API gateway vs. AI gateway for modern AI infrastructure

At a glance

What this is: This is a comparison of traditional API gateways and AI gateways, showing that AI workloads need token-aware, streaming-native, content-aware controls that standard gateways do not provide.

Why it matters: It matters because identity and access programmes now have to govern not just human and machine traffic, but AI inference paths, model access, and content-level risk in the same control plane.

By the numbers:

• The AI gateway market was valued at USD 3911 Million in 2024 and is projected to reach USD 9843 Million by 2031, growing at a CAGR of 14.3%.
• Organizations will develop 80% of GenAI business applications on existing data management platforms by 2028, reducing complexity and delivery time by 50%.
• The OWASP ranked prompt injection as the top security risk in its 2025 OWASP Top 10 for LLM Applications report.

👉 Read Kong's guide to API gateway and AI gateway design for modern AI infrastructure

Context

AI gateways exist because traditional API gateways were built for request-response traffic, not for token streams, model routing, or content-aware policy enforcement. In practice, that means organisations can authenticate AI workloads but still lose control over what the model consumes, returns, and stores. For identity teams, the relevant question is no longer only who can call the service, but what the service can do once it has access.

That shift matters across NHI and emerging agentic AI governance because the gateway becomes part of the control boundary for workload identities, model credentials, and downstream data exposure. A modern AI stack needs to separate transport control from inference control, otherwise cost, security, and policy drift accumulate in the same place. Readers who are building AI controls should also review the NHI Lifecycle Management Guide for how access, rotation, and offboarding apply to non-human identities in practice.

Key questions

Q: How should security teams govern AI workloads that use both API and AI gateways?

A: Treat the API gateway as the transport control and the AI gateway as the inference control. The security team should define which policies belong at each layer, then measure token use, streaming behaviour, and content inspection separately. That division reduces blind spots and prevents teams from assuming classic API controls are enough for model traffic.

Q: Why do traditional API gateways fall short for LLM and agentic AI traffic?

A: They were built for request-response traffic, not for token streams, semantic reuse, or content-aware policy enforcement. In AI workloads, the important risks appear after authentication, when the model generates outputs, consumes tokens, or exposes data. Standard gateways can pass the request but still leave governance gaps in cost, safety, and accountability.

Q: What do security teams get wrong about AI gateway security?

A: They often focus on model access and ignore the governance of the data and outputs moving through the gateway. If the platform cannot inspect prompts, detect sensitive content, and attribute token consumption to identities, it is providing transport security rather than AI governance. That leaves the most material risk untouched.

Q: Who should own policy enforcement for AI inference workloads?

A: Ownership should sit with the team accountable for AI runtime governance, not with network routing alone. That owner needs authority over model selection, budget controls, content inspection, and auditability. Without a named owner, AI traffic tends to fragment across platform, security, and application teams, which creates gaps in enforcement and review.

Technical breakdown

Why API gateways break on token streams and LLM traffic

Traditional API gateways are designed around discrete HTTP requests and responses. That model works for microservices, but it does not map cleanly to LLM inference, where responses may stream token by token over SSE or WebSockets and where a single interaction can consume thousands of tokens. The gateway can still pass traffic, but it cannot natively understand cost, semantic reuse, or the risk embedded in model output. In other words, the transport layer remains visible while the inference layer stays opaque.

Practical implication: separate traffic routing from inference governance so token usage, streaming, and content inspection are controlled where they occur.

Token-aware routing and semantic caching in AI gateways

AI gateways add model selection, token accounting, and semantic caching to the front door. Instead of routing only by path or header, they can choose models based on cost, latency, or policy and then measure consumption by user, team, or application. Semantic caching matters because AI prompts are often similar in meaning but not identical in text, so exact-match cache logic leaves savings on the table. This creates a different governance model from conventional API management because spend, performance, and access policy now intersect.

Practical implication: use token-level telemetry and semantic caching controls to prevent uncontrolled spend and to build chargeback or budget enforcement.

Content-aware security for prompt injection and PII exposure

AI gateways extend security beyond authentication and rate limiting by inspecting prompts and outputs for harmful patterns. That includes prompt injection, leakage of personal data, and policy violations that are invisible to a standard gateway because they live in content, not transport metadata. The issue is not just blocking bad input. It is preserving policy as data moves through retrieval, inference, and response generation. For regulated environments, this is where governance becomes continuous rather than point-in-time.

Practical implication: add content-aware controls and audit trails so security teams can detect and review unsafe prompts and outputs across the AI request path.

Threat narrative

Attacker objective: The attacker aims to use legitimate AI access to extract sensitive data, manipulate model behaviour, or generate disproportionate cost and operational disruption.

1. Entry occurs when an AI workload is granted ordinary API access, but the gateway only authenticates the request and does not understand the semantic risk of the prompt or response.
2. Escalation follows when token-heavy or maliciously crafted prompts drive model behaviour, content leakage, or unbounded cost because the gateway lacks inference-native controls.
3. Impact is exposed in the form of PII leakage, policy bypass, and runaway spend, which together turn the gateway into a blind spot rather than a control point.

Breaches seen in the wild

• Salesloft OAuth token breach — hackers stole OAuth tokens to access Salesforce data via Salesloft.
• Internet Archive breach — unsecured GitLab authentication tokens exposed 31M Internet Archive accounts.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

API gateway thinking stops at transport, but AI governance begins at inference. Traditional gateways are good at authenticating calls and shaping throughput, yet they do not understand token economics, semantic reuse, or the content risks that emerge after the request is accepted. That gap is why AI infrastructure needs a separate control plane for inference decisions. Practitioners should treat gateway design as a governance boundary, not just an integration pattern.

Token-level visibility is now a governance requirement, not a nice-to-have metric. Once an AI workload can spend by the token rather than by the request, cost control becomes an identity and policy issue as much as a FinOps issue. The named concept here is inference governance gap: the mismatch between what a traditional gateway can observe and what AI systems actually consume. Organisations that cannot attribute token use by identity, team, or model will struggle to enforce accountability.

Content-aware controls matter because AI abuse is often semantic, not syntactic. Prompt injection, indirect data leakage, and policy bypass do not look like classic API misuse, so transport-layer rules alone miss the failure mode. That means security teams must evaluate where inspection happens, what it can understand, and whether the control point is close enough to the model to be meaningful. The practitioner conclusion is simple: if the gateway cannot inspect meaning, it cannot govern the risk.

Specialised AI infrastructure is becoming part of the identity stack. As more applications orchestrate models, tools, and data sources, the control boundary shifts from human-facing application access to machine-facing inference access. That does not replace IAM, PAM, or NHI governance; it extends them into AI runtime behaviour. Teams should expect model routing, budget enforcement, and output policy to sit alongside existing identity controls rather than outside them.

Platform convergence will push identity teams to re-evaluate ownership. The market is moving toward shared control planes that cover APIs, AI inference, and observability together, but governance responsibility still has to be assigned somewhere. If identity teams do not define who owns model access, token budgets, and content policy enforcement, those responsibilities will fragment across platform, security, and application teams. The practical conclusion is that AI gateway governance needs a named owner before AI traffic scales.

From our research:

• When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes and as quickly as 9 minutes in some cases, according to LLMjacking: How Attackers Hijack AI Using Compromised NHIs.
• DeepSeek accidentally embedded over 11,000 secrets in its training data and left a database exposed online, revealing more than one million sensitive records including chat histories, backend credentials, and API keys.
• Forward pivot: For a broader view of the governance problem, see AI LLM hijack breach for how stolen access keys can turn AI infrastructure into an execution target.

What this signals

Inference governance is becoming the next identity boundary. As AI systems move from isolated experiments to shared infrastructure, teams need to decide where model access, token policy, and output inspection live in the control stack. The practical risk is that organisations will keep treating AI traffic like ordinary API traffic even after the workload has outgrown transport-only controls. For teams designing programme scope, the right reference point is the OWASP Agentic Applications Top 10, which frames how agentic systems shift the attack surface.

With 80% of organisations reporting AI agents acting beyond intended scope, the governance gap is already operational. That figure matters because it shows the problem is not speculative, it is happening in production-like environments today. The next step for practitioners is to align AI gateway policy, workload identity, and auditability before usage scales further.

AI gateway adoption will force a split between transport control and inference control. The organisations that prepare now will treat token telemetry, semantic inspection, and model routing as part of security architecture, not optional observability. That is the same pattern identity teams learned with NHI governance: access without lifecycle visibility is only partial control. The NHI Lifecycle Management Guide remains the clearest reference for lifecycle discipline when AI services rely on non-human credentials.

For practitioners

• Define the control boundary for AI inference Map where API routing ends and inference governance begins, then assign ownership for model access, token policy, and output inspection to a named team.
• Instrument token usage by identity and workload Track tokens consumed by user, service account, application, and model so budget enforcement and abuse detection can operate at the right granularity.
• Test streaming and content controls separately Validate SSE and WebSocket handling, then run prompt injection and PII leakage tests to confirm the gateway can inspect meaning, not just transport.
• Review non-human identity lifecycle around AI services Confirm that service credentials, API keys, and model access tokens have clear provisioning, rotation, and offboarding rules tied to the AI workload lifecycle.

Key takeaways

• AI gateways exist because conventional API gateways cannot govern token streams, content risk, or model-level cost in the way AI workloads require.
• The market data shows rapid expansion in AI infrastructure, but the security burden is shifting faster than many identity programmes are adjusting.
• Practitioners should split transport security from inference governance and assign clear ownership for model access, token policy, and content inspection.

Key terms

• AI Gateway: An AI gateway is a control layer built for model traffic rather than ordinary API calls. It manages inference routing, token accounting, streaming, and content inspection so organisations can govern what the model consumes and produces, not just who is allowed to call it.
• Semantic Caching: Semantic caching reuses model responses based on meaning, not exact text. It reduces redundant inference cost and latency, but it also changes governance because cache hits must be understood in the context of prompts, models, and policy boundaries rather than just URLs or request headers.
• Token Telemetry: Token telemetry is the measurement of how many tokens an AI system consumes, by whom, and for what workload. In practice it gives security, platform, and finance teams the data they need to enforce budgets, detect abuse, and attribute AI usage to the right identity.
• Inference Governance: Inference governance is the set of controls applied while an AI system is generating output. It covers model selection, content inspection, auditability, and policy enforcement, which means it operates closer to runtime behaviour than traditional API governance does.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an identity security programme, it is worth exploring.

This post draws on content published by Kong: API Gateway vs. AI Gateway: The Definitive Guide to Modern AI Infrastructure. Read the original.