April 22, 2025 API Strategy

API Management: How to Monitor API Usage Across Multiple API Gateways

Enterprise organizations rarely operate with a single API gateway. As business units adopt technologies independently, it is common to find, for example, Kong in one domain, AWS API Gateway in another, and additional platforms elsewhere. This flexibility drives velocity—but it also fragments visibility. Without a unified view of API activity, enterprise teams face inconsistencies in reporting, gaps in customer insights, and difficulty enforcing governance policies.

The consequences compound quickly. When siloed systems trap API usage data, product managers lack the context to make informed roadmap decisions. Security and compliance teams struggle to audit behavior across services. And revenue opportunities tied to API monetization go unnoticed or unmeasured.

Moesif solves this by offering a unified analytics and governance layer that works across multiple gateways, without requiring changes to your infrastructure. In this article, we will demonstrate how to monitor API usage across two gateways—Kong and AWS API Gateway—by tracking a GenAI platform with endpoints split between them. You will understand how Moesif centralizes metrics, enables consistent billing, and helps unlock insights that align product strategy with operational performance.

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Understanding the Problem: API Sprawl and Siloed Data
Why a Unified Pane of Glass is Non-Negotiable
Moesif for Multi-Gateway API Monitoring
Example: Tracking Usage from Kong and AWS API Gateway
Example: Monetization API Usage Across Two API Gateways
- Tracking Token Usage
- Create the Billing Meter
Decoupling API Infrastructure from API Analytics
Conclusion

Understanding the Problem: API Sprawl and Siloed Data

As organizations scale, APIs no longer remain centrally managed assets—they’re distributed across teams, regions, and technology stacks. For example, a product-led business unit might deploy Kong for its flexibility and Lua-based plugins. Meanwhile, a cloud-native team might default to AWS API Gateway to stay aligned with their broader serverless architecture. Each decision is rational in isolation. But collectively, they lead to API sprawl—a growing number of APIs you have deployed across gateways, clouds, and teams with little coordination.

This fragmentation creates significant blind spots for stakeholders who need a deeper understanding into how customers are using the APIs. Siloed data—where each gateway stores analytics, logs, and performance data in its own format—means you possess no single source of truth:

Product managers can’t track adoption across services.
Platform teams can’t compare performance baselines.
Security and compliance teams lack consistent audit trails.

Even simple questions like “which customers are generating the most API calls?” become time-consuming to answer.

It gets worse when you have configured each gateway differently. One team might log enriched metadata like customer ID and rate limit tier, while another logs only basic path and status code. This lack of consistency breaks downstream analytics, making it difficult to create unified metrics or enforce cross-team governance. The more APIs you ship without coordination, the harder it becomes to make data-driven decisions that span your organization.

The root issue is both technical and structural. As each product unit makes independent decisions about API design, gateway configuration, and analytics tooling, no standardization exists in how usage data is collected, tagged, or reported. Without a governance layer that spans all APIs, you are left with an operational black box. You can’t manage what you can’t measure—and right now, you can’t measure across silos.

Why a Unified Pane of Glass is Non-Negotiable

Fragmented API environments demand centralized observability. When you have deployed APIs across different gateways, clouds, and teams, there must exist a single interface—a unified pane of glass—to monitor, analyze, and act on usage across all services. Without it, you have to stitch together partial metrics from siloed systems, often manually. This slows down decision-making, introduces data inconsistencies, and prevents organizations from scaling API programs efficiently.

A unified API analytics layer solves three critical problems:

Visibility
Consistency
Coordination

First, it allows teams to monitor API usage regardless of the underlying infrastructure. Whether you serve an endpoint from Kong, AWS API Gateway, or a third-party edge service, all requests and responses are ingested into a single system with normalized fields. That includes metadata like customer ID and version, which becomes essential when segmenting customers by usage tier, product line, or behavior, enforcing rate limits, or tracking monetization.

Second, it ensures consistent metrics across services. Response time, error rates, traffic volume, and retention can’t mean different things across teams. A unified layer aligns definitions and data models across APIs, making dashboards, alerts, and billing logic accurate and trustworthy. Compliance reporting, SLA enforcement, and executive-level performance reviews rely on shared definitions and consistent measurements across all APIs.

Finally, it enables cross-functional alignment. Product teams can prioritize features based on real usage data. Network operations centers (NOCs) can detect anomalies across gateways in real time, regardless of deployment architecture. API providers gain clear insights into what works and what doesn’t, and API consumers benefit from reliable performance backed by data. A single analytics layer becomes the bridge that connects developers, operations, product, and security—without forcing a migration or consolidating gateways.

The goal isn’t to replace your existing API infrastructure. Rather, it decouples observability and governance from the specifics of your runtime environment. That flexibility is what makes the unified pane of glass not just a convenience—but a requirement for scaling modern API ecosystems.

Moesif for Multi-Gateway API Monitoring

When organizations manage APIs across multiple gateways—AWS API Gateway, Kong, Azure APIM, NGINX, and more—the complexity of tracking API usage grows exponentially. Each gateway surfaces different metrics, exposes different logging mechanisms, and structures its data differently. Moesif addresses this by acting as a centralized API analytics and observability layer, purpose-built for cross-gateway environments, where traditional logging and monitoring tools fall short.

At its core, Moesif aggregates API traffic across gateways and standardizes the data into a unified format. This enables product teams to compare usage, error rates, and performance across services, even when those services live in completely different runtime environments. This aggregation happens in real time without requiring API developers to rewrite their APIs or migrate infrastructure. Moesif uses native gateway plugins and integrations to ingest data with minimal configuration and no downtime.

One of the factors that distinguishes Moesif from gateway-native monitoring comes from its user-centric model. Instead of treating API calls as infrastructure-level events, Moesif attributes them to specific users, customers, or organizations—linking behavioral analytics with operational data. This lets teams perform cohort analysis, measure retention, and identify which customers are most affected by slow endpoints or frequent errors. It moves monitoring out of the realm of DevOps and into the toolkit of product management and business strategy.

Additionally, Moesif supports high-cardinality, high-dimensional analysis, making it suitable for environments with complex usage patterns. You can break down traffic by customer plan, region, API version, or custom metadata fields like llm_model or subscription_type. If you manage APIs as commercial products, where billing meters, quota enforcement, and usage-based pricing depend on accurate, granular metrics, you will find this flexibility particularly valuable.

For teams that need to move fast, Moesif’s out-of-the-box integrations with platforms like AWS and Kong make setup straightforward. You can stream AWS API Gateway logs through Kinesis Firehose, while the Kong plugin captures and enriches traffic at the gateway level before forwarding it to Moesif. These integrations avoid the need for sidecars, custom logging agents, or backend rewrites, allowing organizations to decouple their API management from API analytics without friction.

Example: Tracking Usage from Kong and AWS API Gateway

Let’s walk through how an organization can monitor and analyze API usage across two different product groups, each with different API gateways —Kong Konnect and AWS API Gateway—using Moesif. Because Moesif is decoupled from the API runtime, Moesif ties them together into a single, actionable analytics layer.

Architecture Overview

Kong Konnect serves an embeddings API at v1/embeddings/generate.
AWS API Gateway serves a text generation API at v1/text/generate.

Why This Setup Matters for Product Teams

Most analytics platforms tied to a single gateway can’t answer questions like the following:

How much are our customers using our GenAI platform across products?
Which API is nearing quota first—and why?
Which customer segment is driving error spikes?

Moesif makes this possible by aggregating and normalizing traffic across gateways. Regardless of whether a call goes through AWS or Kong, usage data flows into Moesif thanks to a shared application ID, where Moesif tags, filters, and transforms the usage data into metrics.

What You Can Track

Once you complete the integration, you can access valuable information about your product’s usage and the customers. For example:

API calls by endpoint (you can split by API Gateway and Kong if you want)
Customer-specific usage, based on user and company identification methods. In Kong Konnect, you can pass them using headers or authorization tokens. In API Gateway, Moesif by default leverages context variables and supports IAM authorization. For more information, see Overview of User Tracking and Enabling Company Tracking .
Quota setup and tracking (how close each user or company is to their usage limit)
Error breakdowns (4xx or 5xx HTTP errors by product or endpoint)
Latency trends (you can segment by gateway, plan, or geography)

You can enrich and visualize all this data in workspaces and organize them in dashboards to suit product, growth, and ops teams—not just backend engineers.

Moesif automatically adds a kong metadata field for Kong Konnect and API and stage details for API Gateway. This allows you to filter by Kong service name, request ID or API key for API Gateway, and so on. Here, for example, we use those metadata fields to filter out API calls in a Live Event Log workspace to observe recent traffic into the two GenAI APIs:

You can view the metadata fields by selecting Metadata filters in the Filters pane or by expanding an API event. The following shows the kong metadata fields for a request to the embeddings API:

For the text generation API in API Gateway, here’s how the metadata might look in Moesif:

For more information about enriching events with metadata in API Gateway, see Event Metadata.

How Moesif Ties It Together

While the Kong Konnect and AWS setups differ under the hood, both stream event data into Moesif. By using the same Application ID for both setups, you make analytics data of both business units (or as many business units you have) under a single app in Moesif.

You can then create custom dashboards to compare APIs, flag performance regressions, and measure the impact of product changes—all without changing the infrastructure.

Example: Monetization API Usage Across Two API Gateways

If you have used the same Application ID for the two REST APIs, you can aggregate usage from both into a single billing meter. This unification makes it possible to apply one pricing model across the product line—without rewriting infrastructure or standardizing log formats across gateways.

Tracking Token Usage

For GenAI platforms, an accurate metric to bill on is token usage. Moesif allows you to easily extract the token value from response headers or metadata fields and define it as a billable metric in the billing meter. For example, the embeddings API response may look like this:

{
  "generated_text": {
    "object": "chat.completion",
    "created": 1744296855,
    "usage": {
      "total_tokens": 39,
      "completion_tokens": 17,
      "prompt_tokens": 22
    },
    "id": "chatcmpl-pWOFA9cwZdRxPe2zGhoTR9BNxcD"
  }
}

And the text generation API response may look like the following:

{
  "embeddings": {
    "usage": {
      "total_tokens": 67,
      "completion_tokens": 19,
      "prompt_tokens": 48
    },
    "choices": [
      {
        "index": 0,
        "finish_reason": "stop",
        "logprobs": null
      }
    ],
    "object": "chat.completion",
    "id": "chatcmpl-QDtgBuwgrpJ2tvHy6A6NkPjxQ2Z",
    "created": 1744712264
  }
}

Create the Billing Meter

Go to your Moesif Web Portal.
Select Billing Meters in the navigation menu and then select Add Billing Meter.
Enter your billing meter name.
Select your billing provider, product, and prices.
In the Filters pane, define your filters to only count events for the two GenAI APIs.
In the Metrics pane, define the metric you want to bill on.
Select Create to finish creating the meter.

For example, here is a billing meter that meters output token usage:

Decoupling API Infrastructure from API Analytics

API gateways excel at routing traffic, enforcing security policies, and managing load—but they are not designed to act as governance or analytics platforms. When observability, billing logic, and policy enforcement depend directly on your gateway configuration, you introduce a tight coupling that limits organizational agility. As APIs proliferate across clouds, gateways, and product teams, this coupling becomes a liability—not just a scaling challenge.

Consider this: a product unit might migrate from Kong to AWS API Gateway for performance or compliance reasons. Many organizations implement governance logic—like rate limit tiers, usage enforcement, or custom billing rules—through ad hoc scripts or internal tooling layered on top of each gateway’s APIs. When infrastructure shifts, that logic doesn’t port cleanly. As a result, you have to rebuild usage tracking pipelines, alert criteria, and billing code from scratch—introducing friction and inconsistency that product teams can’t afford.

Moesif avoids this by treating observability and governance as a layer above your infrastructure. Whether you serve APIs through Kong, AWS, Azure, or NGINX, Moesif centralizes traffic analysis and governance logic in one place. You don’t need to duplicate rules or data pipelines per gateway. This architectural decoupling gives product managers and platform teams the freedom to evolve infrastructure without impacting how you track, analyze, or monetize usage.

It also improves consistency and compliance. You should uniformly apply governance policies like SLAs, audit trails, and API usage limits—regardless of where you host the API. Moesif supports this by using normalized metadata and customer-level enrichment that abstract away gateway-specific quirks. For example, you can enforce a quota across AWS and Kong-served APIs based on customer ID, without worrying about how each gateway logs or forwards data.

Decoupling gives organizations strategic leverage as well. It enables experimentation with new gateway technologies, shifts between cloud regions, or even multi-cloud strategies—without compromising compliance, observability, or revenue tracking. This is what mature API governance looks like: infrastructure-agnostic, data-consistent, and built to adapt.

Conclusion

Managing APIs across multiple gateways doesn’t have to mean sacrificing visibility, billing accuracy, or governance consistency. Moesif allows you to avoid the hassle of standardizing your infrastructure. Your APIs can live behind Kong, AWS API Gateway, or both. Moesif brings them together under a single pane of glass, ready for observability, monetization, and customer insight.

So where should you begin?

Sign up for Moesif today and start by integrating one gateway. Configure accordingly and enrich it with meaningful metadata, and watch how usage patterns emerge. Then layer in your second gateway. Within hours, you will have unified dashboards, billing meters, and alerts that span your entire API footprint—without touching your backend.

The sooner you decouple infrastructure from analytics, the sooner your API strategy stops lagging behind your architecture and starts driving business forward.

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API Analytics , API Monetization , API Monitoring , API Observability

Abu Sakib

Technical Writer at Moesif. Previously @Arcion Labs and @Dgraph Labs.

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