Technology Comparison

Cequence AI Gateway vs.
LiteLLM: Two Different Problems, Two Different Layers

As enterprises move agentic AI into production, security teams are evaluating a growing landscape of AI infrastructure products. Cequence AI Gateway and LiteLLM are both described as “AI gateways,” but they are built to solve different problems for different stakeholders.

This document provides a clear-eyed comparison to help your security team understand where each technology fits, what it governs, and which one addresses the risks you are evaluating.

The Core Distinction

The simplest way to frame the difference:

	Cequence AI Gateway	LiteLLM
What it controls	What AI agents are allowed to do with your enterprise applications, data, and approved LLM providers — including which models they can call, under what spend limits, and with what data protections.	Which LLM processes a request and how much it costs.
Where it sits	Between AI agents and your backend services and approved LLM providers (APIs, SaaS apps, databases, legacy systems, and LLMs).	Between your applications and LLM API endpoints (OpenAI, Anthropic, Azure, Bedrock).
Boundary	Agent-to-application and agent-to-model	Application-to-model

Both boundaries matter in a production AI architecture. Enterprise data exposure, compliance risk, and unauthorized access can occur at either one — which is why Cequence AI Gateway governs both the agent-to-application boundary and the agent-to-model boundary under a single identity and policy system.

When to Use Each Technology

Use Cequence AI Gateway When:

Your AI agents need to interact with enterprise applications, data, and LLM providers in production, and you need governance, security, and compliance around those interactions.

Scenario 1: Enterprise Productivity Agents
Your organization is deploying AI agents (Copilot, Claude, custom agents) that need to access Salesforce, Jira, Snowflake, Confluence, and internal APIs on behalf of employees. You need to ensure each agent only accesses what the user is authorized for, with full audit trails.

Scenario 2: Agentic E-Commerce / Customer-Facing AI
You are building AI-powered shopping assistants or customer service agents that interact with commerce platforms and customer databases. You need to prevent business logic abuse, enforce rate limits per user per tool, and detect sensitive data exposure in real time.

Scenario 3: MCP Governance at Scale
Multiple teams are standing up MCP servers to give agents access to internal tools. You need a centralized, trusted MCP server registry so security can govern what is exposed, to whom, and under what policies. No shadow MCP servers. No agent-discovered endpoints.

Scenario 4: Regulated Industries (FinServ, Healthcare, Telecom)

You operate under SOC 2, HIPAA, PCI DSS, or GDPR and need compliance-mapped detection policies, user-attributed audit trails in OpenTelemetry format, and sensitive data scanning on every tool call.

Scenario 5: Zero Trust for Agentic AI

You need a two-layer trust boundary where the agent authenticates to the gateway (Layer 1) and the gateway authenticates to the backend using credentials the agent never sees (Layer 2).

Scenario 6: Governing Direct LLM Access
Your agents call LLM providers directly, not just tools and APIs. Every model call is brokered through the same identity and policy engine that governs MCP and API access: approved providers and models only, credentials the agent never holds, token and spend limits per persona, and inspection of every prompt and response for injection attempts, jailbreak patterns, and sensitive data.

Use LiteLLM When:

Your engineering team needs to manage LLM API routing, cost optimization, and provider redundancy across multiple model providers.

Scenario 1: Multi-Provider LLM Cost Management
Your teams are using OpenAI, Anthropic, and Azure OpenAI across projects. You want a single interface to route requests, track spend per team, and set budget limits per API key.

Scenario 2: Model Fallback and Load Balancing
You need automatic failover when your primary LLM provider hits rate limits or goes down. LiteLLM routes to a backup model transparently.

Scenario 3: Developer Experimentation and Prototyping
Your AI team is evaluating different models and wants to swap between them without rewriting application code. LiteLLM provides a unified OpenAI-compatible interface.

Scenario 4: LLM Usage Observability for Platform Teams
Your platform team needs to understand which teams are calling which models, how many tokens they consume, and where costs are concentrated.

When You Might Need Both

In a mature enterprise AI architecture, these technologies can serve different audiences. Cequence AI Gateway governs both boundaries under one policy system: which LLM providers and models an agent may call, at what spend and rate limits, with every prompt and response inspected — and, in the same session, what the agent is allowed to do with enterprise systems such as Salesforce, Snowflake, or any other internal API in your environment — and on behalf of which user. LiteLLM gives engineering teams a dedicated interface for routing requests across providers and tracking spend, independent of agent policy.

The security-critical decisions — data exposure, privilege escalation, compliance violations — span both boundaries. Cequence AI Gateway is built to govern both of them under a single identity and audit trail.

Detailed Capability Comparison

Capability	Cequence AI Gateway	LiteLLM
Primary function	Secure AI enablement. Governs agent interactions with enterprise apps, data, and LLM providers via MCP, API, LLM, and Skills Registries.	Open-source LLM proxy. Unified interface to 100+ LLM providers.
AI/Agentic discovery	AI Discovery surfaces every agent, LLM provider, and MCP server running across the enterprise, pulled from existing SIEM logs, whether or not it went through an official process.	None. Visibility limited to requests explicitly routed through LiteLLM's own proxy.
MCP support	Native. No-code MCP server creation from OpenAPI specs. Centralized trusted registry.	None.
API registry	Brokers API credentials so agents never hold them directly. Every call runs through the same policy-enforced identity as MCP and LLM traffic.	None. LiteLLM manages LLM provider keys only.
Skills registry	Curated, vetted, reusable agent capabilities that security and platform teams approve once and reuse across every agent.	None.
Identity and access	OAuth 2.1 with enterprise IdPs (Okta, Entra ID, Google). Two-layer trust boundary. Agent Personas bind each agent's tools, APIs, approved LLM models, and guardrails to a single job description.	Virtual API keys with team/org hierarchy. No IdP integration.
Agent governance	Agent Personas: least-privilege scoping at the intersection of user permissions and allowed tools, APIs, and LLM models.	None.
Security	Business logic abuse prevention and sensitive data scanning (PCI, SOC 2, HIPAA, GDPR) across every tool call, API request, and LLM prompt and response.	Third-party add-ons only (e.g., Pillar Security). No native security.
Prompt injection protection	Prompt Guard detects prompt injection, jailbreak attempts, and system-prompt extraction on every LLM prompt and response.	Third-party add-ons only (e.g., Pillar Security).
Sensitive data	Real-time payload inspection across tool calls, API requests, and LLM prompts and responses. Compliance-mapped detection, including base64-encoded payloads and evasion-pattern Unicode. Block, redact, or alert.	No native inspection. Requires third-party.
Audit and compliance	User-attributed trails in OpenTelemetry (SIEM-ready). Identity tracked across multi-step workflows, tool calls, API requests, and LLM calls.	Logging to S3/Datadog/OTel. Cost attribution per org/team/user.
Network security	IP CIDR filtering, geo-filtering, auth-bound IP pinning via JWT, fail-closed evaluation.	None.
Rate limiting	Per-user, per-tool, and per-model granularity. Prevents runaway loops, resource exhaustion, and uncontrolled LLM spend.	Per-key RPM/TPM only. No per-tool granularity.
App connectivity	200+ connectors. Auto-converts OpenAPI specs to MCP tools. API registry integration.	None. Connects to LLM providers only.
LLM routing / cost	Brokers every LLM call through the providers and models each Agent Persona is approved to use, with token and spend limits enforced at the point of request.	Multi-provider request routing, fallback, spend tracking, and budgets for teams managing model access directly.
Model fallback	Model selection is policy-driven per Agent Persona, scoped to the providers and models each job is approved to use.	Yes. Auto-failover across providers on rate limits or errors.
Deployment	Managed SaaS or self-hosted (Helm/K8s). Enterprise SLAs.	Self-hosted OSS (Docker/K8s). No vendor SLAs for OSS tier.

Security Considerations

Enterprise Identity and Credential Isolation

Cequence AI Gateway enforces a two-layer trust boundary. Layer 1 authenticates the agent and enforces all access policies. Layer 2 authenticates to the customer’s backend and to approved LLM providers using credentials stored in a secrets manager that agents and users never see. This separation ensures that even a compromised agent cannot reach backend services or model providers directly.

LiteLLM uses virtual API keys for access management. There is no enterprise IdP integration, no credential isolation between layers, and no mechanism to prevent a compromised key from accessing any model the key is authorized for.

Agent Governance and Least Privilege

Cequence Agent Personas constrain each agent’s scope to the intersection of what the user has been provisioned and what the persona explicitly allows. This is a reduction of privilege, not an expansion. The agent can never access more than the user is entitled to, and in practice accesses far less. This minimizes attack surface, reduces LLM context for more accurate tool selection, and enforces governance at the tool call level, the API call level, and the LLM call level.

LiteLLM has no concept of agent personas, tool-level access scoping, or least-privilege enforcement for AI agents.

Sensitive Data and Compliance

Cequence provides real-time content-layer inspection across MCP tool calls, API requests, and LLM prompts and responses to detect PII, credentials, financial data, and other sensitive content categories, including obfuscation attempts such as base64 encoding and evasion-pattern Unicode characters. Detection rules map to specific compliance frameworks so organizations can enforce data handling policies aligned to their regulatory obligations (PCI DSS, SOC 2, GDPR, HIPAA).

LiteLLM does not inspect tool call payloads. Content scanning is only available through third-party integrations.

Supply Chain and Operational Security

LiteLLM is an open-source project distributed via PyPI. In March 2026, LiteLLM experienced a supply chain compromise where malicious package versions deployed credential harvesting, Kubernetes lateral movement, and persistent backdoor payloads. Organizations self-hosting LiteLLM bear full responsibility for vulnerability patching, dependency management, secrets management, and incident response.

Cequence AI Gateway is a commercially supported, managed SaaS platform with enterprise SLAs, dedicated security operations, and continuous monitoring.

Shadow AI and Agentic Discovery

AI Discovery surfaces every agent, LLM provider, and MCP server running across the enterprise from existing SIEM logs, whether or not it went through an official process, giving security teams visibility that is not limited to whatever traffic happens to pass through a given proxy.

Summary

Question	Cequence AI Gateway	LiteLLM
Secure agent-to-application and agent-to-model interactions?	Yes, core function	No
Enterprise identity integration?	Yes (OAuth 2.1, Okta, Entra ID, Google)	No
Least-privilege for AI agents?	Yes (Agent Personas)	No
Scan tool call payloads for sensitive data?	Yes, in real time	No (third-party required)
Compliance-mapped audit trails?	Yes (OpenTelemetry, SIEM-ready)	Partial (cost/usage only)
Discover shadow agents, LLM usage, and MCP servers enterprise-wide?	Yes (AI Discovery)	No
Route LLM requests across providers?	Yes, across every provider and model your policy approves	Yes, core function
Optimize LLM costs and provide fallback?	Partial — token and spend limits enforced per Agent Persona	Yes
Primary buyer?	CISO, VP Security, Security Architect	Platform Engineering, DevOps

The right question is not “which gateway should we pick,” but who can answer for the behavior and actions of your agents. Cequence AI Gateway governs what AI agents are doing with your enterprise data and with your LLM providers alike: who authorized it, under what limits, and whether you can prove compliance. That is the problem it was built to solve, end to end.