How to Choose AI Gateway Vendors and 14 Solutions Compared

What Are AI Gateway Vendors?

AI gateway vendors provide software solutions that act as intermediaries between enterprise applications and AI model providers, such as OpenAI, Anthropic, or Google. These platforms offer a unified API that enables organizations to interact with multiple AI models through a single integration point.

To choose the right AI gateway vendor, evaluate your infrastructure stack, performance needs, compliance rules, and caching features. Start by assessing your current tools and team capabilities to dictate whether you need a quick managed service, an open-source solution, or deep Kubernetes and edge infrastructure.

Key evaluation criteria:

Use these five dimensions to compare any AI gateway against your own requirements:

• Model, provider, and protocol coverage: what models, providers, tools, and protocols (such as MCP and A2A) the gateway can connect to, and how easily you can add or switch them.
• Security, access control, and governance: authentication, authorization, data protection, guardrails, audit trails, and compliance.
• Cost management and observability: usage and cost tracking, budgets and quotas, logging, tracing, and analytics.
• Deployment and scalability: SaaS, self-hosted, VPC, on-prem, or air-gapped options, plus latency, throughput, and availability.
• Integration and extensibility: migration effort, fit with your existing stack, and how far you can customize behavior.

Solutions compared in this guide:

• API security and management platforms with AI gateway capabilities
  • Cequence AI Gateway
  • Kong AI Gateway
  • Zuplo
  • Solo.io Gloo AI Gateway
  • F5 AI Gateway
• AI-native gateway and LLMOps platforms
  • Portkey
  • TrueFoundry AI Gateway
  • Helicone AI Gateway
  • Lunar.dev
• Developer-first and open-source LLM routers
  • LiteLLM
  • OpenRouter
  • Vercel AI Gateway
  • Cloudflare AI Gateway
  • Maxim AI (Bifrost)

In this article:

• When Should You Use an AI Gateway Vendor?
• How to Choose an AI Gateway Vendor
• Common AI Gateway Solutions and How They Meet the Criteria
• Notable AI Gateway Solutions

When Should You Use an AI Gateway Vendor?

You Use Multiple AI Model Providers

Organizations often experiment with or deploy models from different providers to meet varying requirements for cost, accuracy, or compliance. Directly integrating with each provider creates overhead in managing distinct APIs, credentials, and model behaviors. An AI gateway vendor abstracts these differences, offering a single API surface while handling provider-specific nuances behind the scenes. This allows development teams to switch providers or models without major code changes or disruptions to production systems.

Using multiple AI model providers also raises challenges in monitoring, cost tracking, and policy enforcement. An AI gateway consolidates usage data and provides unified analytics, enabling organizations to track costs, allocate spending, and enforce quotas across all providers. This centralization helps avoid vendor lock-in, supports A/B testing of models, and ensures business continuity if a provider experiences downtime or changes its offerings.

Teams Are Building AI Applications Independently

In large organizations, different teams may develop AI applications independently, each adopting their own approach to integrating with model providers. This can lead to inconsistent security practices, duplicated effort, and fragmented governance. An AI gateway vendor imposes a common integration layer, ensuring that all teams benefit from standardized authentication, logging, and monitoring. It also allows IT and security teams to set global policies and audit all AI usage from a central dashboard.

Standardization through an AI gateway reduces operational risk and accelerates onboarding for new projects. Teams can focus on application logic rather than infrastructure, while platform teams retain control over which models are accessible, how data is handled, and what compliance measures are in place. This structure is essential for scaling AI adoption across business units without sacrificing security or oversight.

AI Agents Can Access Enterprise Systems

AI agents with access to enterprise systems (such as databases, CRMs, or internal APIs) introduce new risks around data leakage, privilege escalation, and unauthorized actions. An AI gateway vendor provides granular access controls and audit trails that are tailored for AI-driven interactions. By mediating requests between AI agents and sensitive resources, the gateway can enforce policies, mask confidential data, and log all actions for compliance review.

Enterprises can configure the gateway to restrict which data fields or system functions are exposed to AI agents, preventing accidental or malicious misuse. This is especially important when integrating generative AI models, which may unpredictably use or output sensitive information. AI gateways enable organizations to confidently extend AI capabilities while maintaining strong safeguards over critical systems and data.

Sensitive Data Is Included in AI Requests

Many AI use cases require processing sensitive or regulated data, such as personal information, financial records, or intellectual property. Sending such data directly to external model providers can violate compliance requirements or increase exposure risk. AI gateway vendors offer built-in data protection mechanisms, such as redaction, encryption, or tokenization, that operate before data leaves the organization. These controls help ensure that only necessary information is shared with the AI model and that sensitive elements remain protected.

Gateways also provide detailed logging and policy enforcement, enabling organizations to demonstrate compliance with data privacy regulations like GDPR or HIPAA. They can restrict which data types are allowed in requests, automatically flag or block violations, and generate reports for audits. This centralized approach is critical for industries with strict data governance needs or where AI adoption depends on robust security assurances.

Related content: Read our guide to sensitive information disclosure and how to defend against it.

Existing API Gateways Lack AI-Specific Controls

Traditional API gateways are designed for generic web services, not the unique requirements of AI workloads. They often lack features needed for managing model selection, prompt inspection, or usage metering at the level required by AI governance frameworks. AI gateway vendors address these gaps with capabilities such as prompt filtering, response validation, model version management, and real-time usage analytics tailored to AI APIs.

By complementing or integrating with existing API gateways, AI-specific gateways provide the necessary granularity for controlling and monitoring AI interactions. This includes features like blocking unsafe prompts, enforcing model-specific access, or setting usage limits based on business rules. Adopting an AI gateway ensures that organizations can safely scale AI usage without compromising on oversight, compliance, or operational efficiency.

Related content: Read our guide to API security.

How to Choose an AI Gateway Vendor

1. Model, Provider, and Protocol Coverage

An AI gateway sits between your applications and agents and the models or systems they call, so the first thing to establish is what it can actually connect to. Some gateways focus on routing requests across many LLM providers through one unified API. Others focus on connecting agents to enterprise APIs, tools, and data using protocols such as the Model Context Protocol (MCP) and agent-to-agent (A2A) communication. The breadth of models, providers, and protocols a gateway supports determines how much integration work you avoid and how easily you can switch or combine systems later.

Evaluation criteria:

• Does it expose a single, OpenAI-compatible (or similar) API across multiple model providers?
• How many models and providers does it support, and can you add new ones through configuration rather than code?
• Does it support MCP, A2A, or tool and agent connectivity, not just raw LLM calls?
• Can it route to self-hosted or open-source models alongside hosted providers?

2. Security, Access Control, and Governance

Because a gateway becomes the single point through which AI traffic and credentials flow, its security and governance controls carry a lot of weight. This dimension covers how the gateway authenticates and authorizes users, agents, and applications, how it protects sensitive data in prompts and responses, and how it enforces organizational policy. For teams exposing internal systems to autonomous agents, controls such as least-privilege access, guardrails, and audit trails move from nice-to-have to mandatory.

Evaluation criteria:

• Does it integrate with your identity provider (OAuth/OIDC, SSO, SCIM) and support role-based access control?
• Can it detect, redact, or block sensitive data such as PII and enforce guardrails against prompt injection or unsafe output?
• Does it provide complete audit logs that attribute actions to a specific user, agent, or tool call?
• Does it hold relevant compliance certifications (SOC 2, HIPAA, GDPR) or map to frameworks such as the EU AI Act?

Related content: Read our guide to AI governance, including risks, frameworks, and components.

3. Cost Management and Observability

LLM spend is unpredictable because pricing is token-based and usage can spike without warning. A capable gateway gives you visibility into what is being consumed and spent, plus the controls to cap it. This dimension covers usage and cost tracking, budgets and quotas, and the depth of logging, tracing, and analytics available for debugging and reporting.

Evaluation criteria:

• Does it track token usage and cost per user, team, model, or application in real time?
• Can you set budgets, quotas, or token and dollar rate limits with hard stops, not just alerts?
• Does it offer request-level logging and tracing, including for multi-step agent workflows?
• Can telemetry be exported to your existing observability or SIEM stack?

4. Deployment and Scalability

Where and how a gateway runs affects both your compliance posture and its performance in production. Deployment options range from fully managed SaaS to self-hosted, private cloud, and fully air-gapped installations, and each carries trade-offs around control, data residency, and operational burden. Scalability and added latency matter too, because a gateway sits directly in the request path.

Evaluation criteria:

• Does it support the deployment models you need (SaaS, self-hosted, VPC, on-prem, air-gapped)?
• What latency overhead does it add, and can it sustain your expected throughput?
• Does it offer high availability and failover so it is not a single point of failure?
• Can sensitive data stay within your infrastructure or region when required?

5. Integration and Extensibility

A gateway is only useful if it fits the stack your teams already run and can adapt as requirements change. This dimension covers how easily existing applications migrate to it, whether it complements your current API management and identity tooling, and how far you can customize its behavior. Ease of onboarding and the availability of programmable policies often decide how quickly a gateway delivers value.

Evaluation criteria:

• Can existing OpenAI or Anthropic integrations migrate with a base URL swap rather than a rewrite?
• Does it complement your existing API gateway, identity provider, and agent frameworks?
• Can you write custom routing, policies, or guardrails when off-the-shelf options fall short?
• How quickly can a new application or team be onboarded and governed?

Common AI Gateway Solutions and How They Meet the Criteria

The table below summarizes how each solution measures up against the five criteria. Each is explored in detail in the sections that follow.

Notable AI Gateway Solutions

How we selected these solutions: We shortlisted AI gateway platforms based on their ability to route and govern traffic between applications, agents, and models, connect to multiple providers and tools, enforce security and access controls, and provide cost visibility and observability in production.

API Security and Management Platforms with AI Gateway Capabilities

1. Cequence AI Gateway

Best for: Security teams safely connecting AI agents to enterprise apps and data.

Strengths: Agent-level governance, MCP enablement, OAuth 2.1, DLP, and audit logging.

Things to consider: Built for end-to-end agentic AI governance, not an API management platform.

The Cequence AI Gateway is a governance layer that connects and protects enterprise applications and data as they are exposed to AI agents. It transforms existing internal, external, and SaaS APIs into MCP-compatible tools without coding, so agents can discover and use them at runtime. Rather than only authorizing a call, it governs the agent itself, applying policy inline on every tool call for the full session.

At its center is an agentic zero trust approach. The gateway authenticates an agent, then verifies every subsequent action against a defined boundary. Its Agent Personas feature lets a team describe an agent’s job in plain English and automatically scopes the tools, APIs, skills, and permissions that agent is allowed to use. The gateway integrates with the Cequence platform’s API Security and Bot Management for broader protection against agent-driven abuse.

Key features include:

• MCP enablement without code: Uploading OpenAPI specifications turns traditional APIs into agent-ready MCP tools, with native support for connecting agents to more than 140 enterprise applications such as Salesforce, Jira, Slack, and Confluence.
• Agent Personas and least-privilege access: Plain-English job descriptions generate dynamically scoped access policies, binding each agent to only the tools and data it needs and judging actions on both identity and behavior.
• Identity and access governance: Integration with OAuth 2.1-compliant identity providers, token lifecycle management, RBAC, and session binding that locks authenticated sessions to their originating IP to prevent token reuse.
• Sensitive data protection: DLP scanning of agent requests and MCP responses across more than 100 detection types, with the ability to monitor, redact, or block sensitive data and integrate with existing DLP infrastructure.
• Monitoring, discovery, and audit: Real-time visibility and full audit logging of user, agent, and tool behavior, plus AI discovery that surfaces sanctioned and shadow agents, MCP servers, and LLM providers from existing SIEM logs.
• Enterprise deployment and trusted registries: SaaS and on-premises deployment with horizontal scaling, discrete pre-production and production modes, and trusted registries of vetted MCP servers, APIs, and skills with automated tool risk scoring.
• AI discovery: Discovers Agents, MCP servers, and LLM providers across the enterprise from SIEM logs. Surfaces shadow MCP/AI usage and brings it under governance.
• Prompt injection protection: Prompt Guard screens prompts for injection, jailbreak, and system-prompt-extraction patterns before they reach the model, like semantic threat detection for LLM traffic.

Source: Cequence

2. Kong AI Gateway

Best for: Teams already running Kong for API management adding AI traffic.

Strengths: LLM, MCP, and A2A governance on one proven runtime with 1,000+ plugins.

Things to consider: Configuration and enterprise pricing grow complex at scale.

The Kong AI Gateway extends the Kong Gateway runtime to govern generative and agentic AI traffic through a single platform. It handles three patterns of AI traffic: LLM connectivity, MCP tool access, and agent-to-agent (A2A) communication. Because it is built on the same core as Kong’s API gateway, its existing plugin ecosystem for authentication, traffic control, and transformations applies to AI traffic as well.

For LLM traffic, Kong offers a unified interface across multiple providers so teams can switch models without rewriting code. It layers on semantic caching, routing, load balancing, PII sanitization, and prompt guards. For agents, it can automatically generate MCP tools and servers on top of Kong-managed APIs and enforce authentication for MCP access, and it captures telemetry on A2A calls.

Key features include:

• Unified multi-LLM access: A single API interface works across multiple AI providers, allowing teams to switch between providers to unlock use cases or maintain availability during provider downtime.
• LLM policy enforcement: PII sanitization to reduce data leakage, semantic prompt guards, access control, and semantic caching, routing, and load balancing to make LLM traffic more efficient.
• MCP and A2A governance: Automatic generation of MCP tools and servers on Kong-managed APIs, authentication for MCP server access, context optimization, and centralized authentication and auditing for A2A traffic.
• AI quota and cost management: User, model, and time-bound quotas on LLM consumption and token spend, enforced at the gateway, with showback and chargeback across LLM, agent, and MCP usage.
• L7 observability: Tracking of AI consumption, tool usage, and token spend, with logging and tracing to debug AI exposure and predictive consumption models for cost tuning.
• Flexible runtimes: Deployment as open-source, enterprise self-hosted, or cloud-managed through Kong Konnect.

Source: Kong

3. Zuplo

Best for: Teams wanting one programmable gateway for REST, LLM, and MCP traffic.

Strengths: Hierarchical dollar budgets, TypeScript policies, and edge deployment.

Things to consider: Programmability assumes TypeScript; primarily cloud-hosted.

Zuplo is a programmable API gateway with a full AI gateway feature set built in, so LLM calls, agents, and MCP servers run behind the same gateway as your REST APIs. It routes requests to OpenAI, Anthropic, Gemini, and Mistral through one endpoint, with providers swapped in configuration rather than code. Because it runs on the same policy engine as Zuplo’s API gateway, teams manage AI and traditional traffic from one control plane.

A distinguishing element is its hierarchical budget model. Rather than a single global cap, Zuplo lets organizations nest teams, sub-teams, and apps, each with daily and monthly dollar budgets that cascade from the top down and halt requests when hit. It also runs at the edge across a large points-of-presence network and exposes TypeScript programmability for custom policies.

Key features include:

• Multi-provider routing: A single endpoint routes to OpenAI, Anthropic, Gemini, and Mistral, with per-route, per-tenant, or per-key model selection and drop-in compatibility with the OpenAI SDK base URL.
• Hierarchical dollar budgets: Nested organization, team, sub-team, and app budgets in USD, where sub-team limits cannot exceed the parent ceiling and requests stop with hard 429s before overspend.
• AI security policies: A prompt-injection policy blocks malicious instructions before they reach the model, and a secret-masking policy redacts API keys, emails, and other sensitive values from responses.
• Semantic caching: Responses are cached by vector similarity rather than exact match to cut latency and spend on repeated or similar prompts.
• MCP support: An MCP Server Handler exposes API endpoints as MCP tools, and an MCP Gateway federates remote MCP servers behind a single OAuth 2.1 endpoint with per-tool governance and audit logging.
• Observability and edge deployment: Every request and tool call is logged with caller identity, tokens, and cost, streamed to Galileo, Comet Opik, or an OTel collector, running on a 300-plus point-of-presence edge network.

Source: Zuplo

4. Solo.io Gloo AI Gateway

Best for: Cloud-native and Kubernetes teams needing AI-native traffic control.

Strengths: Rust and Envoy performance across LLM, MCP/A2A, and inference routing.

Things to consider: Kubernetes-oriented; recently rebranded to agentgateway.

Solo.io’s AI gateway, delivered as agentgateway and building on its Gloo Gateway lineage, is an AI-native gateway written in Rust and hosted as a Linux Foundation project. It is designed around three use cases in one gateway: an LLM gateway for unified provider access, an MCP and A2A gateway for tool and agent federation, and an inference gateway for routing to self-hosted models. It is built on Envoy and the Kubernetes Gateway API, so it fits cloud-native environments.

As an LLM gateway, it exposes an OpenAI-compatible endpoint for OpenAI, Anthropic, Azure, Gemini, and self-hosted models, with inline guardrails, centralized key storage, and token-based rate limits. As an MCP and A2A gateway, it turns OpenAPI specs into MCP tools, federates agents over the A2A protocol, and sandboxes shadow MCP access. Its inference gateway routes and prioritizes traffic to self-hosted models with context-aware routing.

Key features include:

• Unified LLM access: One OpenAI-compatible API works across providers and self-hosted models, so teams can switch models without changing code, with real-time logging and end-to-end tracing via OpenTelemetry.
• MCP and A2A gateway: OpenAPI specs become MCP tools without code changes, agents federate over Google’s A2A protocol, and every elicited URL and tool call is validated, sandboxed, and audited from one control point.
• Inline guardrails and key management: Custom semantic rules block prompt attacks and data leaks on every prompt and response, and provider keys are centralized with enterprise IAM integration and fine-grained access control.
• Inference gateway: Requests route to specialized fine-tuned models, priority scheduling protects critical workloads, and real-time inference metrics and llm-d integration improve GPU utilization and time to first token.
• Spending controls: Token-based rate limiting per user, team, or key, budget enforcement, per-model cost attribution, and model failover for price and availability.
• Rust performance and open governance: Sub-millisecond overhead at high query rates, an Apache 2.0 open-source core, and backing from Microsoft, T-Mobile, Dell, CoreWeave, and Akamai.

Source: Solo.io

5. F5 AI Gateway

Best for: Enterprises securing AI models and agents within an F5 estate.

Strengths: Prompt-injection defense, data-leak prevention, and compliance guardrails.

Things to consider: Security-forward rather than a routing or cost-control layer.

F5’s AI offering focuses on securing AI models and agents at runtime as part of its broader application delivery and security portfolio. It protects AI systems against adversarial attacks, data leakage, and harmful outputs, with deployment flexibility across public cloud, private cloud, on-prem, and air-gapped environments. It applies consistent, model-agnostic policy across public and proprietary models and integrates with F5’s NGINX and BIG-IP platforms.

Its protections are informed by a large threat library and F5 Labs research, and it targets the OWASP Top 10 for LLM risks. Custom policies can be created through a natural language interface, and out-of-the-box controls map to PII, EU AI Act, PCI, and PHI requirements. Audit-ready logging traces every enforcement action and can be exported to a SIEM.

Key features include:

• Prompt injection and jailbreak defense: Protection against prompt injection, data exfiltration, and jailbreak attacks, informed by a threat library that adds attack patterns on an ongoing basis.
• Data-leak prevention: Semantic data security detects and prevents leakage of standard and custom sensitive data categories during AI interactions, with model-agnostic policy enforcement.
• Compliance controls: Out-of-the-box guardrails aligned to PII, EU AI Act, PCI, and PHI, plus automated auditing templates for regulations such as GDPR and HIPAA.
• Agent security: Controls that prevent excessive agency and privilege escalation by enforcing guardrails on agent actions and tool use, applied to OpenAI, Anthropic, or similarly formatted agents.
• Observability and audit: Audit-ready logging that explains and traces every enforcement action, agent visibility into prompts and tool calls, and a performance dashboard exportable to a SIEM.
• Deployment flexibility: Model-agnostic enforcement across public cloud, private cloud, on-prem, and fully air-gapped environments, with native integration into F5 NGINX and BIG-IP.

AI-Native Gateway and LLMOps Platforms

6. Portkey

Best for: Teams needing an LLMOps control plane across many providers.

Strengths: 1,600+ models, routing, 50+ guardrails, governance, and observability.

Things to consider: Log-based pricing and tiered enterprise governance features.

Portkey is an enterprise AI gateway and production control plane for generative AI workloads. It provides a unified API across 1,600-plus LLMs and providers and extends the gateway with observability, guardrails, governance, and prompt management. It is designed to take AI applications from prototype to production, with an open-source gateway core and enterprise controls layered on top.

For reliability, it offers conditional routing, fallbacks, retries, load balancing, and canary testing, plus simple and semantic caching. For governance, it provides workspaces, RBAC, budgets, data residency, SSO and SCIM, audit logs, and policy-as-code. Its guardrails library covers PII redaction, jailbreak detection, and safety filters. Portkey was recently acquired by Palo Alto Networks.

Key features include:

• Unified model access: A single API connects to 1,600-plus LLMs and providers across text, vision, audio, and image modalities, with no separate integration per provider.
• Routing and reliability: Conditional routing, fallbacks, automatic retries, load balancing, canary testing, and request timeouts keep applications online during provider failures.
• Guardrails and safety: A library of guardrails enforces PII redaction, jailbreak detection, toxicity and safety filters, and request and response policy checks for agentic workflows.
• Governance and access control: Workspaces, RBAC, budgets, rate limits, quotas, data residency, SSO and SCIM, audit logs, and policy-as-code for consistent enforcement.
• Observability: Detailed logs, latency traces, and token and cost analytics broken down by app, team, or model, with export to reporting tools.
• Key management and prompt tooling: A vault for provider keys with virtual keys for rotation and access control, plus prompt templates, versioning, and environment promotion.

Source: Portkey

7. TrueFoundry AI Gateway

Best for: Enterprises governing multi-provider AI with self-hosted control.

Strengths: 1,600+ models, low-latency routing, RBAC, MCP, and air-gapped deployment.

Things to consider: Feature-rich and infrastructure-heavy for smaller teams.

TrueFoundry provides a unified AI gateway to manage and govern AI across 1,600-plus models with policy control, real-time monitoring, and cost controls. It connects to OpenAI, Claude, Gemini, Groq, Mistral, and more through one API and supports chat, completion, embedding, and reranking model types. It reports low internal latency and high throughput, and it centralizes key management and team authentication.

Beyond routing, it offers quota and access control, observability, guardrails, and native MCP support for agent workflows. It can also serve self-hosted open-source models in VPC, hybrid, or air-gapped environments using the same policies as hosted models, which suits teams that want infrastructure ownership and data residency.

Key features include:

• Unified model access: One API and one gateway key reach 1,600-plus models across providers, with centralized key management and the ability to swap providers without changing application code.
• Quota and access control: Rate limits per user, service, or endpoint, cost and token-based quotas using metadata filters, RBAC, and governance of service accounts and agent workloads.
• Routing and fallbacks: Latency-based routing to the fastest model, weighted load balancing, automatic fallback to secondary models, and geo-aware routing for regional compliance.
• Observability: Monitoring of token usage, latency, error rates, and request volumes, full request and response logging, metadata tagging, and filtering by model, team, or geography.
• Guardrails and self-hosted models: PII filtering and toxicity detection, integration with external moderation services, and serving of open-source models via vLLM, SGLang, KServe, and Triton.
• MCP and enterprise controls: Native MCP support for tools such as Slack, GitHub, and Datadog with OAuth2 and RBAC, plus SOC 2, HIPAA, and GDPR, SSO, audit logging, and 24/7 SLA-backed support.

Source: TrueFoundry

8. Helicone AI Gateway

Best for: Teams prioritizing lightweight routing with deep observability.

Strengths: Open-source Rust router, smart routing, caching, and rate limits.

Things to consider: Governance and native MCP support are limited.

Helicone’s AI Gateway is an open-source router written in Rust that pairs LLM request routing with observability. It exposes one API for 100-plus models across 20-plus providers using OpenAI syntax, so teams stop rewriting integrations per provider. It is positioned as a lightweight, high-performance layer that can be run cloud-hosted or self-hosted in seconds via Docker.

Its routing is aware of provider uptime and rate limits, with strategies for latency, cost, and weighted distribution. Every request is logged into Helicone’s monitoring, and OpenTelemetry support exports logs, metrics, and traces. Caching backed by Redis and S3 can cut cost and latency substantially for repeated requests.

Key features include:

• Unified interface: One API using familiar OpenAI syntax reaches OpenAI, Anthropic, Google, AWS Bedrock, and 20-plus more providers across 100-plus models.
• Smart routing: Built-in strategies include model-based latency routing, provider latency-based routing, weighted distribution, and cost optimization, aware of provider uptimes and rate limits.
• Spending control: Rate limiting per user, team, or globally, with support for request counts, token usage, and dollar amounts to prevent runaway costs.
• Caching: Response caching with Redis and S3 backends and intelligent invalidation to reduce cost and latency for repeated requests.
• Tracing and observability: Built-in Helicone integration plus OpenTelemetry support for logs, metrics, and traces, with a dashboard for monitoring and debugging.
• Flexible deployment: A cloud-hosted gateway or self-hosting via Docker and Kubernetes, with low latency and small memory footprint for production.

Source: Helicone

9. Lunar.dev

Best for: Enterprises governing employee and agent access to AI tools.

Strengths: Vetted MCP catalogs, identity-aligned audit, and self-hosted deployment.

Things to consider: MCP and agent governance first, not LLM inference routing.

Lunar.dev is an enterprise MCP gateway and AI control plane that centralizes how agents and AI applications authenticate, discover tools, and access enterprise resources. It is designed to give security and IT teams visibility and control as AI adoption outpaces governance, closing gaps around accountability, AI sprawl, and over-privileged agents. It is recognized by Gartner as a Representative Vendor in both AI Gateways and MCP Gateways.

The product starts with observability, capturing full lineage across user, agent, model, MCP server, and the final tool or API call, mapped to verified user identity. It then enforces access through an admin-vetted MCP catalog, a risk-evaluation sandbox, and dynamic, time-bound access controls, while keeping approved tools easy for teams to use. It runs self-hosted for data sovereignty.

Key features include:

• Full lineage observability: A centralized inventory of all agents and applications, identity-aligned attribution mapping every action to a verified user, and immutable real-time audit trails.
• Vetted tool access: An enterprise MCP catalog of admin-approved tools, a risk-evaluation sandbox to detect data exposure and unsafe tool execution before rollout, and dynamic, intent-aware, time-bound access.
• Governed skills and tool variants: Tool groups and Skills that combine workflow knowledge and MCP tools, with authentication, RBAC, audit, and versioning applied to every skill and call.
• User authentication without secret exposure: Requests are authenticated with user credentials and resource access is controlled without exposing keys or secrets, with hardened tool variants per agent logic.
• Enterprise assurance: Private deployment and data sovereignty, SSO, roles, audit logs, DLP and sensitive-data controls, and fault-tolerant design.
• Path to unified governance: An MCP gateway that extends to unify governance across LLM and API traffic as usage scales.

Source: Lunar.dev

Developer-First and Open-Source LLM Routers

10. LiteLLM

Best for: Platform teams giving developers self-hosted access to many LLMs.

Strengths: 100+ LLMs in OpenAI format, spend tracking, fallbacks, and virtual keys.

Things to consider: Self-managed infrastructure; enterprise controls are paid.

LiteLLM is an open-source AI gateway that provides model access, fallbacks, and spend tracking across 100-plus LLMs, all in the OpenAI format. It is delivered as a Python SDK and a proxy server that teams typically run and operate themselves, which gives full control over networking and data flow. It is widely adopted, with large download and request volumes and use by companies including Netflix and Lemonade.

Its core value is a single, OpenAI-compatible interface that lets platform teams give developers access to many providers while attributing cost to keys, users, teams, or organizations. It adds budgets and rate limits, load balancing, and fallbacks, with an enterprise tier that unlocks JWT auth, SSO, and audit logs.

Key features include:

• Unified model access: A single OpenAI-compatible API and pricing layer spans 100-plus LLM providers, enabling fast model switching and provider comparison during experimentation.
• Spend tracking: Automatic cost attribution to key, user, team, or organization, tag-based tracking, and logging of spend to s3, gcs, and other stores.
• Budgets and rate limits: Per-key and per-team budgets with RPM and TPM rate limits to prevent runaway usage.
• Reliability: LLM fallbacks and load balancing across providers with retry logic.
• Access control and keys: Virtual keys, teams, and, in the enterprise tier, JWT auth, SSO, and audit logs.
• Flexible deployment: Self-hosted by default, with air-gapped deployment available and infrastructure-as-code configuration.

Source: LiteLLM

11. OpenRouter

Best for: Developers wanting fast access to a large model catalog.

Strengths: 400+ models across 70+ providers with fallbacks and one billing account.

Things to consider: No self-hosting and limited routing and observability depth.

OpenRouter is a cloud-managed unified interface for LLMs that provides access to 400-plus models across 70-plus providers through a single, OpenAI-compatible endpoint. It emphasizes better prices and uptime with no subscriptions, and it consolidates billing into a single prepaid credit balance instead of separate provider accounts. Setup is quick because the API is compatible with the OpenAI SDK.

Beyond broad access, it provides higher availability by falling back to other providers when one goes down, runs at the edge for lower latency, and supports custom data policies so prompts only reach trusted models and providers. It also spans modalities including chat, image, embeddings, and transcription through one base URL.

Key features include:

• Broad model access: A single interface reaches 400-plus models across 70-plus providers, with the OpenAI SDK working by changing the base URL and key.
• Higher availability: Automatic fallback routes to another provider when the primary returns an error, backed by distributed infrastructure.
• Consolidated billing: A prepaid credit system manages a single balance across providers, with provider token rates passed through.
• Custom data policies: Fine-grained policies ensure prompts only go to the models and providers an organization trusts.
• Edge performance: Requests run at the edge to reduce latency between users and inference.
• Every modality: Chat, image generation, embeddings, and transcription run through one base URL by changing the model string and content type.

Source: OpenRouter

12. Vercel AI Gateway

Best for: Teams on Vercel wanting one endpoint across every modality.

Strengths: Hundreds of models, no markup, fallbacks, ZDR routing, and spend tracking.

Things to consider: Tied to Vercel; limited conditional routing and deep tracing.

Vercel AI Gateway is a cloud-managed routing layer that provides one API key and endpoint for hundreds of models across text, image, video, audio, realtime, speech, transcription, embeddings, and reranking. It passes through provider token rates with no markup, supports bring-your-own-keys, and offers automatic fallbacks when a provider goes down. Migration is typically a base URL swap for existing OpenAI or Anthropic integrations.

It optimizes routing for availability, cost, or latency, failing over to the same model on another provider when one degrades. It provides unified billing and observability across the stack, with dashboards for usage, spend, time to first token, and token counts, plus a Custom Reporting API. Security options include zero-data-retention routing and provider allowlists.

Key features include:

• Unified multimodal access: One endpoint and key reach hundreds of models across text, image, video, realtime, speech, transcription, embeddings, and reranking.
• Routing and fallbacks: Routing optimized for availability, cost, or latency, with automatic failover to the same model on another provider during outages.
• No-markup pricing and BYOK: Provider token rates are passed through with no platform fee, and existing provider commitments flow through with bring-your-own-keys.
• Security and compliance controls: Zero-data-retention routing, a no-training guarantee, provider allowlists, short-lived OIDC tokens, and per-user spend limits.
• Observability and reporting: Dashboards for usage, spend, requests, time to first token, and token counts by model, provider, and project, plus a Custom Reporting API.
• Coding agent support: Routing of coding agents such as Claude Code, OpenAI Codex, and Cline through the gateway with unified spend tracking.

Source: Vercel

13. Cloudflare AI Gateway

Best for: Teams on Cloudflare needing edge caching and basic AI controls.

Strengths: Global edge network, caching, dynamic routing, and observability.

Things to consider: Basic versus purpose-built gateways; no native MCP.

Cloudflare AI Gateway is an intelligent control plane for AI applications that connects to any model, dynamically routes requests, and manages usage, billing, and logs from one gateway. It runs on Cloudflare’s global network, so it provides low-latency, distributed access with automatic scaling and built-in security. It is compatible with the OpenAI SDK and the AI SDK and integrates with Cloudflare Workers AI.

Its emphasis is on reducing cost and latency through caching, improving reliability with dynamic routing and fallbacks, and adding observability such as token counts and prompt performance. It also provides rate limiting and safety guardrails, and it unifies billing across providers behind a single API.

Key features include:

• Dynamic routing: Requests route automatically based on latency, cost, or availability, with rules adjusted from the dashboard or API without redeploys.
• Caching: Automatic storage and reuse of frequent requests reduces redundant API calls to save cost and improve response times.
• Observability: Logs, metrics, and usage analytics including token counts, prompt performance, and pattern analysis, with support for custom dashboards and alerting.
• Reliability and safety controls: Fallback routing, rate limiting, and safety guardrails to manage cost, behavior, and compliance across providers.
• Security controls: Protection against leaking sensitive information and against malicious traffic without extra configuration.
• Edge network and unified billing: Global points of presence with automatic scaling, one API across providers, and a single consolidated bill.

Source: Cloudflare

14. Maxim AI (Bifrost)

Best for: Performance-sensitive teams wanting an open-source, low-overhead gateway.

Strengths: Very low latency in Go, governance, MCP, guardrails, and fallbacks.

Things to consider: Fewer native providers and lighter analytics than broad routers.

Bifrost is the AI gateway from Maxim AI, an open-source project under Apache 2.0 built in Go and positioned for high-performance, production workloads. It provides a drop-in, OpenAI-compatible API that routes to multiple providers with minimal overhead, and it integrates with Maxim’s broader evaluation and observability platform. Vendor benchmarks report very low added latency at high request rates.

It supports 1,000-plus models across providers through a unified interface, including custom deployed models, with automatic failover and load balancing. Enterprise features add governance, guardrails, and a built-in MCP gateway. Setup is designed to be fast, with a single command to run locally and a one-line change to point existing SDKs at it.

Key features include:

• Unified model access: A model catalog reaches 1,000-plus models across providers through a single OpenAI-compatible interface, including custom deployed models.
• Drop-in replacement: A one-line change points existing OpenAI, Anthropic, Vercel AI SDK, LangChain, or Google GenAI code at the gateway.
• Governance: Budgets per team or virtual key, audit logs, access control, and SSO, with virtual keys carrying independent budgets and access.
• Reliability: Automatic provider fallback and adaptive load balancing for high uptime, plus semantic caching to cut repeated inference cost.
• MCP gateway and guardrails: A built-in MCP gateway centralizes tool connections with policy enforcement, and guardrails block unsafe outputs and enforce compliance.
• Observability and performance: Out-of-the-box OpenTelemetry support and a built-in dashboard, with very low added latency at high request rates.

Source: Maxim AI

Conclusion

Choosing an AI gateway vendor requires balancing flexibility, governance, security, and operational simplicity. Organizations should evaluate how well a gateway supports their AI architecture, including model providers, agent frameworks, enterprise APIs, and deployment requirements, while also considering identity integration, policy enforcement, cost controls, and observability. As AI adoption expands across applications and autonomous agents, a centralized gateway helps reduce integration complexity, enforce consistent security policies, improve visibility into AI usage, and provide the governance needed to operate AI systems safely and at scale.