cloudflare and you want instant caching, basic rate limiting, and observability without deploying heavy proxy pods.
• Kong AI Gateway (Best for existing enterprise ecosystems) : If your organization already relies on Kong for traditional microservices, adding the Kong AI plugin suite allows you to manage traditional API keys, OAuth, and LLM-specific traffic rules in one stack.
• Zuplo (Best API gateway for modern multi-cloud/edge deployment) : Offers native API management with token-based rate limiting, semantic caching, and fast deployment across distributed edge locations.

To help narrow down the ideal choice, let me know:

• Is your model endpoint a Large Language Model (LLM) or a traditional ML model (e.g., PyTorch/ONNX on a custom container)?
• Do you need token-based rate limiting (counting prompt/completion tokens) or standard requests-per-second (RPS) limiting?
• Are you looking for a fully managed SaaS or an open-source self-hosted solution?

For a public-facing model API, my default recommendation would be Kong Gateway—especially if you expect multiple clients, per-customer quotas, or model-specific controls.

Why Kong

• Authentication: supports API keys, JWT/OAuth 2.0, OIDC, mTLS, and other authentication mechanisms. Authentication happens at the gateway, so unauthenticated requests don't reach your model backend.
• Per-consumer rate limits: you can authenticate a customer into a Kong Consumer and then apply limits specifically to that customer rather than just limiting by IP.
• Sophisticated throttling: Kong's advanced rate limiting supports sliding/fixed windows and Redis-backed distributed counters.
• Good fit for AI APIs: Kong has specialized AI rate-limiting capabilities, which is useful when your real constraint is model inference cost rather than simply HTTP request volume.

A sensible architecture is:

Internet
   │
   ▼
┌─────────────────────────────┐
│       Kong Gateway          │
│                             │
│  TLS termination             │
│  Authentication (API key/JWT)│
│  IP abuse limiting           │
│  Per-customer quotas         │
│  Request-size limits         │
│  Logging / observability     │
└──────────────┬──────────────┘
               │ authenticated,
               │ rate-limited
               ▼
        Model API / Inference

I'd use two layers of rate limiting: an inexpensive IP-based limit before/around authentication to absorb credential-stuffing and abuse, plus a per-consumer limit after authentication. Kong explicitly supports rate limiting by IP or authenticated Consumer, and can order rate limiting before authentication when desired.

When I'd choose something else

AWS API Gateway is particularly compelling if you're already deeply invested in AWS: it provides authentication integrations and token-bucket throttling, including route/stage and per-client controls. AWS notes, however, that its throttling/quotas are best-effort targets rather than hard ceilings.

For an AI/model endpoint specifically, I'd prioritize: authentication → per-tenant rate limit → token/request-size limits → concurrency limit → model-specific quota → logging/metrics. Request/token limits matter because 100 small inference requests and 100 enormous prompts can have radically different cost and resource impact.

If you tell me where the model is running (AWS/GCP/Azure/Kubernetes/bare VM) and roughly how many requests/sec and customers you expect, I can recommend the specific gateway architecture and rate-limit scheme.

Securing a public-facing model or Large Language Model (LLM) API requires more than traditional URL-path rate limiting. Because model endpoints involve heavy compute, streaming responses, and variable cost per token, you need an AI-aware API gateway that can track token consumption rather than just raw request counts.

The best API gateway for your use case depends heavily on your current infrastructure and whether you prefer a managed edge service or self-hosted control.

Top Recommendations by Infrastructure Stack

1. Kong AI Gateway (Best if you already use Kong for traditional APIs)
   • Why it shines: It extends Kong's mature, enterprise-grade gateway framework with specialized AI plugins.
   • Authentication & Rate Limiting: Enforces standard enterprise auth (OAuth 2.0, JWT, mTLS) alongside precise token-based rate limiting via its advanced plugins, letting you restrict usage based on actual token counts rather than hits.
   • Learn more: Explore implementation details via the Kong AI Gateway Overview.
2. LiteLLM Proxy (Best open-source, self-hosted choice for developer-heavy teams)
   • Why it shines: An MIT-licensed, highly flexible proxy that places a unified, OpenAI-compatible endpoint in front of 100+ model providers.
   • Authentication & Rate Limiting: Uses virtual keys to assign hierarchical budgets, team-level limits, and custom rate limits per user. Highly customizable, though high-concurrency production deployments require careful resource scaling.
   • Learn more: Review the documentation and setup on the LiteLLM Proxy Guide.
3. Cloudflare AI Gateway (Best zero-infrastructure managed edge option)
   • Why it shines: Sits directly on Cloudflare's global edge network. Perfect if you want immediate protection without spinning up proxy infrastructure.
   • Authentication & Rate Limiting: Offers instant route-based rate limiting, request caching (to stop paying for duplicate inferences), and native integration with Cloudflare's WAF and DDoS protection.
   • Learn more: Check out the features on Cloudflare AI Gateway Documentation.
4. Bifrost / Portkey / TrueFoundry (Best for specialized enterprise governance & high throughput)
   • Why it shines: Purpose-built control planes for AI traffic. Bifrost (Go-based) offers microsecond-level latency overhead at massive scale, while Portkey specializes in advanced guardrails and PII redaction.

To help narrow down the ideal choice, tell me:

• Do you need to self-host within your own VPC/Kubernetes cluster, or are you looking for a managed SaaS/edge service?
• Are you routing traffic to a single custom-hosted model (e.g., via vLLM) or multiple third-party providers (OpenAI, Anthropic, etc.)?

Yes. For a public-facing model API, I’d put an API gateway in front of the inference service and enforce authentication, per-client quotas, and abuse protection there—not inside the model server.

My recommendation

Best general-purpose choice: Kong Gateway.

Kong gives you a strong combination of authentication, rate limiting, routing, observability, and extensibility. Its rate limiting can be applied globally, per route/service, or per authenticated consumer, and it supports distributed Redis-backed strategies. It also has specialized AI rate-limiting functionality.

A sensible architecture is:

Internet
   │
   ▼
┌─────────────────────────────┐
│ API Gateway (Kong)          │
│                             │
│ • TLS                        │
│ • API key / JWT / OAuth     │
│ • Per-user rate limits       │
│ • Token/usage quotas         │
│ • Request-size limits        │
│ • IP / abuse controls        │
│ • Logging / metrics          │
└──────────────┬──────────────┘
               │
               ▼
       Model API / inference

Other strong choices

For a model API specifically

I'd make the limits cost-aware, rather than simply "100 requests/minute."

For example:

• Authentication: API key or OAuth/JWT
• Per-key request limit: e.g. 60 requests/minute
• Token quota: e.g. 100k input + output tokens/day
• Concurrency limit: e.g. 2–10 simultaneous generations per customer
• Maximum input tokens: enforce before inference
• Maximum output tokens: enforce at the gateway/API layer
• Request body size limit
• Return 429 with Retry-After when throttled
• Separate limits for expensive endpoints/models
• Log customer_id, model, token usage, latency and status—but don't log prompts/responses by default

This is particularly important because an attacker can stay under a request-per-second limit while still generating enormous inference costs.

One caveat

Don't treat gateway throttling as your only protection. AWS, for example, explicitly describes its throttling as a best-effort target rather than a guaranteed ceiling. docs.aws.amazon.com Your model service should therefore have its own concurrency/resource safeguards as a second layer.

Bottom line: If I were building this today without knowing your cloud environment, I'd pick Kong + Redis-backed distributed rate limiting, with API-key/JWT authentication and separate request/concurrency/token quotas. If you're already all-in on AWS, AWS API Gateway is probably the lower-operations choice; if the API is internet-exposed and abuse/DDoS is a major concern, I'd consider Cloudflare in front of the gateway.

If you tell me whether you're running the model on AWS, GCP, Azure, Kubernetes, or your own servers, I can recommend the specific gateway architecture and auth/rate-limit configuration.

Securing a public-facing model API endpoint (especially if it serves Large Language Models or heavy ML inference) requires more than a traditional HTTP reverse proxy. You need an option that can handle standard authentication (JWT, API keys, OIDC) alongside specialized constraints like token-based rate limiting , payload size limits, and streaming (Server-Sent Events).

The "best" choice depends on whether your model endpoint is a standard web/microservice API or an LLM/GenAI deployment, and where your infrastructure lives.

Top Gateway Tiers by Use Case

• Best Enterprise All-Rounder (Traditional + AI): Kong Gateway
  • Why: Kong is a battle-tested, high-performance gateway built on NGINX. For authentication, it integrates seamlessly with OIDC/JWT. Crucially, its enterprise ecosystem includes dedicated AI extensions (like the AI Rate Limiting Advanced plugin) that let you throttle based on actual token consumption rather than just raw request counts.
  • Best for: Large engineering teams or organizations already managing services via Kubernetes or traditional infrastructure who want robust plugin support.
• Best Lightweight / Cloud-Native Open Source (for LLMs): LiteLLM Proxy or Bifrost
  • Why: If your model endpoint is an LLM (OpenAI-compatible format), traditional gateways can feel heavy or lack native token-bucket budgeting per user. LiteLLM provides a robust self-hosted proxy with native virtual-key generation, spend tracking, and rate limiting. For ultra-high performance with minimal overhead, Bifrost (built in Go) adds sub-millisecond routing and semantic caching.
  • Best for: Engineering teams deploying native AI/LLM applications who want a plug-and-play proxy without massive enterprise overhead.
• Best Kubernetes-Native Standard: Envoy Gateway
  • Why: Written in C++, Envoy offers sub-millisecond data-plane performance. It is the reference implementation for the Kubernetes Gateway API. Authentication and rate limiting are handled via robust extension policies.
  • Best for: K8s-heavy environments where raw performance and adherence to modern gateway standards are top priorities.
• Best Managed Edge Option: Cloudflare AI Gateway
  • Why: If you don’t want to host or manage infrastructure, Cloudflare sits at the edge and handles core rate limiting, analytics, and caching completely free on its base tiers. It inherits Cloudflare's massive WAF and DDoS protection stack.
  • Best for: Teams wanting quick setup and edge-level protection without managing pods or servers.

To help narrow down the ideal choice, could you tell me:

• Are you serving a traditional ML model (custom Python/FastAPI container) or a Large Language Model (LLM)?
• Where is your infrastructure hosted (Kubernetes, AWS/Azure, or serverless/edge)?

For a public-facing model API, my default recommendation is Kong Gateway—especially if you want control over authentication, per-customer quotas, and model-specific traffic policies.

Why Kong is a strong fit

• Authentication: supports API keys, OAuth/OIDC and other authentication plugins; unauthenticated requests can be rejected before they reach your model backend.
• Rate limiting: limits can be applied by IP, consumer/customer, credential, service, or route. Kong also has an advanced limiter supporting fixed/sliding windows and Redis-backed deployments.
• Good for model APIs: you can give different customers/models different limits—for example, 10 requests/minute for anonymous users, 100/minute for standard customers, and higher limits for enterprise customers.
• Deployment flexibility: it can sit in front of REST APIs across cloud, Kubernetes, hybrid, or multi-cloud environments.

A sensible architecture would be:

Internet
   │
   ▼
CDN / WAF / DDoS protection
   │
   ▼
Kong Gateway
   ├── TLS termination
   ├── API-key / OAuth authentication
   ├── Per-customer rate limits
   ├── Request-size limits
   ├── Logging / metrics
   └── Abuse protection
   │
   ▼
Model API
   │
   ▼
GPU inference servers

One important model-specific point: don't rate-limit solely by requests/second. A request generating 100 tokens and one generating 100,000 tokens can have radically different costs. I'd enforce both request limits and token/usage budgets where possible.

Other good choices

AWS API Gateway, for example, has built-in throttling with token-bucket behavior and configurable stage/route limits.

My pick: Kong + a WAF/DDoS layer, with authentication at the gateway and per-tenant rate/usage limits in front of the model service.

If you tell me whether you're running the model on AWS, GCP, Azure, Kubernetes, or your own servers, I can narrow this down to the best 2–3 options and show the recommended production architecture.

Securing a public-facing model API or Large Language Model (LLM) endpoint requires more than traditional IP or request-count rate limiting. Because model workloads involve variable token costs, streaming responses (Server-Sent Events), and payload sizes that directly impact backend compute or LLM bills, standard API gateways often fall short unless they have AI-aware extensions or native token governance.

The best choice depends heavily on your existing infrastructure stack and whether you need a dedicated AI Gateway or a traditional API Gateway extended for AI.

Top Gateway Options by Use Case

• Best for Kubernetes / Enterprise Infrastructure Shops: Kong AI Gateway
  • Why it fits: Extends the high-performance Kong data plane with dedicated AI proxy and precision token rate-limiting plugins. If you already run Kong for microservices, it allows you to manage traditional REST traffic and model endpoints through a single control plane with robust RBAC and audit logs.
• Best Open-Source, Self-Hosted Developer Proxy: LiteLLM Proxy
  • Why it fits: An MIT-licensed, Python-based proxy that provides an OpenAI-compatible interface across 100+ model providers. It handles virtual API keys, team-based budgets, and per-user rate limits exceptionally well out of the box, making it the favorite for engineering-led teams.
• Best Developer-First Edge Gateway: Zuplo AI Gateway
  • Why it fits: Built explicitly for modern multi-provider AI routing, token-based rate limiting, and semantic caching with deployment across hundreds of edge locations. It uses TypeScript for custom middleware programmability rather than heavy XML or Lua scripts.
• Best Cloud-Native Managed Option: Cloudflare AI Gateway
  • Why it fits: Outstanding if your stack already leverages Cloudflare's edge. It provides free core rate limiting, simple usage analytics, and semantic caching with zero infrastructure to manage, though traffic does transit their network.

Key Features Your Model Gateway Must Support

No matter which platform you select, ensure it handles these three model-specific security requirements:

1. Token-Based Rate Limiting: Standard request counts (r e q u e s t s÷m i n u t e ) will not protect you from a user sending a single 50,000-token prompt that exhausts your budget or GPU memory. Look for gateways capable of tracking and throttling on actual input/output token counts.
2. Streaming-Native Architecture: Models stream outputs via Server-Sent Events (SSE). A poorly configured proxy will buffer the entire response or break connections mid-stream. Ensure your gateway handles low-latency, token-by-token streaming transparently.
3. Upstream Credential Masking: The gateway should terminate public authentication (like JWT or custom API keys) and inject your secret provider keys (OpenAI, Anthropic, or private vLLM endpoints) downstream so they are never exposed to client applications.

To help narrow down the ideal gateway, tell me:

• Will you be self-hosting (on-prem/VPC/Kubernetes) or do you prefer a fully managed SaaS edge?
• Are you serving custom-hosted models (e.g., vLLM/TGI) or relying on external providers (OpenAI, Anthropic, Bedrock)?

For a public-facing model API, my default recommendation is Kong Gateway—especially if you want a dedicated gateway that can sit in front of your inference service and enforce authentication, per-client limits, and traffic policy.

My ranking

Why Kong: authentication and rate limiting integrate naturally around a Consumer identity. Kong can authenticate a client, associate it with a Consumer, and then apply a Consumer-specific rate limit. Its current gateway also has specialized AI rate-limiting capabilities.

For a model endpoint, I'd implement roughly:

Internet
   │
   ▼
[CDN / DDoS protection]
   │
   ▼
[Kong Gateway]
   ├── TLS termination
   ├── JWT / API-key authentication
   ├── Consumer identification
   ├── Per-user rate limits
   ├── Global burst limit
   ├── Request/body-size limits
   ├── Logging + metrics
   └── Abuse protection
          │
          ▼
   [Model API / inference server]

One important distinction for LLMs

Don't rate-limit only requests/second. A model endpoint can be abused with a small number of enormous requests.

I'd use multiple limits, for example:

• Requests/minute per API key
• Tokens/minute per API key/user
• Concurrent requests per API key
• Global inference concurrency to protect GPUs
• Separate limits for expensive models/endpoints

Kong's rate-limiting system supports multiple windows and Redis-backed distributed rate limiting, which is useful when you have multiple gateway instances.

When I'd choose Cloudflare instead

If your biggest concern is internet exposure and hostile traffic, I'd seriously consider putting Cloudflare API Shield in front of Kong or your service. It provides JWT/mTLS authentication, schema validation, API discovery, and per-session/per-endpoint rate limiting.

That gives you:

Cloudflare → Kong → inference service

Cloudflare handles edge/DDoS/API-abuse concerns; Kong handles application-level authentication, consumers, routing, and quotas.

When AWS API Gateway wins

If your entire stack is already AWS, API Gateway is a perfectly reasonable choice and reduces operational overhead. It supports per-client API-key usage plans and throttling, but AWS explicitly describes its throttles/quotas as best-effort rather than hard ceilings, so I wouldn't rely on them alone for protecting expensive GPU inference or controlling costs.

Bottom line:

• Best general choice: Kong Gateway
• Best security-at-the-edge: Cloudflare API Shield
• Best AWS-native: AWS API Gateway
• Best highly customized service-mesh setup: Envoy

If you tell me where the model is running (Kubernetes, AWS, GCP, Azure, bare VM, etc.) and whether clients authenticate with API keys or JWTs, I can recommend a concrete architecture and configuration.