LiteLLM vs. Pomerium: What's the Difference and Which One Do You Need?

As AI adoption grows, teams are looking for reliable, scalable ways to manage and secure access to LLMs. LiteLLM and Pomerium are both powerful tools in this space that solve very different problems. When comparing LiteLLM vs. Pomerium, it’s important to understand where each one fits in your stack and how they can even work together.

This guide breaks down how LiteLLM and Pomerium differ, where they overlap, and what role they play in the modern AI gateway and security ecosystem.

What Is LiteLLM?

LiteLLM is an open-source LLM gateway that acts as a translation layer between your applications and over 100 model APIs. With a single OpenAI-compatible endpoint, LiteLLM lets developers call models from OpenAI, Anthropic, Mistral, Together.ai, Groq, and more.

Key Features:

• OpenAI-compatible API server

• Multi-model routing and failover

• Usage logging, retries, and cost tracking

• Python SDK for direct integration

LiteLLM simplifies multi-provider model integration for developers. It is widely adopted in applications that use LangChain, OpenAI SDKs, and retrieval-augmented generation (RAG) pipelines. 

What Is Pomerium?

Pomerium is an identity-aware access proxy that helps teams secure access to web services, including LLM gateways like LiteLLM. It authenticates users and enforces detailed policies based on identity, device status, time, and other contextual signals.

Key Features:

• Works with any HTTP-based service, including LLM gateways

• Integrates with identity providers (e.g. Okta, Microsoft Entra ID)

• Dynamic, context-aware access policies

• Access logging with full context metadata

Pomerium does not route LLM traffic or abstract model APIs. Instead, it provides a secure access layer to control how users interact with tools like LiteLLM. This allows organizations to meet enterprise compliance needs such as SOC 2 and HIPAA by logging every LLM call with full identity metadata.

LiteLLM vs. Pomerium: Feature Comparison

How They Work Together

LiteLLM and Pomerium address different layers of the stack but complement each other effectively:

• LiteLLM simplifies development: It gives developers a single OpenAI-compatible API for integrating multiple LLM providers without refactoring code.

• Pomerium secures every interaction: It ensures that requests between developers, LLMs, and both internal and external data sources—including MCP servers—are governed by identity-aware policies and detailed audit logging.

Using both together allows teams to:

• Securing LiteLLM endpoints behind Pomerium without exposing public endpoints

• Applying dynamic access policies (role, time, device) to LiteLLM requests

• Logging requests with full identity and session context

• Automatically rotating tokens and applying session-based credentials

Together, LiteLLM accelerates integration, while Pomerium makes sure those integrations remain secure and compliant. LiteLLM and Pomerium help teams balance developer agility with enterprise-grade security and compliance.

Security Considerations

A common vulnerability in AI infrastructure is the lack of robust access control, which can lead to publicly exposed endpoints, hardcoded API keys, and overlooked security practices that have long been standard in API development. LLMs often process sensitive data or trigger actions, making authorization a key concern.

LiteLLM supports API key-based access and offers advanced controls through its enterprise features. However, many teams using the open-source version end up placing LiteLLM behind their own reverse proxy to implement authentication and TLS.

Pomerium provides these features natively:

• Authentication via OIDC/Connect

• Agent-level RBAC and short-lived credentials

• Structured audit logs easily ingested by SIEM and observability tools

• Dynamic policy enforcement based on IP, device, identity, time, or third party data

Pomerium also provides the platform stability, support, and documentation that teams need for production.

Scenarios where Pomerium adds value:

• Securing interactions between models, LLMs, and internal data based on department or user group

• Blocking access from unmanaged devices or outside work hours

• Providing rich audit trails and compliance-ready logging

These kinds of policies are difficult or impossible to enforce in LiteLLM alone.

Final Thoughts: LiteLLM, Pomerium, or Both?

Use LiteLLM when you want to simplify the integration of multiple LLM providers through a single API surface.

Use Pomerium when you want a production-ready solution to secure, monitor, and control access to your AI infrastructure.

While LiteLLM focuses on model abstraction and routing, Pomerium offers many of the same core features as LiteLLM such as acting as a model access gateway and managing connections to upstream Model Context Protocol (MCP) servers. Pomerium further provides identity-aware access policies, detailed audit logging, and flexible authorization controls that support secure agentic workflows and enterprise compliance.

Learn more about securing LLM infrastructure:

• Why Traditional Access Controls Fail in LLM Deployments

• Your Employees Are Already Dumping Company Data to LLMs (Here’s What To Do About It)