Vercel AI Gateway and Kong AI Gateway both advertise failover, spend tracking, and multi-provider access, so feature grids make them look interchangeable. The choice comes down to what infrastructure your team already operates. Vercel AI Gateway is a fully managed layer for teams that want to run no gateway at all, and Kong AI Gateway extends an API gateway that teams already staff for REST traffic. This guide works through where the two diverge on setup, failover, governance, and pricing, so the right call for your team is clear by the end.
Key takeaways:
Vercel AI Gateway is a fully managed routing layer with no token markup and automatic provider failover, so there is no gateway infrastructure to provision, patch, or scale.
Kong AI Gateway adds AI routing as plugins on Kong Gateway, which fits teams already operating Kong for REST APIs and adds an operational surface for everyone else.
In a week-long production A/B test against its previous router, Cline cut API error rates by 43.8% and improved P99 streaming latency by 10 to 14% on Vercel AI Gateway.
Vercel AI Gateway passes provider list prices with no platform fee, while Kong Konnect Plus meters models and requests beyond an included tier, so neither is cheaper in every case.
Over 90% of the gateway's first production month was spent waiting on AI providers rather than computing, so LLM infrastructure is an I/O problem before a compute problem.
Copy link to headingWhere Vercel AI Gateway and Kong AI Gateway differ
The sharpest difference sits underneath both products. Vercel AI Gateway was designed around large language model (LLM) traffic from the start, including long-tailed latency, streaming responses, provider failover, and token-level cost tracking. Kong AI Gateway is a set of plugins layered onto an API proxy built for synchronous HTTP. That single architectural split explains most of what follows, from how fast a team reaches a first response to who owns the nodes when traffic grows.
Copy link to headingWhat this comparison is based on
This comparison draws on published production data and each vendor's documentation. Vendor docs describe intended behavior, so where they diverge from observable production numbers, the production numbers take precedence.
Named Vercel AI Gateway customer results with metrics are public, while no published Kong AI Gateway case study names a customer alongside measured outcomes. Independent third-party benchmarks are labeled as directional where they appear.
The differences that matter in production fall across a handful of dimensions. Each row is an operational tradeoff rather than a checkbox:
Each row has a different shape than a single cell suggests, so the dimensions that decide most migrations are worth walking through one at a time.
Copy link to headingSetup path to a first streaming response
The two quickstarts assume different operators, and the gap shows up before the first token streams. A first streaming response through Vercel AI Gateway takes five steps:
Create a project directory and initialize Node.js.
Install the AI SDK and its development dependencies with npm.
Generate an API key and set it in .env.local.
Reference a model as a string in code, such as openai/gpt-5.5.
Run the script to stream a response.
On Vercel deployments, VERCEL_OIDC_TOKEN is available automatically. For local development, vercel link and vercel env pull can provision OIDC tokens.
Kong requires three configuration entities at minimum before the first call, after provisioning a Konnect control plane or a self-hosted deployment:
Create a gateway service with a POST to /services.
Create a route.
Add the AI Proxy plugin with provider, model, and key fields.
Self-hosted production adds container-runtime work and Admin API fluency on top. Kong's one clear advantage is that it is a language-agnostic HTTP proxy, so any language routes through it. The low-setup Vercel path assumes TypeScript and the AI SDK, and callers using direct HTTP manage their own key.
Copy link to headingRouting and failover behavior
Failover is where a managed layer and a plugin model diverge the most. Vercel AI Gateway adds sub-20ms routing overhead and selects providers by recent uptime and latency. When a provider degrades, the same request retries on another provider serving the same model, with no code change. Routing rules, now available, let teams rewrite requests from one model to another or deny retired models at the gateway level.
Kong's AI Proxy Advanced configures semantic, lowest-latency, usage-based, and weighted balancing through YAML or the Admin API. That gives fine control over provider selection at the cost of writing and maintaining the configuration.
Copy link to headingGovernance and data controls
Governance is the dimension where Kong leads. Kong's PII sanitizer redacts personally identifiable information (PII), semantic prompt guard blocks intent rather than keywords, and secrets integrate with HashiCorp Vault, Azure Key Vault, and AWS Secrets Manager, though most of these need enterprise licensing.
Vercel AI Gateway offers Zero Data Retention (ZDR) routing, a no-training guarantee, provider allowlists, and Bring Your Own Key (BYOK), but no gateway-layer PII redaction. Teams that need sensitive-data sanitization ahead of the model handle it in application code or reach for Kong's proxy-layer controls.
Copy link to headingPricing and traffic shape
Neither product is cheaper in the abstract, because the two pricing models are shaped for different traffic. Vercel AI Gateway passes provider list rates through with no platform fee, including on BYOK requests, and meters enterprise features separately. Kong Konnect Plus includes five models and 1 million requests per month, then bills per additional model proxied and $200 per additional 1 million requests, with enterprise AI plugins as add-ons.
Self-hosting does not escape the math, since production Kong infrastructure carries server, database, cache, and monitoring costs before engineering time. The crossover depends on model count and request volume, so price both against your own traffic.
Copy link to headingVercel AI Gateway is a fully managed LLM routing layer
Vercel AI Gateway is a fully managed routing layer that reaches more than 20 providers through one endpoint. It runs on Fluid compute across multiple AWS regions with Anycast routing. Applications authenticate with OpenID Connect tokens and never see the underlying provider keys, and the gateway injects credentials at routing time and retries failed requests on other providers.
It also tracks usage and spend per model and project with no markup on tokens. The AI SDK defaults to the gateway when a model is named as a plain string, such as openai/gpt-5.5, with no provider imports. The gateway is generally available on all Vercel plans and has handled tens of trillions of tokens across hundreds of models over seven months of production traffic.
Copy link to headingPros and cons of Vercel AI Gateway
Vercel AI Gateway's strongest argument is low operational overhead, since there is no gateway to run. The same managed model creates a few real limits.
Vercel AI Gateway pros:
No infrastructure to operate: The routing layer is fully managed on Fluid compute, so teams provision, patch, and scale nothing to run it.
No token markup: Provider list prices pass through with no platform fee, including on Bring Your Own Key requests.
Automatic provider failover: A degraded provider triggers a retry on another provider serving the same model, with no application code change.
One TypeScript integration: The AI SDK routes to the gateway from a plain model string, which collapses multiple provider SDKs into one configuration.
Vercel AI Gateway cons:
No self-hosting or data-plane control: The gateway is managed only, so teams that require self-hosted deployment or control over the data plane cannot run it in their own infrastructure.
No gateway-layer PII redaction: Sensitive-data sanitization has to live in application code or another layer.
TypeScript-first ergonomics: Any language can call the HTTP endpoint, but the lowest-setup path assumes the AI SDK, and non-Vercel callers manage their own key.
Best for: Vercel AI Gateway fits teams building AI features on Next.js or the AI SDK that want zero gateway overhead and automatic failover without assembling plugins. It suits fast-moving product teams more than teams that require self-hosted deployment or gateway-layer data controls.
Pricing: Inference is billed at provider list prices with no platform fee, and the free tier includes $5 of monthly credit. Enterprise controls such as team-wide ZDR and provider allowlists are metered separately per 1,000 requests, and Custom Reporting is metered per write and per query.
Copy link to headingWhere Vercel AI Gateway stands out
Vercel AI Gateway pulls ahead on time to a working integration and on failover that needs no code. A team reaches a first streaming response in five steps, switches models with a one-string change, and lets the gateway reroute around a provider outage without a deploy. In a week-long A/B test Cline cut API error rates by 43.8% and improved P99 streaming latency by 10 to 14%, and Zo Computer saw P99 latency fall from 131 seconds to 81 seconds after migrating.
Copy link to headingKong AI Gateway is AI routing built on Kong Gateway
Kong AI Gateway is a set of AI plugins layered onto Kong Gateway, the open-source API gateway built on Nginx and Lua. The plugin set includes AI Proxy, AI Proxy Advanced, AI Semantic Cache, and AI Rate Limiting Advanced. It runs self-hosted, including on Kubernetes, or through the Konnect SaaS control plane with customer-operated data planes.
The initial six AI plugins shipped in Kong Gateway 3.6 in February 2024 and are open source. Enterprise features such as the PII sanitizer, semantic prompt guard, and AI Rate Limiting Advanced require paid licensing. Routing configuration runs through the Admin API and decK YAML as primary paths, with Terraform supported.
Copy link to headingPros and cons of Kong AI Gateway
Kong AI Gateway makes the most sense as an extension of infrastructure a team already runs. Its strengths and its costs both follow from being a plugin layer on an API proxy.
Kong AI Gateway pros:
Extends existing Kong: Teams already operating Kong for REST traffic add AI routing with no new infrastructure to staff.
Language-agnostic proxy: Any language or framework routes through the HTTP proxy, which fits polyglot backends.
Deep gateway-layer governance: The PII sanitizer, semantic prompt guard, and vault integrations enforce policy at the proxy, ahead of the model.
Self-hosting with data-plane control: Kubernetes and multi-cloud deployments keep data planes under the team's control.
Kong AI Gateway cons:
Operational overhead without existing Kong: Standing up and running the proxy for AI routing alone is a heavy addition for teams that do not already run Kong.
Enterprise licensing for key features: Semantic cache, the PII sanitizer, and AI Rate Limiting Advanced sit behind paid licensing, so the community edition lacks the differentiating controls.
Team-owned observability: Self-hosted teams build and maintain their own OpenTelemetry or Prometheus and Grafana pipeline.
I/O wait becomes the team's problem: A self-operated proxy hands the cost of waiting on providers to whoever runs the nodes.
Best for: Kong AI Gateway fits teams already operating Kong for API traffic, polyglot backends, and Kubernetes or multi-cloud deployments that need data-plane control. It also fits compliance programs that require PII redaction at the gateway layer.
Pricing: Kong Gateway open source is free to self-host. Kong Konnect Plus includes five models and 1 million requests per month, then meters additional models and requests, with enterprise AI plugins as add-ons. Self-hosted production adds server, database, cache, and monitoring costs, so a total cost of ownership calculation rarely favors self-hosting at production scale on price alone.
Copy link to headingWhere Kong AI Gateway stands out
Kong stands out on gateway-layer governance and protocol reach. Its balancing policies, PII sanitization, and semantic prompt guard enforce control ahead of the model, and MCP Tool ACLs, added in Kong AI Gateway 3.13, give per-tool access control for agentic Model Context Protocol (MCP) workloads. For a team already fluent in Kong, these controls are additive rather than a new system to learn.
Copy link to headingWhen to choose Vercel AI Gateway or Kong AI Gateway
The decision comes down to what infrastructure a team already operates and which failure mode it would rather own. Two cases cover most teams.
Copy link to headingChoose Vercel AI Gateway when your team runs no gateway
Vercel AI Gateway is the stronger call when a team builds on Next.js or the AI SDK and wants zero gateway overhead. Automatic failover and routing rules cover model policy without assembling plugins, and there is no proxy to provision or scale. The cost accepted in exchange is no self-hosting and no gateway-layer PII redaction, so this fits product teams more than compliance programs that need proxy-level data controls.
Copy link to headingChoose Kong AI Gateway when Kong already carries your REST traffic
Kong AI Gateway is the stronger call when a team already operates Kong for REST traffic, because the AI plugins add no new infrastructure. It also fits polyglot backends, Kubernetes or multi-cloud deployments with data-plane control, and compliance programs that need PII redaction at the gateway. The cost accepted is operational, since someone owns the nodes, the licensing, and the observability pipeline.
Copy link to headingHow Vercel AI Gateway solves LLM routing at scale
Teams building AI features hit the same operational patterns regardless of which gateway they pick. Four show up in production for almost every team, and each has a specific answer on Vercel AI Gateway.
Copy link to headingProvider outages break AI features
The first failure teams hit is a provider outage taking a feature down with it. A single-provider integration has no path around a degraded endpoint, so errors surface as broken generations and retries for the user. Vercel AI Gateway retries the same request on another provider serving the same model with no code change, and routing rules let a team reroute off a retired or failing model by pushing one rule instead of shipping a deploy. That automatic retry is what produced the error-rate reduction Cline recorded after moving to the gateway.
Copy link to headingSeparate provider SDKs fragment spend and credentials
The pain that grows with model count is fragmentation. Every provider added means another SDK, another key to rotate, and another billing dashboard to reconcile. Vercel AI Gateway exposes one endpoint across more than 20 providers, authenticates with OpenID Connect tokens so applications never hold provider keys, and tracks spend per model and project in a built-in dashboard. Okara collapsed eight provider SDKs into one configuration and now routes billions of tokens daily with a team of four.
Switching providers is a single string in the AI SDK, with no imports to change:
import { streamText } from 'ai';
const result = streamText({ model: 'openai/gpt-5.5', prompt: 'Summarize this changelog.',});Changing openai/gpt-5.5 to another provider's model string reroutes traffic without touching the rest of the application.
Copy link to headingCompute billing runs while the gateway waits on providers
The cost that surprises teams at scale is paying compute rates for time spent waiting. LLM calls are I/O-bound, so a gateway spends most of each request idle while a provider generates tokens. In its first month, Vercel AI Gateway logged roughly 16,000 runtime hours, of which only about 1,200 involved CPU work and the remaining 14,800 were spent waiting on providers. Fluid compute with Active CPU pricing bills CPU only while code runs, so that waiting time is charged at a lower memory rate instead of full compute. A self-operated proxy on a traditional serverless pays for the wait.
Copy link to headingSelf-operated gateways add an infrastructure surface
The last pattern compounds. Every provider and every node is something to run, and a self-hosted proxy hands scaling, patching, and the I/O-wait cost to the team that owns the nodes. Model diversity keeps rising, too. The production index shows Anthropic taking 61% of gateway spend on 32% of tokens while open-weight models reached 29% of volume, so the provider set is widening rather than settling. A managed layer absorbs each new provider as configuration, while a self-operated proxy turns each one into more infrastructure to maintain.
Copy link to headingShip AI features without owning gateway infrastructure
The Vercel AI Gateway versus Kong AI Gateway decision is about which cost a team would rather carry. Kong extends infrastructure a team already staffs and hands back operational control along with the burden of running it. Vercel AI Gateway absorbs the routing layer entirely, trading self-hosting and plugin flexibility for zero maintenance and failover that needs no code. For teams shipping AI features rather than operating gateways, the I/O-bound, multi-provider reality of LLM traffic is the problem to offload.
For teams that pick Vercel AI Gateway, the operational concerns of LLM routing land on managed primitives. Each one removes work a self-operated proxy would hand back:
Fully managed Fluid compute: Routing runs across multiple AWS regions with Anycast and sub-20ms overhead, with nothing to provision, patch, or scale.
No token markup: Provider list prices pass through with no platform fee, including on Bring Your Own Key requests.
Automatic failover and routing rules: A degraded provider retries on another serving the same model, and one rule reroutes off a retired model with no deploy.
Unified spend and observability: A built-in dashboard and Custom Reporting API track requests, tokens, time to first token, and spend per model and project.
ZDR routing, allowlists, and BYOK: Team-wide Zero Data Retention, provider allowlists, and Bring Your Own Key enforce data policy on every request.
Start a new project and make a first call with a single model string, or browse the templates for a setup that already routes through the gateway.
Copy link to headingFAQs about Vercel AI Gateway vs Kong AI Gateway
Copy link to headingDoes Vercel AI Gateway work outside Next.js or Vercel?
Yes. Any HTTP client can reach the direct endpoint at https://ai-gateway.vercel.sh/v1/, which supports the OpenAI Chat Completions, Anthropic Messages, and OpenResponses formats. Automatic OpenID Connect authentication is limited to Vercel deployments, so callers elsewhere authenticate with an API key.
Copy link to headingDoes Vercel AI Gateway mark up tokens?
No. Provider list prices pass through with no platform fee on inference, including on Bring Your Own Key requests. Enterprise controls such as team-wide Zero Data Retention and provider allowlists are metered separately, but token pricing matches the provider directly.
Copy link to headingCan Kong's AI plugins run on open-source Kong without a license?
Partly. The initial six plugins from Kong Gateway 3.6, including AI Proxy and AI Prompt Guard, are open source. Semantic cache, the PII sanitizer, semantic prompt guard, and AI Rate Limiting Advanced require a paid license.
Copy link to headingWhat happens if a provider goes down on Vercel AI Gateway?
The gateway automatically retries the request on another provider that serves the same model, with no application code change. A failed Bring Your Own Key request falls back to system credentials so the request still completes.