Gartner Market Guide: Top 8 AI Gateways

TL;DR

AI gateways are the control plane between apps and LLMs, handling routing, cost governance, security, and observability. The guide explains why teams need one now and profiles eight options, from NeuralTrust for security and governance to LiteLLM and Apache APISIX for open-source flexibility, Envoy and Traefik for cloud-native performance, Portkey for compliance and guardrails, Lasso for runtime visibility, and Helicone for low-latency prototypes. Selection advice maps needs to strengths across security, performance, openness, and ecosystem fit.

Many organizations adopting large language models quickly discover a painful truth: building an AI demo is easy, but running one in production is not. Costs rise without warning, data passes through third-party APIs without visibility, and when model providers face outages, entire applications grind to a halt.

This operational fragility has created demand for a new category of infrastructure known as the AI gateway. Acting as the control plane for enterprise AI systems, the gateway manages everything between your applications and large language models, from routing and authentication to cost optimization and runtime security.

Analysts now see this layer as essential to scaling AI safely. Gartner has just published a Market Guide for AI Gateways. Gartner members can access the full report here.

What is an AI Gateway

An AI gateway is the central orchestration layer between applications and large language models. It manages how requests flow, which models they reach, and how results return, all while enforcing the policies, controls, and visibility that enterprises need to operate safely at scale.

Unlike traditional API gateways, which route standard web traffic, an AI gateway understands the unique behavior of LLM workloads. It interprets token-based pricing, model context windows, and provider-specific rate limits. It also tracks how models are used, records their outputs, and applies compliance rules to prevent data misuse.

Modern AI gateways include several key capabilities:

• Model routing: Directing each request to the best model based on cost, latency, or capability.
  
  

• Cost governance: Monitoring token consumption, setting limits, and enforcing budgets in real time.
  
  

• Security and compliance: Filtering sensitive data, applying role-based access control, and ensuring audit readiness.
  
  

• Observability: Capturing detailed logs and metrics for debugging, optimization, and regulatory reporting.

In short, the AI gateway acts as the control plane for intelligent systems, providing the structure, safety, and efficiency that transform AI from experimentation into reliable enterprise infrastructure.

Why AI Teams Need a Gateway Now

Running AI applications in production exposes problems that most teams do not anticipate during early experimentation. Token costs spike without warning, models fail under load, and data security concerns slow deployments. These issues are not exceptions. They are the natural outcome of scaling complex model interactions without a control layer.

1. Cost volatility

Large language models are priced per token, which makes usage unpredictable. A single unoptimized query can consume ten times the expected tokens. Without budget enforcement, a small error or test loop can create thousands of dollars in charges overnight. AI gateways solve this by providing real-time cost attribution and automated limits across users, models, and teams.

2. Vendor lock-in

Many organizations start with one provider, only to realize later that they need alternatives for performance or compliance reasons. Hard-coded integrations make switching costly. Gateways abstract provider APIs so teams can route traffic dynamically between OpenAI, Anthropic, Google, or local models without rewriting code.

3. Security and compliance

Enterprise data frequently travels through third-party APIs. Without control, sensitive information such as financial records or medical text can be exposed. AI gateways introduce policy enforcement, redaction, and encryption before data leaves the organization. They also maintain audit logs that satisfy frameworks like NIST AI RMF and SOC 2.

4. Observability and reliability

LLM-based systems often fail in ways that traditional monitoring tools cannot detect. Models may appear functional but return incorrect or inconsistent outputs. AI gateways add structured logging, latency tracking, and error analysis, giving teams the visibility they need to identify root causes quickly.

For any enterprise adopting AI at scale, the gateway has become the foundation of operational maturity. It enables teams to deliver performance, control costs, and maintain trust as model complexity and traffic volume continue to grow.

Top 8 AI Gateways in 2025

The AI gateway market is consolidating around solutions that balance scalability, observability, and policy control. The following eight platforms represent the most capable options for organizations bringing AI into production. Each serves a distinct purpose, from developer-oriented frameworks to enterprise-grade runtime security.

1. NeuralTrust AI Gateway

Purpose-built for AI Security. NeuralTrust redefines the AI gateway category by focusing on protection, visibility, and governance rather than simple routing. It integrates behavioral threat detection, prompt and data masking, and runtime policy enforcement to secure every request between applications and large language models.

The platform aligns with leading security frameworks such as NIST AI RMF, OWASP GenAI, and ISO/IEC 42001, ensuring compliance across regulated industries. NeuralTrust’s analytics layer also provides evaluation metrics that quantify trust and performance across AI workflows.

Best for: Enterprises that need measurable, policy-driven security for LLMs and multi-agent systems.

2. LiteLLM

LiteLLM is an open-source AI gateway that routes requests to over 100 models through a single OpenAI-compatible API. It simplifies switching between providers such as Anthropic, Google, and Azure without vendor lock-in. The system includes basic cost tracking, retry logic, and load balancing with YAML-based configuration for full transparency.

While it lacks enterprise-grade compliance or runtime controls, LiteLLM is valued for its speed of deployment and flexibility.

Best for: Engineering teams that prefer open-source control and rapid experimentation across multiple LLM providers.

3. Apache Software Foundation (Apache APISIX)

Apache APISIX is a high-performance, open-source gateway now evolving to support AI workloads. Built on NGINX and Lua, it provides advanced routing, authentication, and observability features. Community plugins enable token metering, caching, and rate limiting for LLM traffic.

Its extensibility allows organizations to create custom modules for AI use cases, including prompt tracking and latency analytics. APISIX benefits from a large open-source community and a proven architecture trusted by enterprises worldwide.

Best for: Teams seeking a flexible, open-core gateway that can extend from traditional APIs to AI-specific operations.

4. Lasso

Lasso positions itself at the intersection of AI performance and security. The gateway offers context monitoring, prompt inspection, and data flow visibility tailored for generative AI systems.

The platform focuses on preventing data leakage and ensuring runtime observability across AI pipelines. Its lightweight design allows integration with cloud or on-prem deployments.

Best for: Organizations prioritizing runtime visibility and protection for LLM and AI environments.

5. Envoy

Developed by the Cloud Native Computing Foundation, Envoy is one of the most established open-source gateways and has recently expanded its support for AI inference traffic. It offers extensible filters, gRPC streaming, and observability hooks that make it well-suited for managing latency-sensitive model requests.

Envoy’s modular design allows integration with AI frameworks through extensions, while built-in metrics support performance tuning and auditing.

Best for: Platform teams that need a cloud-native gateway with reliability and ecosystem maturity.

6. Traefik Labs

Traefik is known for simplifying traffic management across distributed systems. The company’s latest updates extend support for AI and LLM routing with features such as token-aware load balancing, role-based routing, and service mesh integration.

Its dashboard and automation tools allow teams to configure AI traffic policies without writing extensive code. Traefik’s hybrid deployment options make it suitable for multi-cloud AI workloads.

Best for: Mid-size organizations that need automation, simplicity, and hybrid routing capabilities.

7. Portkey

Portkey combines gateway functionality with observability and governance. It provides unified model access, prompt management, and automated guardrails for AI safety. Its compliance certifications include SOC 2, ISO 27001, and HIPAA, making it viable for regulated industries.

The platform’s analytics dashboard tracks token usage, latency, and user behavior across models, while real-time monitoring helps prevent runaway costs.

Best for: Enterprises seeking visibility, compliance, and advanced guardrail enforcement alongside gateway operations.

8. Helicone

Helicone is a lightweight AI gateway designed for speed and developer ease of use. Written in Rust, it processes requests with minimal latency while providing caching, retries, and simple analytics. Integration with OpenAI-compatible SDKs makes it quick to adopt, especially for smaller projects.

Although it lacks advanced policy enforcement or enterprise governance features, Helicone remains one of the fastest open-source options for production-ready AI prototypes.

Best for: Startups and developers building fast, low-latency AI experiences without complex infrastructure.

Best Purpose-Built AI Gateways on the market

How to Choose the Right AI Gateway

Selecting the right AI gateway depends on your organization’s infrastructure maturity, risk tolerance, and performance priorities. The best approach is to match platform strengths to operational needs rather than chasing feature lists.

1. Prioritize security and governance

If data protection and compliance are central to your operations, select gateways that integrate with established frameworks. NeuralTrust and Portkey provide the strongest built-in controls for runtime protection, audit trails, and policy enforcement. Lasso is also a strong contender for observability-driven governance in regulated environments.

2. Optimize for performance and scalability

When latency and throughput define success, focus on lightweight or edge-optimized gateways. Helicone and Traefik Labs deliver minimal processing overhead, making them ideal for high-traffic or user-facing AI applications. Envoy also fits this category due to its cloud-native performance profile and reliability in distributed architectures.

3. Choose open-source for flexibility

Organizations that need customization and control often favor open-source ecosystems. LiteLLM and Apache APISIX allow teams to extend functionality through plugins, modify routing logic, and deploy anywhere. This model suits platform teams that already manage their own infrastructure.

4. Evaluate ecosystem fit

For enterprises that depend on integrated AI stacks or cross-cloud operations, NeuralTrust and Portkey offer APIs and telemetry designed to blend with MLOps and SIEM systems. Look for gateways that complement your deployment model, not compete with it.

Ultimately, the right AI gateway provides balance: the security to protect sensitive data, the performance to scale globally, and the visibility to stay compliant as AI workloads evolve.

Conclusion: The Control Layer for Enterprise AI Maturity

AI gateways have become the foundation of enterprise-scale operations. They transform disconnected model calls into structured, monitored, and compliant workflows. As organizations expand their use of large language models, this layer provides the governance, cost control, and security required for sustainable adoption.

The key is not choosing the most complex platform, but selecting one that fits your operational goals and compliance posture. The right gateway turns AI from a prototype into an auditable system of record, one where performance, privacy, and reliability are measurable in real time.

In a landscape defined by rapid change, AI gateways represent stability. They are the control layer that allows innovation to scale without losing trust, transparency, or accountability.