Best Secure LiteLLM Alternatives for Self-Hosted Routing

Updated 5 November 2026 · Standard formula, shown below

For most teams, LiteLLM’s strongest secure alternative is Portkey Gateway when a maintained, policy-oriented gateway matters more than keeping every component open source. For a fully self-hosted open-source starting point, compare Kong AI Gateway and Envoy AI Gateway; the best fit depends on whether you need a ready-made model interface or want to build routing into infrastructure you already operate.

What to Look For

“Secure” is not a product checkbox. Confirm where prompts, responses, credentials, and logs travel; whether telemetry can leave your network; and whether the gateway supports your identity provider, audit needs, and retention policy. A self-hosted deployment can still send data to an external model provider, so distinguish control-plane hosting from inference and data-plane traffic.

Deployment and data flow: Check what can run on your own machines or cluster, what requires a vendor service, and which external providers receive requests.
Credential handling: Prefer a design that supports secret-manager integration, scoped credentials, rotation, and access controls. Avoid putting long-lived provider keys in source code or client applications.
Access and audit: Look for authentication, role-based access, request-level policies, and logs that help investigations without retaining sensitive content unnecessarily.
Routing and resilience: Verify provider and model selection, fallbacks, timeouts, rate limits, and behavior during upstream failures. Test these rather than assuming a feature works the way your application expects.
Operational fit: Consider Kubernetes or gateway expertise, upgrade cadence, observability, licensing, and the effort to maintain custom integrations.

Portkey Gateway

Strengths: Portkey provides an AI gateway focused on routing, provider connections, access controls, and request observability. Its open-source gateway can be self-hosted, while the wider Portkey platform offers managed capabilities. This makes it a practical option for teams that want an AI-specific interface without assembling every feature from lower-level proxy components.

Trade-offs: Verify which features are available in the self-hosted gateway versus commercial or hosted offerings. “Self-hosted” does not automatically mean every control-plane function, analytics feature, or provider integration is local. Review telemetry settings and data flows before sending confidential traffic.

Best for: Teams seeking a purpose-built gateway and willing to assess a mix of open-source and commercial features. Confirm current licensing and deployment boundaries against your requirements.

Kong AI Gateway

Strengths: Kong extends the Kong Gateway ecosystem with AI-focused capabilities, bringing model traffic into a familiar API gateway environment. Teams can apply established gateway patterns such as authentication, traffic controls, and operational monitoring alongside AI-specific routing features. Kong Gateway has an open-source edition; the exact AI capabilities and packaging can vary by edition.

Trade-offs: It is most compelling when Kong is already part of your infrastructure. New users may face more setup and configuration than with a narrower AI proxy, and should check whether required plugins or advanced governance functions require a commercial edition.

Best for: Platform teams already operating Kong, or organizations that want AI endpoints managed alongside conventional APIs.

Envoy AI Gateway

Strengths: Envoy AI Gateway is an open-source project built around Envoy and Kubernetes-oriented gateway infrastructure. It suits organizations that want to manage AI traffic through cloud-native networking patterns and retain control over deployment and configuration.

Trade-offs: It is a newer, more infrastructure-oriented choice than a turnkey model proxy. Confirm that its current provider support, policy features, and project maturity meet your needs; expect to supply operational expertise and integrate surrounding identity, secrets, and observability systems.

Best for: Kubernetes and Envoy teams comfortable operating gateway infrastructure and preferring an open-source foundation.

Hugging Face Inference Endpoints and TGI

Strengths: For teams primarily serving models they control, Hugging Face’s Inference Endpoints can deploy dedicated inference infrastructure, while Text Generation Inference (TGI) is an open-source serving option. This can reduce dependence on a general-purpose multi-provider proxy when the main requirement is serving selected models.

Trade-offs: These are not direct, feature-for-feature replacements for LiteLLM’s broad provider-routing layer. Endpoint hosting may be managed rather than entirely inside your own environment; self-managed TGI means you operate the serving stack. Check model support, hardware, networking, and current project maintenance before committing.

Best for: Teams prioritizing deployment of particular open models over unified routing across many third-party APIs.

Build on NGINX or HAProxy

Strengths: A conventional reverse proxy can provide a self-managed entry point, TLS termination, network restrictions, and basic traffic distribution. NGINX and HAProxy are mature options when your needs are limited and your team already operates them.

Trade-offs: Neither should be treated as a drop-in AI gateway. Model-aware routing, provider-specific request transformations, token-aware limits, and reliable fallbacks may require custom code or additional services. That custom layer becomes your responsibility to test, secure, and maintain.

Best for: Organizations with simple routing requirements, existing proxy expertise, and the engineering capacity to own any missing AI-specific behavior.

How to Choose

Start with the deployment boundary. If prompts must remain within a private network, map the complete path from client to gateway to model provider. A local gateway does not make an external inference API private. For strict isolation, evaluate models and inference servers that can run in the approved environment, and verify outbound network access is actually restricted.

Match the tool to your use case. For an AI-specific gateway with a relatively direct setup, evaluate Portkey. If you already operate Kong, assess Kong AI Gateway and its edition requirements. If your platform is Kubernetes- and Envoy-centric, consider Envoy AI Gateway. If you mainly serve a few self-hosted models, inference serving may matter more than a multi-provider router. A general reverse proxy is reasonable only when its limitations are acceptable.

Set a realistic budget. Include infrastructure, support or licensing, engineering time, upgrades, monitoring, and the cost of running inference. Open-source software has no license fee in some configurations, but it is not cost-free to operate. Hosted management can reduce maintenance while adding a provider and possibly changing where metadata is processed.

Run a small security and failure test. Use non-sensitive requests to check authentication, secret rotation, logging, rate limits, provider timeouts, fallback behavior, and whether errors expose credentials or prompt content. Record which features are available in the exact edition and version you intend to deploy. Keep provider credentials server-side and restrict who can change routing policies.

Frequently Asked Questions

Is there a one-to-one replacement for LiteLLM?
No. Some options focus on AI gateway policies, some on general API traffic, and others on model serving. Compare the provider integrations and features you actually use.

Does self-hosted mean prompts never leave my network?
No. Requests may still go to an external model provider. Check the full request path, telemetry, and logging configuration; use private inference if data must stay within your environment.

Which option is easiest to evaluate first?
Portkey is a sensible first evaluation for an AI-specific gateway. Teams already using Kong or Envoy may find their existing infrastructure more practical. Ease depends on your deployment and governance needs.

Can NGINX or HAProxy replace an AI gateway?
They can proxy traffic and handle common network functions, but may not provide model-aware routing or provider-specific features without custom development.

What should I test before switching?
Test the models and request formats you use, authentication, secret rotation, retries, timeouts, fallback behavior, logs, and policy enforcement. Check behavior during upstream outages, not just successful requests.

Bottom Line

Choose Portkey Gateway as the first alternative to evaluate if you want a purpose-built AI gateway and can verify that its self-hosted capabilities meet your security and feature requirements. Choose Kong AI Gateway when Kong is already your API platform, or Envoy AI Gateway when your team wants a cloud-native, open-source foundation and can operate it. For self-hosted model inference rather than broad provider routing, assess Hugging Face serving options. Whichever you choose, validate data flows, edition boundaries, and failure behavior before moving production traffic.

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