Technology Comparison

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

Why This Comparison Matters

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 address fundamentally different layers of the enterprise AI stack and 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 and data. Which LLM processes a request and how much it costs.
Where it sits Between AI agents and your backend services (APIs, SaaS apps, databases, legacy systems). Between your applications and LLM API endpoints (OpenAI, Anthropic, Azure, Bedrock).
Boundary Agent-to-application Application-to-model
Both boundaries matter in a production AI architecture. However, the agent-to-application boundary is where enterprise data exposure, compliance risk, and unauthorized access occur. That is the boundary Cequence AI Gateway was built to secure.

When to Use Each Technology

Use Cequence AI Gateway When:

Your AI agents need to interact with enterprise applications and data 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).

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 coexist. LiteLLM manages the model layer: which LLM answers the question, at what cost, with what fallback logic. Cequence AI Gateway manages the application layer: once the agent has its answer, what is it allowed to do with your Salesforce instance, your Snowflake warehouse, your internal APIs, and on behalf of which user.
The security-critical decisions live at the application layer. That is where data exposure, privilege escalation, and compliance violations occur.

Detailed Capability Comparison

Capability Cequence AI Gateway LiteLLM
Primary function Secure AI enablement. Governs agent interactions with enterprise apps and data via MCP. Open-source LLM proxy. Unified interface to 100+ LLM providers.
MCP support Native. No-code MCP server creation from OpenAPI specs. Centralized trusted registry. None.
Identity and access OAuth 2.1 with enterprise IdPs (Okta, Entra ID, Google). Two-layer trust boundary. Agent Personas. 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. None.
Security Prompt injection protection, business logic abuse prevention, sensitive data scanning (PCI, SOC 2, HIPAA, GDPR). Third-party add-ons only (e.g., Pillar Security). No native security.
Sensitive data Real-time payload inspection (request + response). Compliance-mapped detection. 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. 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 granularity. Prevents runaway loops and resource exhaustion. Per-key RPM/TPM only. No per-tool granularity.
App connectivity 140+ connectors. Auto-converts OpenAPI specs to MCP tools. API registry integration. None. Connects to LLM providers only.
LLM routing / cost Not the primary function (different layer). Core strength. Multi-provider routing, fallback, spend tracking, budgets.
Model fallback Not applicable (different layer). 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 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 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 introduces Agent Personas, which 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.
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 of MCP tool call payloads to detect PII, credentials, financial data, and other sensitive content categories. 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.

Summary

Question Cequence AI Gateway LiteLLM
Secure agent-to-application 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)
Route LLM requests across providers? No (different layer) Yes, core function
Optimize LLM costs and provide fallback? No (different layer) Yes
Primary buyer? CISO, VP Security, Security Architect Platform Engineering, DevOps
The right question is not “which gateway should we pick.” These products address different risks at different layers. If your concern is which model processes a request and what it costs, LiteLLM is a reasonable tool for your platform team. If your concern is what AI agents are doing with your enterprise data, who authorized it, and whether you can prove compliance, that is the problem Cequence AI Gateway was built to solve.