Most teams pick an AI gateway to control LLM traffic. That is the wrong problem to solve first. Routing and rate-limiting prevent cost blowouts, but they cannot prevent an agent from hallucinating due to stale context or leaking data through an ungoverned connector. The harder problem is the business data underneath the models: who can see what, how fresh it is, and whether every query is auditable.
This article walks through the strongest AI gateways on the market, ranks them on routing, control, and governance, and shows why the data layer is where scalable agent infrastructure begins.
TL;DR Airbyte Agents: MCP-native context and routing layer with a Context Store across 600+ sources and row-level ACLs enforced at query time.Portkey: LLM gateway with semantic caching, fallback routing, and cost tracking across 1,600+ models.LiteLLM: Open-source proxy that unifies 100+ providers behind the OpenAI format, with budget controls and load balancing.Kong AI Gateway: Enterprise layer on Kong Gateway adding token rate limiting, prompt injection protection, and PII sanitization.AWS Bedrock (with Guardrails): A managed gateway within the AWS ecosystem with content filtering, PII masking, and CloudWatch observability.Want to see permission-aware context and MCP routing in action before you finish reading? Try Airbyte Agents free on the 1,000 Agent Operations monthly tier and build against your own data in an afternoon.
Why AI Gateway Architecture Is the Backbone of Scalable Agent Infrastructure An AI gateway is a dedicated middleware layer that manages and secures interactions between your applications and AI models. It authenticates, authorizes, routes, rate-limits, masks data, and manages prompts under a single control plane. It is not an orchestration framework or a vector database, and it overlaps with but is broader than an MCP gateway purpose-built for agent traffic. It governs the traffic generated by those tools.
It differs from a traditional API gateway because LLM traffic behaves differently. Three pillars organize the differences. Routing decides which model handles which prompt and what happens on failure. Control caps spend and throughput through token-based limits, budgets, and caching. Governance enforces access, audit logging, and compliance across every call.
These pillars map directly to the pain engineers feel in production: cost blowouts from unbounded token spend, security gaps from ungoverned data access, hallucinations from stale context, and compliance blockers when an audit finds sensitive data flowing to a third-party model.
Traditional API Gateway vs AI Gateway The table below shows the core mechanical difference between a legacy API gateway and an AI-native one.
Dimension Traditional API Gateway AI Gateway Rate limiting Per request Per token Caching Exact-match keys Semantic, embedding-based Content inspection Minimal Prompt injection, PII redaction Access control Endpoint-level Row- and user-level Cost signal Requests per second Tokens and spend per team
These mechanical differences explain why bolting AI onto a legacy proxy tends to break. The unit of consumption changed, and so did the surface area for risk, which is exactly what the platforms below were built to handle.
What Platforms Help You Route, Control, and Govern AI Traffic? We selected the tools below on routing intelligence, governance depth, observability, data access, and open-source availability. Some focus purely on LLM traffic. Others reach deeper into the business data your agents actually need, replacing fragile runtime API crawls with a governed Context Store that agents can query directly. The list begins with the one that governs the data layer.
1. Airbyte Agents Airbyte Agents manages the harder problem beneath the traffic layer, granting agents permission-aware access to real business data via open-source, type-safe connectors and low-latency search. Its Context Store, a managed searchable replica of select entities from your connected sources, replaces live runtime API calls with a pre-indexed layer that improves speed and reduces token consumption. For routing, it operates as a unified Agent MCP endpoint across all tools, with incremental updates via Change Data Capture and access controls for both structured records and unstructured files.
Key Features
Type-safe connectors with managed credentials, covering 600+ replication connectors and 50+ agent connectors. Pre-indexed Context Store that replaces fragile runtime API crawls with low-latency search. Row- and user-level ACLs are enforced before data reaches the agent, with immutable append-only audit logs. Unified Agent MCP endpoint plus Agent SDK, Agent CLI, Agent API, and web app access. SOC 2 Type II, ISO 27001, GDPR, and HIPAA Conduit certifications out of the box. Airbyte Agents is the strongest fit for teams whose agents need to answer questions spanning multiple business systems across tools like Salesforce, Zendesk, and Stripe. Startups ship on real customer data via the free tier of 1,000 Agent Operations per month, while enterprises pass security reviews with a single credential flow instead of ungoverned MCP sprawl. Because governance sits at the data layer, hallucinations and audit gaps are prevented before they reach the model.
2. Portkey Portkey is an operational control plane that sits between your applications and model providers, covering gateway, AI observability , guardrails, governance, and prompt management. Its model support covers 1,600+ LLMs and processes trillions of tokens daily, with a two-minute, three-line integration. It focuses on the traffic layer, giving engineering teams a single place to route, cache, and monitor LLM calls without having to build that plumbing themselves.
Key Features
Fallback routing that switches to a backup model on any non-2xx status code. Conditional routing that directs requests based on edge metadata. Load balancing that distributes traffic across keys and providers. A semantic cache that can serve responses up to 20x faster and more cheaply. Prompt management, guardrails, and cost analytics under one control plane. Portkey is best suited for AI-native product teams running many models in production who need mature routing and observability without having to operate their own proxy. The main benefit is breadth: broad provider coverage, quick integration, and strong cost visibility across teams. The limitations are twofold: semantic caching is Enterprise-only, and Portkey is a traffic layer, not a data-context governance platform, so it does not maintain a permission-aware replica of your business data.
3. LiteLLM LiteLLM is an open-source AI gateway and Python SDK that provides a single, unified interface to call 100+ LLM providers in the OpenAI format . With 240M+ Docker pulls, it has strong community traction and appeals to engineers who want vendor-neutral model switching without rewriting call sites for each provider. It self-hosts with a Docker quick start plus Postgres and Redis, which keeps it approachable for small teams that want to run their own gateway.
Key Features
Unified OpenAI-format interface across 100+ LLM providers. Routing strategies include least-busy, latency-based, and cost-based selection. Typed fallbacks for content policy and context window failures. Budget controls, virtual keys, and per-team spend tracking. Self-hosted deployment with Docker, Postgres, and Redis. LiteLLM is best suited for engineering teams that want an open-source, vendor-neutral proxy they can run themselves and swap models without rewriting code. The benefit is transparency and portability: the codebase is open, the interface is familiar, and it removes the cost of coupling application code to a specific provider. The limitation is that several features engineers assume are free, including SSO/SAML, audit logs, JWT Auth, and budget tiers, sit behind the Enterprise wall, and, like the others here, it stops at the LLM API layer.
4. Kong AI Gateway Kong AI Gateway is a connectivity and governance layer for AI-native applications built on top of Kong Gateway. Teams already running Kong can layer AI-specific plugins onto a runtime that inherits sub-millisecond latency, making it an extension rather than a new platform to stand up. Its AI Proxy plugins provide a standardized interface across providers, with dynamic and semantic routing that selects the best model for each prompt.
Key Features
AI Proxy plugins with dynamic and semantic routing across providers. AI Rate Limiting Advanced plugin for time-based token or cost limits per team or user. AI Semantic Cache that delivers 3–10x latency improvements on repeated prompts. AI Semantic Prompt Guard that blocks prompt injection attempts. AI PII Sanitizer that redacts sensitive data across 20 categories and 9 languages. Kong AI Gateway is best suited for platform teams already running Kong who want to extend an existing footprint into AI workloads rather than add a new vendor. The benefits are operational leverage: sub-millisecond latency, a familiar plugin architecture, and enterprise-grade security controls layered on infrastructure the team already trusts. The limitation is that its heritage is API-gateway-first rather than agent-native, so teams building agent stacks from scratch may find it heavier than needed and still have to solve data-layer governance separately.
5. AWS Bedrock (With Guardrails) AWS Bedrock is a managed AI gateway inside the AWS ecosystem that gives teams access to 100+ foundation models with routing, safety, and observability built in. For AWS-native enterprise stacks, it consolidates a lot into infrastructure your team already trusts. It leans on native AWS services like CloudWatch, IAM, and KMS so that AI traffic inherits the same operational posture as the rest of the account.
Key Features
Access to 100+ foundation models through a single managed endpoint. Intelligent Prompt Routing that can reduce costs by up to 30% without compromising accuracy. Bedrock Guardrails with six safeguard policy types applied via the ApplyGuardrail API. CloudWatch metrics for invocations, latency, and token counts. Native integration with IAM, VPC, and KMS for enterprise controls. AWS Bedrock is best suited for enterprise teams already standardized on AWS who want a managed AI gateway that fits inside their existing account, billing, and compliance boundaries. The benefit is consolidation: one bill, one identity model, and one observability surface for both AI and the rest of the stack. The limitations come from ecosystem gravity: Intelligent Prompt Routing works only within the same model family, log destinations stay within the same account and Region, and the PII filter does not cover tool_use output parameters.
Ready to Govern the Data Layer? Across these five options, the split is clear. Portkey, LiteLLM, Kong, and Bedrock all govern the LLM call. Only one option governs the data your agents pull into that call.
Routing, caching, and rate limiting solve the traffic problem. They do not solve the data problem. Your agents can route perfectly across models and still produce hallucinations because they stitch together fragmented context from five APIs at runtime, with no unified permission model underlying them. That gap is where cost blowouts, audit failures, and wrong answers come from, and an LLM traffic layer alone cannot close it.
Airbyte Agents closes it by governing the data layer directly, with a pre-indexed, permission-aware Context Store; row- and user-level ACLs enforced before data reaches the model; immutable audit logging for every query; and a unified Agent MCP endpoint across 600+ sources. Startups ship on real customer data in an afternoon. Enterprise leads pass their security review using a single credential flow rather than ungoverned MCP sprawl.
To see it running against your own stack, get a demo with our team today.
Frequently Asked Questions What Is an AI Gateway? An AI gateway is a dedicated middleware layer that sits between your applications and AI models. It authenticates, routes, rate-limits, masks data, manages prompts, and enforces policy under a single control plane. It governs the traffic your agents and LLM frameworks generate.
Do AI Gateways Add Latency? Well-designed ones reduce it more often than not. Semantic cache hits typically return in under 5 milliseconds, compared with 2 to 5 seconds for a full model call. Kong inherits sub-millisecond latency from its runtime; AWS reports router overhead of roughly 85 ms at P90; and Airbyte's Context Store returns answers via pre-indexed search rather than slow live API crawls.
How Do AI Gateways Handle PII and Compliance? Approaches vary. AWS Bedrock Guardrails blocks or masks 20+ predefined PII types. Kong's AI PII Sanitizer covers 20 categories across 9 languages. Airbyte Agents enforce row- and user-level ACLs before data reaches the agent, log every query immutably, and maintain SOC 2 Type II, ISO 27001, GDPR, and HIPAA Conduit certifications.
Do AI Gateways Prevent Vendor Lock-In? Provider-neutral tools like LiteLLM and Portkey unify many providers into a single interface, so you can switch models without rewriting code. Ecosystem-native options like AWS Bedrock offer breadth within their platform but route only within the same model family. Open-source options like LiteLLM add code transparency on top of that.
Are AI Gateways Only for Enterprises? No. Startups use them to control cost and ship faster, and both Airbyte Agents and LiteLLM offer open-source and free-tier paths that require no procurement cycle. Airbyte's free tier includes 1,000 Agent Operations per month, so a small team can build on real data before any sales conversation.
