NeuralTrust vs LiteLLM: Best for Enterprise AI Security?

This comparison is written for enterprise security and platform leaders (CISOs, CIOs, Heads of AI, and platform engineering teams) evaluating an AI gateway as the control layer for LLM, MCP (Model Context Protocol), and agent traffic. It compares two products in that category: NeuralTrust TrustGate and LiteLLM (the LiteLLM AI Gateway / Proxy Server).

An AI gateway sits between AI agents and the services they call (LLM providers, MCP tool servers, and other agents) and becomes the single place where routing, policy, security enforcement, and observability attach. Both TrustGate and LiteLLM operate at this layer as open-source, self-hostable gateways with a commercial tier on top.

The two products come from different origins. TrustGate is built by a security company and treats runtime security enforcement as the gateway's organizing principle. LiteLLM began as a developer-focused unified interface to many LLM providers and grew into a full gateway, widely adopted for its breadth and simplicity. This article compares them on architecture, runtime security, posture management, governance, MCP and tool controls, observability, and deployment. Only the gateway/proxy component of each vendor is in scope.

Related article: The 11 Best AI Gateways for Enterprise AI Security in 2026

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Executive Summary (TL;DR)

• TrustGate is security-first, positioning as "the AI gateway built by a security company." Its differentiating design choice is a Security Engine that attaches to every Route, into which NeuralTrust's own runtime security product (TrustGuard) plugs.
• TrustGate emphasizes a single unified multi-protocol trace tree across LLM, MCP, and A2A traffic, with cost, latency, and security findings attributed inline at every level (Consumer, Route, Provider, span).
• NeuralTrust offers a companion posture product (TrustLens) that discovers and assesses AI running outside the gateway's path, a layer LiteLLM does not include.
• TrustGate ships a first-party runtime detection engine. LiteLLM's runtime security is a guardrails framework that wraps external moderation and detection services, several of which require an Enterprise license.
• LiteLLM is a mature, extremely widely adopted open-source gateway (MIT-licensed core, 50K+ GitHub stars, 1B+ requests served, 240M+ Docker pulls) with a unified OpenAI-compatible API to 100+ LLM providers. Its breadth, simplicity, and community are genuine strengths.
• LiteLLM has a real, documented MCP gateway with permission management by key, team, and organization, including tool-level permissions. This is stronger than it is sometimes given credit for.
• Key LiteLLM security and governance features are gated behind its Enterprise license (SSO/SAML beyond a small user count, audit logs with retention, the built-in guardrails suite, Prometheus metrics, SLAs).
• Both are open-source and self-hostable (on-prem, air-gapped, cloud, Kubernetes) and both cover LLM and MCP traffic.

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Comparison at a Glance

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Platform Overview

What is NeuralTrust TrustGate?

TrustGate is NeuralTrust's AI gateway. It sits between agents and the services they call (LLM providers, MCP servers, and other agents) and is designed to be the one place where routing, policy, security, and observability attach across all three kinds of AI traffic. Its core abstractions are Consumers, Providers, Routes, and Policies. Provider connections are configured once and reused; routing, failover, retries, and caching live in the gateway rather than in each application's code.

TrustGate's defining design choice is that a Security Engine attaches to every Route: when one is attached, every request is inspected and an allow/block/transform decision is executed before the request reaches its target. Security findings render as first-class spans in the same trace tree as operational telemetry. NeuralTrust positions this as the architectural consequence of being "the only AI gateway built by a security company."

Per NeuralTrust's own documentation, TrustGate is explicitly not itself the content-detection product (that is TrustGuard, the engine that attaches) and not a posture-management product (that is TrustLens, a separate product). The gateway core is Apache 2.0 and open source; the governance layer, retention, and security-finding depth are commercial.

What is LiteLLM?

LiteLLM is an open-source AI gateway that provides a single, unified, OpenAI-compatible interface to 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and more). It is available as a Python SDK for direct in-application use and as a Proxy Server (the AI Gateway) deployed as a centralized service for a team or organization. It is one of the most widely adopted gateways in the category, with the project reporting 50K+ GitHub stars, over 1,000 contributors, 240M+ Docker pulls, and 1B+ requests served.

Out of the box, the proxy provides virtual keys, spend and cost tracking, budgets and rate limits, load balancing, an admin dashboard, and a guardrails framework. LiteLLM also exposes an MCP Gateway (a fixed endpoint for MCP tools with access control by key and team) and A2A agent invocation, positioning itself as a single control plane for LLMs, MCP servers, and agents. The core is MIT-licensed and free to self-host.

A material nuance for enterprise evaluation: several production-grade capabilities require a LiteLLM Enterprise license, including SSO/SAML beyond a small free-user threshold, audit logs with retention policies, the full built-in guardrails suite, and Prometheus metrics. The OSS core is genuinely capable, but the enterprise governance surface is a paid tier.

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Architecture Comparison

Both are software gateways that front AI traffic, are open-source and self-hostable, and expose a centralized control point for routing, policy, and observability.

TrustGate organizes around Consumers, Providers, Routes, and Policies, and is designed so that a Security Engine attaches at the Route level as a foundational concern. It covers LLM, MCP, and A2A traffic in one model and renders security findings inline in the trace tree. NeuralTrust's stated deployment model is an open-source self-hosted core, with managed and hybrid (VPC, air-gapped) deployment at the enterprise tier.

LiteLLM offers two deployment shapes from one codebase: an in-process Python SDK and a standalone Proxy Server. The proxy is OpenAI-API compatible, so existing OpenAI SDK code works by changing the base URL, which is a major driver of its fast adoption. It is deployed via Docker, Helm, and published Terraform modules, and is designed as a central control plane for LLMs, MCP, and A2A under one API key, rate limiter, and usage dashboard. The trade-off is orientation: LiteLLM's architecture optimizes for provider breadth and developer convenience, with security added as a configurable guardrails layer rather than as the foundational design concern.

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Runtime AI Security

This is the sharpest difference, and it is a difference of model.

TrustGate places a Security Engine attachment at the center of its design: NeuralTrust's own runtime product, TrustGuard, plugs in and inspects every request inline, with the decision executed before the request reaches its target. NeuralTrust positions TrustGuard as delivering conversation-level and multi-turn analysis rather than isolated per-request filtering. Two honest caveats: the content-detection capability is TrustGuard (a separate, attachable product), not the bare gateway, and TrustGuard's specific detections should be verified against its own documentation.

LiteLLM implements runtime security as a guardrails framework. The open-source framework supports custom guardrails and Presidio-based PII masking, plus secret detection/redaction. However, several built-in guardrail integrations, including llmguard_moderations, llamaguard_moderations, openai_moderations, google_text_moderation, lakera_prompt_injection, and aporia_prompt_injection, require a LiteLLM Enterprise license. In other words, LiteLLM's security model is per-request checks that wrap external moderation and detection services, and the more capable integrations sit behind the paid tier. LiteLLM does not document a first-party, multi-turn behavioral detection engine of its own; the analysis is delegated to the guardrail providers you connect.

Bottom line: TrustGate offers a first-party engine from a security company, attached inline as the gateway's organizing principle, with multi-turn analysis positioned as core. LiteLLM offers a flexible guardrails framework that orchestrates third-party checks, with the strongest integrations gated behind Enterprise. Which fits depends on whether the buyer wants a single security-company stack or prefers to assemble and license external guardrail providers.

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AI Security Posture Management (AI-SPM)

Posture management means discovering and assessing AI agents, tools, and models across the estate, including those that do not route through the gateway.

Neither gateway does this on its own. For TrustGate, NeuralTrust's documentation is explicit that discovering and assessing AI outside the gateway's path is the job of TrustLens, a separate NeuralTrust product that consumes gateway signals. For LiteLLM, estate-wide AI discovery and posture assessment is not a documented feature; LiteLLM governs and reports on the traffic that flows through the proxy.

On a strict gateway-only basis this is a tie: neither gateway is a posture-management product. The practical difference is that NeuralTrust offers a dedicated companion posture product (TrustLens) that a customer can evaluate alongside the gateway, whereas LiteLLM has no equivalent companion layer for ungoverned AI across the estate.

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AI Governance

LiteLLM's governance surface is broad and well documented: virtual keys, per-key/team/org budgets and rate limits, projects for grouping keys, model allowlists, IP-based access controls, and key rotation. The important nuance for a security review is licensing: SSO/SAML beyond a small free-user threshold, audit logs with retention, and SCIM provisioning are Enterprise features. TrustGate similarly gates SSO/SAML, RBAC, and long-term retention behind its enterprise tier, so both products reserve the enterprise governance layer for paid customers.

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MCP & Tool Governance

Both products have a genuine MCP governance story, and LiteLLM's is more capable than it is sometimes described.

TrustGate governs MCP traffic that flows through it: per-Consumer tool access, tracing of every tool invocation (which tool, which arguments, which Consumer, which result, at what cost), and a unified audit trail. NeuralTrust's documentation is explicit that TrustGate is not an MCP connectivity platform and does not ship a library of pre-built MCP integrations; it governs the servers the customer registers.

LiteLLM exposes an MCP Gateway that acts as a centralized hub for MCP tools with a fixed endpoint and shared authentication. It provides permission management by key, team, and organization: when a client lists tools, LiteLLM returns only the tools that entity is permitted to access, and it supports tool-level permissions (for example, giving Engineering one subset of a server's tools and Sales another), access groups, per-parameter restrictions, and public-internet visibility controls. Permissions resolve through an inheritance-and-intersection model where the most restrictive wins.

Neither product ships a large managed catalog of ready-to-use MCP integrations as the gateway's core value; both govern the servers you register. If a big pre-built MCP catalog is a hard requirement, qualify it separately for both.

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Observability

Both provide solid observability, with different emphases and licensing.

TrustGate resolves a full agent workflow into a single trace tree (the model call, the MCP tool calls it triggers, and A2A delegations) with cost, latency, and security findings attributed at every level (Consumer, Route, Provider, span). NeuralTrust frames this unified multi-protocol trace as a differentiator, with security findings inline in the same view. Live observability is in the open-source core; long-term retention and historical analytics are commercial.

LiteLLM provides cost and spend tracking, an admin dashboard for usage, token burn, and latency, and pluggable callbacks that stream every request to external observability tools (Langfuse, OpenTelemetry, MLflow, Helicone, s3, Datadog, and others). This callback model integrates cleanly into an existing observability stack. A licensing nuance: Prometheus metrics and additional alerting are Enterprise features, and deep long-term analytics typically rely on the external tools you connect rather than a built-in store.

The practical difference is emphasis: TrustGate centers a unified multi-protocol trace with inline security findings; LiteLLM offers broad, integration-friendly observability that leans on the external tools and stack you already run.

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Deployment

LiteLLM's deployment story is a strength: self-hosted on any environment, air-gapped supported, with Docker, Helm, and Terraform paths, keeping data on the customer's own servers. TrustGate positions air-gapped and hybrid at its enterprise tier. Both keep the deployment inside the customer's infrastructure, which matters for data-residency-sensitive buyers.

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Detailed Feature Comparison

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Which Platform Should You Choose?

Choose NeuralTrust TrustGate if...

• Your driver is a security mandate and you want the runtime-security engine to be a first-party product from a security company, not a framework that wraps external, separately licensed guardrail services.
• You want multi-turn and conversation-level analysis positioned as core, and you will validate TrustGuard's detections against its documentation.
• You want security findings inline in the same trace tree the platform team already reads.
• You expect to also adopt a companion posture product (TrustLens) to cover AI outside the gateway's path.
• You want the strongest security and governance capabilities available without assembling and licensing multiple third-party guardrail providers.

Choose LiteLLM if...

• Your priority is the broadest, simplest open-source multi-provider gateway, with a drop-in OpenAI-compatible API across 100+ providers and Day-0 access to new models.
• You value a large, active community, extensive documentation, and a battle-tested, high-adoption codebase.
• You want a flexible guardrails framework and are comfortable connecting (and licensing) external moderation and detection services.
• You want broad, integration-friendly observability that plugs into your existing stack (Langfuse, OpenTelemetry, Datadog, and others).
• You need MCP governance with per key/team/org and tool-level permissions in an open-source gateway.
• Cost sensitivity favors an MIT-licensed core you can self-host, with the option to add Enterprise for governance and support.

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Final Verdict

Both are credible, open-source, self-hostable AI gateways, and on core routing, reliability, MCP governance, and deployment flexibility they are genuinely comparable. LiteLLM's advantages are breadth of provider coverage, developer simplicity, community scale, and integration-friendly observability. TrustGate's advantages are first-party inline security enforcement, unified multi-protocol tracing with security findings in the same view, and a companion posture product.

The decision usually turns on the security model and where enterprise capabilities live. TrustGate treats a first-party Security Engine attachment as the gateway's reason for existing and delivers security from a single security company. LiteLLM delivers a flexible guardrails framework that orchestrates external services, with the strongest guardrails and much of the enterprise governance surface (SSO beyond a small user count, audit-log retention, Prometheus) behind an Enterprise license.

A security-led buyer who wants a first-party enforcement substrate with multi-turn analysis and a dedicated posture companion will likely prefer TrustGate. A team optimizing for provider breadth, simplicity, community, and a low-cost MIT core, and comfortable assembling external guardrails, will likely prefer LiteLLM.

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Key Takeaways

• TrustGate's biggest differentiators are a first-party Security Engine (TrustGuard), unified multi-protocol tracing with inline security findings, and a companion posture product (TrustLens).
• LiteLLM's biggest strengths are very broad provider coverage (100+ LLMs, OpenAI-compatible), simplicity, an MIT-licensed core, and one of the largest communities in the category.
• LiteLLM's runtime security is a guardrails framework wrapping external services; several key integrations (LLM Guard, Llama Guard, Lakera, Aporia, OpenAI/Google moderation) require an Enterprise license.
• LiteLLM has a real MCP gateway with permission management by key, team, and organization, including tool-level permissions.
• Much of LiteLLM's enterprise governance surface (SSO/SAML beyond a small user count, audit logs with retention, SCIM, Prometheus) sits behind the Enterprise license.
• Neither gateway ships a large pre-built MCP catalog; both govern customer-registered servers.
• Both are self-hostable including air-gapped, keeping data inside the customer's infrastructure.
• The choice usually reduces to a first-party security-company stack (TrustGate) versus the broadest, simplest open-source multi-provider gateway (LiteLLM).

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Frequently Asked Questions

1. What is the difference between NeuralTrust TrustGate and LiteLLM?

TrustGate is a security-first AI gateway whose Security Engine (TrustGuard) attaches to every Route and which covers LLM, MCP, and A2A traffic with inline security findings. LiteLLM is a widely adopted, MIT-licensed open-source gateway offering a unified OpenAI-compatible API to 100+ providers, with security delivered as a guardrails framework that wraps external services.

2. Does TrustGate support on-prem or air-gapped deployment?

NeuralTrust positions managed and hybrid deployment, including VPC and air-gapped, at its enterprise tier, with an open-source self-hosted core.

3. Does TrustGate ship pre-built MCP servers?

No. NeuralTrust's documentation states TrustGate is not an MCP connectivity platform and does not ship a library of pre-built MCP integrations; it governs the servers the customer registers.

4. Which AI gateway is better for enterprise AI?

Neither is universally better. Security-led buyers wanting a first-party enforcement engine and multi-turn analysis tend toward TrustGate; teams prioritizing provider breadth, simplicity, community, and a low-cost open-source core tend toward LiteLLM.

5. Does either gateway do AI posture management (AI-SPM)?

Not as a gateway feature. NeuralTrust offers a separate posture product (TrustLens) that covers AI outside the gateway's path; LiteLLM has no equivalent companion layer.

6. Is LiteLLM really free and open source?

The LiteLLM core is MIT-licensed and free to self-host. However, several production capabilities, including the full built-in guardrails suite, SSO/SAML beyond a small free-user count, audit logs with retention, SCIM, and Prometheus metrics, require a commercial Enterprise license.

7. Does LiteLLM have an MCP gateway?

Yes. LiteLLM exposes an MCP Gateway with a fixed endpoint, shared authentication, and permission management by key, team, and organization, including tool-level permissions, access groups, and public-internet visibility controls.

8. How does LiteLLM handle runtime security?

Through a guardrails framework. The open-source framework supports custom guardrails, Presidio PII masking, and secret redaction, while stronger integrations such as LLM Guard, Llama Guard, Lakera prompt injection, Aporia, and OpenAI/Google moderation require an Enterprise license. It does not document a first-party multi-turn behavioral detection engine.

9. How many LLM providers does each gateway support?

LiteLLM advertises a unified interface to 100+ LLM providers using the OpenAI format. TrustGate covers the major providers an enterprise deploys and adds inline security enforcement and A2A governance.

10. Which has better observability?

Both are solid. LiteLLM offers cost tracking, an admin dashboard, and pluggable callbacks to external tools (Langfuse, OpenTelemetry, Datadog), with Prometheus metrics in Enterprise. TrustGate emphasizes a single unified multi-protocol trace tree with cost, latency, and security findings attributed inline.

11. Do both gateways support A2A (agent-to-agent) traffic?

Yes. TrustGate covers A2A alongside LLM and MCP, and LiteLLM supports A2A agent invocation through the proxy.

12. Which gateway is more cost-effective?

LiteLLM's MIT core is free to self-host, with Enterprise (reported around a few hundred to a few thousand USD per month depending on tier) adding governance and support. TrustGate offers an open-source core with a commercial enterprise tier. In both cases, budget for the operational and infrastructure cost of running the gateway, not just the license.

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About the Author

Alessandro Pignati is Lead AI Security Researcher at NeuralTrust, where he leads research on AI and agentic security, advancing techniques to evaluate and secure large language models and autonomous AI systems. He specializes in adversarial machine learning, AI red teaming, LLM security, and AI safety, contributing to the development of secure and trustworthy AI.

NeuralTrust is an AI agent security platform, recognized in the Gartner 2025 Market Guide for Guardian Agents. Headquartered in Barcelona with ISO 27001 certification.