We Took Developers Out of the Portal: How APIOps and IaC Reshaped Our API Strategy

Introduction

Enterprise API strategies have long relied on centralized API management portals, tools that allow internal developers to manually configure, publish, and manage APIs. While these portals provide visibility and control, they often become operational bottlenecks. As the number of APIs scales, so do the challenges: inconsistent configurations, manual deployment errors, delayed releases, and audit gaps.

We reached a point where our internal API lifecycle heavily reliant on such a portal – became unsustainable. Developers had to log into the portal to publish new APIs, manually apply policies, and configure routing. This not only introduced friction but also created significant risks around environment drift and compliance.

To address these issues, we made a radical change: we took developers out of the portal. Instead of using the UI to manage APIs, we adopted APIOps practices combined with Infrastructure as Code (IaC) to automate and streamline the entire API lifecycle.

Infrastructure as Code (IaC)

Now, every API is defined, versioned, and deployed entirely through automation. Developers interact only with code, not portals.This article outlines how we implemented this transformation, the lessons learned, and the key architectural and operational shifts that enabled us to scale safely and efficiently.

Figure 1: API Strategy Shift with APIOps

The Problem with Manual API Management Portals

Our initial model involved a centralized API gateway platform managed through a web-based portal. Developers would log in to configure proxies, define routes, attach policies (e.g., authentication, throttling), and promote APIs across environments. On the surface, it appeared to offer flexibility and control.

But over time, several pain points emerged:

• Inconsistent configurations led to different teams following their own conventions when setting up APIs, leading, in turn, to divergent policies, naming schemes, and access controls.
• No version control or auditability meant that there was no easy way to track who made which change, or when, because most of the configuration was done through the portal’s UI.
• Manual error-prone deployments were caused by promoting APIs from one environment to another required step-by-step portal operations. Minor human errors often caused outages or misbehavior in production.
• Delayed releases and onboarding friction, were a consequence of APIs requiring manual coordination with platform engineers or operations staff, creating bottlenecks and delays.
• Poor security posture resulted in many developers having access to sensitive configuration panels, enforcing least privilege and governance policies became difficult.

It became clear that if we wanted to scale our API program while maintaining speed, reliability, and compliance, a fundamental shift was required. The manual, UI-driven model introduced inconsistency, operational risk, and friction across teams. To address these challenges, we adopted an APIOps-driven approach, treating APIs as code. By managing OpenAPI definitions, policies, and configurations in Git repositories, we enabled version control, peer review, and automated promotion through CI/CD pipelines. Tools like GitHub Actions and Terraform allowed us to standardize deployments, enforce governance policies, and eliminate manual errors across environments. This new model provided the consistency, traceability, and scalability that the manual process simply couldn’t deliver.

Adopting APIOps and Infrastructure as Code

We looked to DevOps for inspiration.

Just as DevOps removed the friction from software delivery using pipelines, IaC, and version control, we realized we could apply similar principles to APIs.

This led us to adopt APIOps, the practice of managing APIs in a fully automated, pipeline-driven way.

What Is APIOps?

APIOps is the application of DevOps principles to the entire API lifecycle from design and testing to deployment and observability. Instead of relying on UIs or ticket-driven processes, APIs are defined in code and managed through automated pipelines.

How Infrastructure as Code Enables APIOps

We leveraged Infrastructure as Code (IaC) tools like Terraform and Helm to define and manage both our infrastructure components, such as API gateways, DNS records, and TLS certificates, and the lifecycle of our APIs themselves. Using this approach we brought automation, consistency, and scalability into our API delivery workflows.

Why Terraform and Helm?

Terraform: Cloud-Agnostic Infrastructure Provisioning

Terraform is a declarative IaC tool that allows us to provision infrastructure across multiple cloud providers. We used Terraform to automate the setup of Azure API Management (APIM) instances, virtual networks, DNS zones, and TLS certificates. This modular approach reuses configuration logic across environments while maintaining strict version control.

Helm: Streamlined Kubernetes Deployments

Helm is a package manager for Kubernetes that simplifies the deployment of applications and services using charts. We used Helm to deploy API gateway components (such as ingress controllers and proxies), manage microservice dependencies, and coordinate service configurations. Helm helped enforce consistency across clusters and reduced the cognitive load on developers by abstracting Kubernetes manifests.

Alternatives to Terraform and Helm

While Terraform and Helm suited our use case, there are other tools available based on organizational needs and cloud architecture. Terraform alternatives include Pulumi, AWS CloudFormation, Azure ARM Templates, Google Deployment Manager, AWS CDK, Ansible, and Crossplane. Helm alternatives include Kustomize, Jsonnet, Carvel, CDK8s, and Tanka.

Each alternative comes with trade-offs in flexibility, complexity, and community support.

Managing API Policies Through APIOps

Manual Approach (Before)

In the earlier model, API policies such as Rate limiting, OAuth2 authentication, and JWT validation were configured manually using the Azure API Management portal. These policies required developers to navigate the UI, apply policies one by one, and guarantee alignment across environments, which was an error-prone and non-scalable process.

Code-Driven Approach (Now)

With APIOps, policies are defined as code and managed in Git repositories. Changes go through peer-reviewed pull requests and are deployed via CI/CD pipelines. Employing APIOps results in:

• Version control and traceability
• Automated validation before promotion
• Consistency across environments

Rate Limiting Policy example:

xml
<rate-limit-by-key calls="100" renewal-period="60">
  <key value="@(context.Subscription.Key)" />
</rate-limit-by-key>

This policy limits clients to one hundred requests per minute, defined once and applied uniformly via automation.

JWT Validation Policy example:

xml
<validate-jwt header-name="Authorization" require-scheme="Bearer">
  <openid-config url="https://sts.windows.net/{tenant-id}/.well-known/openid-configuration" />
  <audiences>
    <audience>api://example-app</audience>
  </audiences>
  <issuers>
    <issuer>https://sts.windows.net/{tenant-id}/</issuer>
  </issuers>
</validate-jwt>

Only tokens issued by a trusted identity provider are accepted, enforcing authentication at the gateway layer.

All definitions were checked into Git, versioned, and deployed through CI/CD pipelines, just like application code. With infrastructure components and API policies codified using IaC and APIOps principles, we had established a strong technical foundation. But implementing tools alone wasn’t enough. To fully realize the benefits, we needed to rethink how APIs were designed, reviewed, and delivered across teams.

How We Made the Shift: Key Changes

Specification-Driven API Design

The foundation of our APIOps approach was a contract-first model. Every API started as an OpenAPI specification, reviewed and versioned via pull request. This spec became the single source of truth for:

• Request/Response formats defined clearly in the OpenAPI contract to standardize integration.
• Endpoint structure, using URL patterns, HTTP methods, and parameter schemas were documented up front.
• Auth requirements, specifying which endpoints required tokens, scopes, or custom claims.
• Rate limits and quotas, capturing expected request volumes and throttling behaviors.
• Backend mappings

Linting tools in CI ensured specs were valid and consistent before they moved further in the pipeline.

Infrastructure as Code for Gateways and Policies

We created a set of reusable Terraform modules to configure:

• API Gateway Instances using Azure API Management (APIM) instances with regional or environment-specific configurations.
• DNS Records set up with custom domains and subdomains using DNS as code.
• TLS Certificates automatically provisioned and renewed via Key Vault and Azure Front Door.
• Kubernetes Namespaces and Secrets using Helm charts to configure isolated namespaces, service accounts, and configmaps.
• Monitoring and Alerts defining dashboards, alerts, and diagnostic settings (e.g., Azure Monitor, DataDog).

Teams consumed these modules with minimal configuration. Parameters like environment, API version, and auth type were passed in via variables, while all logic and governance rules remained centrally managed. This approach removed the need for developers to know or understand the internal workings of the API gateway platform. They just defined their intent in code.

CI/CD Pipelines for Lifecycle Management

We implemented CI/CD pipelines that handled the entire API lifecycle:

• Validate API specs
• Generate proxy configs from templates
• Publish to API Management (APIM)
• Deploy to non production and production
• Run automated smoke tests

Each step was logged and auditable. We integrated approvals and compliance checks for production deployments. Because all changes were Git-driven, we gained full auditability and traceability – who changed what, when, and why – with the ability to roll back any deployment.

Zero Developer Access to API Gateway Portals

One of the boldest but most impactful changes was revoking developer access to the API management portal. All configuration and deployment actions had to go through Git and pipelines, which drastically reduced the surface area for misconfiguration and made enforcement of platform rules consistent.

Platform engineers retained limited access for incident triage or emergency fixes, but day-to-day management was hands-off.. All configuration and deployment actions had to go through Git and pipelines. This drastically reduced the surface area for misconfiguration and made enforcement of platform rules consistent. Platform engineers retained limited access for incident triage or emergency fixes, but day-to-day management was hands-off.

The impact of this shift was significant. In the next section, we’ll walk through the key results and benefits that validated our APIOps transformation.

Results and Benefits

Improved Security and Governance

Removing direct portal access meant we could enforce least privilege and eliminate one-off changes. Every configuration change went through peer-reviewed code, subjected to static analysis and policy checks. Secrets and keys were handled through vault integrations, not copy-pasted into UI forms.

Consistency Across Environments

Our IaC templates enforced consistent configuration across dev, staging, and production environments. We no longer had environment drift, the same API deployed in staging would behave identically in production.

Faster API Delivery

What once took days, coordinating between devs, platform engineers, and ops, now takes hours. A developer can submit an OpenAPI spec and a few Terraform parameters, and the API is live in staging within a pipeline run.

Reduced Operational Load

Our platform team no longer has to manually onboard APIs, fix UI misconfigurations, or debug environment mismatches. Instead, we focus on improving modules and pipelines, benefiting every team at once.

Full Auditability

All API changes are tracked through Git history. We can trace a production config to the exact commit and author, simplifying audits, incident response, and compliance reporting.

Measuring the Impact

After adopting APIOps with GitHub Actions, we observed measurable improvements across the API delivery pipeline. The shift from manual, portal-based workflows to automated, code-driven processes helped us achieve greater reliability, consistency, and speed. But to validate this transition, we tracked specific metrics before and after implementation: reduction in deployment time and human errors, consistency in deployment, and developer productivity.

Previously, promoting APIs across environments involved several manual steps, exporting definitions, configuring policies, validating routes, and coordinating approvals, often taking four to six hours per release cycle. With CI/CD automation, the full pipeline now completes in under fifteen minutes, including spec validation, policy application via Terraform, and smoke testing.

Manual deployments introduced frequent errors in routing configurations, authentication rules, and rate limiting. By enforcing specification linting, templated infrastructure, and pull request reviews, production issues caused by human errors were reduced by more than eighty percent.

Reusable Terraform modules and Helm charts ensured that environments were provisioned uniformly. This consistency eliminated discrepancies across development, testing, and production environments, enabling predictable and reliable deployments.

Previously, developers needed to rely on platform engineers for deployment support and had to understand the complexities of the API management portal. With APIOps, developers now focus solely on defining API behavior and specifications in code. Internal surveys and delivery metrics indicated a forty percent improvement in developer throughput, resulting in faster onboarding and time-to-market for new APIs.

These improvements demonstrated that APIOps not only enhanced technical performance but also contributed to operational excellence and team efficiency at scale.

These results provided clear evidence that our APIOps adoption was delivering on its promise. However, the journey wasn’t without challenges and key insights. In the next section, we share the lessons learned that helped shape and refine our approach.

Lessons Learned

Templates and Tooling Are Key

For scalability, we created reusable, composable modules for teams to consume. These templates abstracted complexity and let developers focus on API behavior, not gateway mechanics. However, building and maintaining these modules required a significant up-front investment. Platform teams had to align on standards, create detailed documentation, and establish governance patterns for long-term maintainability.

Validation and Feedback Loops Matter

Static analysis tools, like Spectral and OASLint, combined with CI validation, were essential in catching errors early. We integrated mock server tests to verify contract validity before deployment. While these tools improved quality and reduced production issues, it took time for teams to adjust their workflows and fully adopt the shift-left approach to testing and validation.

Start with One Team, Then Scale

We intentionally started the transformation with one internal product team. Their feedback helped us iterate quickly, refine the tooling, and identify usability gaps before scaling to other teams. This gradual rollout built trust and minimized disruption. We could then demonstrate tangible value early on, which helped secure broader adoption across the organization.

Treat APIs Like Products

APIs are not just backend utilities. They require clear ownership, documentation, observability, and lifecycle management. We integrated automated documentation generation and monitoring into our CI/CD pipelines to make every API discoverable and trackable by default. However, the cultural shift from treating APIs as incidental outputs to treating them as products required ongoing advocacy and enablement.

Acknowledging the Trade-offs

While the benefits of APIOps were clear, the transition came with challenges. Many developers initially faced a steep learning curve with infrastructure-as-code tools like Terraform and Helm. Teams accustomed to UI-based configurations needed time and training to become comfortable with Git-driven workflows. Building reusable modules and standardizing practices across diverse teams required coordination, patience, and a strong platform engineering foundation. These trade-offs were essential to achieving consistency, scalability, and a developer-first API platform.

Final Thoughts

Adopting APIOps with GitHub Actions marked a significant shift in API management, moving away from manual updates and embracing Infrastructure as Code (IaC) principles. This transformation enabled automation, consistency, and reliability in deploying and maintaining APIs. By integrating APIOps into CI/CD pipelines, API lifecycle management became scalable, repeatable, and efficient, drastically reducing the time and effort required for updates.

This transition not only minimized human errors but also accelerated deployment cycles, ensured environment consistency, and reinforced compliance with enterprise standards. By combining Terraform for infrastructure provisioning and APIOps for API management, systems became more resilient and adaptable to evolving business needs.

Through this journey from manual processes to fully automated API operations, APIOps has proven to be a transformative force in modern API strategies. As systems evolve, continued investments in automation and monitoring will further strengthen API management workflows, enhancing both reliability and scalability.

More than just a technical upgrade, APIOps has empowered teams to focus on innovation rather than maintenance, driving operational excellence and boosting productivity. As the API landscape advances, organizations that embrace APIOps and automation will be better positioned to tackle future challenges, accelerate digital transformation, and lead in efficiency-driven API development.