Kong vs Apigee vs MuleSoft vs Azure API Management: An Honest Comparison
These four come up in almost every API management shortlist. All of them can run an enterprise API estate. They differ in what problem they were built to solve, where they will run, how they count what you use, and how far they have gone into AI and agent traffic. Here is the honest comparison.
The deciding question is whether your problem is integration or control of traffic. MuleSoft is an integration platform with API management attached. Apigee is Google's full lifecycle API platform. Azure API Management is the natural choice for an estate centred on Azure. Kong is a gateway first platform that governs API, AI model, MCP and agent traffic in one runtime, and that runtime can run almost anywhere, including fully under your own control. Plenty of organisations sensibly run two.
Comparisons like this get distorted by whoever writes them, so here is the frame. We are not comparing feature checklists, because all four tick most boxes. We are comparing the things that decide whether a platform fits: what it was built for, where it runs, how it is priced, how it handles AI traffic, and how hard it is to leave.
How they compare
| Kong | Apigee | MuleSoft | Azure API Management | |
|---|---|---|---|---|
| Built for | API gateway first, open source core | Full lifecycle API management | Integration, with API management | API management native to Azure |
| Where it runs | SaaS control plane with gateways in your data centres or clouds, or fully self managed | Google Cloud managed, or a hybrid runtime on your own Kubernetes | MuleSoft managed or customer hosted runtimes | Azure, with a self hosted gateway on selected tiers |
| How it is priced | Published self service plan; Enterprise quoted | Subscription tiers by quote, or pay as you go | Usage packages on flows, messages and throughput for new customers; vCores on older contracts | Eight tiers, billed per unit or per call |
| AI and MCP | AI gateway, MCP tool control and agent traffic in one runtime | LLM token limits and quotas, semantic caching, Model Armor screening; MCP generally available on hybrid | AI policies on Omni Gateway: LLM token limits, PII detection, prompt guards, MCP tool access control | AI gateway on all tiers, including MCP servers |
| Strength | Runs anywhere, with API, AI and MCP traffic in one gateway | Maturity, analytics and monetisation at scale | Connectors and integration depth, Salesforce alignment | Azure integration and a low entry point |
| Trade off | Not an integration platform; single sign on, role based access and audit need Enterprise, which is quoted | Pulls you towards Google Cloud | Expensive if you only need a gateway | Networking and multi region push you up the tiers |
The honest read on each
Kong
Kong started as a fast, open source API gateway and has grown into a platform it now calls AI connectivity. Its control plane runs as a service while the gateways that carry your traffic run wherever you need them, in your own data centres, any cloud or Kong's managed infrastructure, and a fully self managed edition exists for organisations that need everything in house. It treats AI as gateway traffic, with model routing, token limits, semantic caching, data redaction and MCP tool control in the same runtime as ordinary APIs, and that runtime can sit anywhere. It is not an integration platform: it will not transform and orchestrate data between business systems the way MuleSoft does. The self service plan is priced publicly, but single sign on, role based access and audit logging sit in the Enterprise edition, which is quoted, so the published prices are not a guide to an enterprise bill. Kong AI Gateway documentation; Kong Konnect pricing.
Apigee
Apigee is one of the most established full lifecycle API platforms, strong on analytics, developer portals, security and monetising APIs as products. It runs as a Google Cloud managed service, with a hybrid option that puts the runtime on your own Kubernetes clusters. Its centre of gravity is Google Cloud, which suits organisations already there and adds friction for those that are not. Google has also added AI gateway policies: LLM token limits and quotas, semantic caching, and Model Armor for screening prompts and responses. MCP support reached general availability on Apigee hybrid in September 2026. Apigee release notes. Pricing is by subscription tier or pay as you go, with call volumes and environments shaping the bill. Apigee pricing.
MuleSoft
MuleSoft is an integration platform first. Its strength is connecting business systems, with a large library of connectors and deep alignment with Salesforce. API management comes as part of that platform. For organisations whose main problem is integration, that is a good fit. For those who mostly need a gateway in front of existing services, they are paying for an integration platform to do a gateway's job. MuleSoft has also built AI policies into its Omni Gateway, formerly Flex Gateway, including LLM token limits, PII detection, prompt guards and rules over which MCP tools are exposed. Its MCP support policy covers specification versions up to 2025-06-18, so check it against the agents and clients you plan to use. MuleSoft, Omni Gateway agent policies; MuleSoft, MCP support policy. New customers buy usage packages measured on flows, messages and data throughput, while older contracts were based on vCores. MuleSoft, Anypoint Platform pricing.
Azure API Management
For estates built on Azure, Azure API Management is the obvious default. It integrates with Azure identity, monitoring and networking, and Microsoft has built a substantial AI gateway into it, covering token limits, semantic caching, load balancing across model deployments, content safety, and exposing and securing MCP servers. Microsoft says the AI gateway applies to all tiers. The catch is the tier structure: virtual network injection, multi region deployment and the self hosted gateway are only available on particular tiers, which is where costs step up. Microsoft, AI gateway capabilities in Azure API Management; Azure API Management pricing.
Is your problem moving and transforming data between systems, or controlling traffic to services and models? If it is integration, MuleSoft or another integration platform leads, with a gateway alongside. If it is control, choose the gateway on where it must run and how far into AI you need it to go: Azure API Management if you are committed to Azure, Apigee if you are committed to Google Cloud and value its lifecycle tooling, Kong if you want one gateway for API, AI and MCP traffic that runs anywhere, including fully under your own control, rather than inside one cloud.
Running two is normal
Many large organisations run an integration platform and a separate gateway, and that is often the right answer rather than a failure of standardisation. The integration platform handles orchestration between business systems. The gateway handles security, rate limits, AI traffic and the developer portal for everything else. Separating them can also reduce how much traffic flows through the more expensive platform. What matters is that each has a clear job and an owner.
How they are bought, and what to weigh
The metric decides the bill, and each platform counts something different. Kong's self service plan charges by control plane, requests and models behind the AI gateway, with Enterprise quoted. MuleSoft's usage packages count flows at the highest number running in any single hour of the month, plus messages and throughput. MuleSoft, usage based pricing: usage and rates. Azure prices by tier and unit, with networking requirements often deciding the tier. Apigee prices by tier or by calls and environments. Before you compare quotes, model each against the same picture of your estate: number of APIs, monthly calls, environments, regions and the AI traffic you expect agents to add. A platform that looks cheaper on today's traffic can look very different once agents start calling APIs at machine speed.
Our position, stated plainly
We design and deliver API and AI gateway platforms, and we advise on which one fits before anyone signs. Where we have a commercial relationship with a vendor, we say so on the page. The recommendation should follow your estate, your cloud commitments and where your AI plans are heading, and sometimes the honest answer is to keep what you have and negotiate it better.
Choosing or rethinking an API platform?
Tell us what you run today, where it runs and where AI fits in your plans. We will tell you which platform fits, or whether the one you have is fine and just needs better terms. We design and deliver the platform too.
Prefer email? Reach us directly at hello@c4cgroup.co.uk.
Frequently asked questions
Is Kong better than Apigee?
Neither is better in general. Kong is stronger where you want the option of running the whole platform yourselves, control plane included, or one gateway spread across several clouds and data centres. Apigee is stronger for organisations committed to Google Cloud that value its analytics, developer portal and API monetisation. The right choice follows where you run and what you need the platform to do.
Can Kong replace MuleSoft?
For gateway and API management work, often yes. For integration work, such as orchestrating and transforming data between business systems, no, because Kong is not an integration platform. Many organisations keep MuleSoft for integration and move gateway traffic to a separate gateway, which can also reduce the traffic counted against MuleSoft.
Is Azure API Management good enough for enterprise use?
Yes, for estates centred on Azure, and Microsoft has built a substantial AI gateway into it. Check the tier carefully: virtual network injection, multi region deployment and the self hosted gateway are only available on particular tiers, and that choice drives the cost more than the feature list does.
Which API management platform is cheapest?
There is no universal answer, because each platform meters something different: requests, flows and messages, calls and environments, or units and tiers. Model each against the same picture of your estate, including the AI traffic you expect to add, and compare the totals rather than the headline rates.
Should we run more than one API platform?
It is common and often sensible to run an integration platform alongside a separate gateway, each with a clear job and owner. What causes problems is several overlapping gateways with no clear ownership, which multiplies cost and leaves gaps in security policy.