Many SaaS companies treat the APJ (Asia Pacific & Japan) region as the final frontier in their global expansion roadmap. After finding success in North America and Europe, they look eastward — often expecting the same playbook to work. But for most, the APAC region proves to be far more complex and difficult to navigate than anticipated.

A Region, Not a Market

One of the biggest misconceptions is treating APAC as a single, unified market. In reality, it’s a patchwork of vastly different economies, languages, legal systems, and business cultures.

• Japan is high-touch, relationship-driven, and risk-averse.
• Australia is fast-moving but expects local support and presence.
• Southeast Asia includes fast-growing but price-sensitive markets.
• India has immense technical talent but unique procurement dynamics.

Trying to apply a one-size-fits-all approach is a common path to failure.

Key Challenges for SaaS Companies in APAC

1. Cultural Complexity

Unlike the relative homogeneity of U.S. or even EU business practices, APAC requires deep cultural intelligence. Decision-making is slower and more hierarchical in Japan and Korea, while speed and volume dominate in Southeast Asia.

2. Localization Beyond Translation

Localization is not just about UI strings. Customers expect:

• Local cloud hosting options (for compliance)
• Local currency billing and payment methods
• Local support teams and SLAs
• Regulatory alignment (e.g., data residency laws in Australia or Singapore)

Without these, you’re not seen as a serious player.

3. Longer Sales Cycles

Enterprise SaaS in APAC can involve:

• More in-person meetings (even post-COVID)
• Multiple layers of approval
• Heavy reliance on trusted relationships and system integrators

This is where many U.S.-born SaaS startups struggle — their sales model relies on speed and scale, not patience and partnerships.

4. Hiring the Right Talent

Hiring APAC-savvy go-to-market leaders is tough. You need people who understand both Western SaaS metrics and local business etiquette. Getting this wrong can stall momentum for years.

5. Trust and Brand Equity

Brand recognition doesn’t always translate. A company well-known in Silicon Valley might be a complete unknown in Tokyo or Jakarta. Building trust takes time — often requiring local references, joint ventures, or partnerships with regional players.

What Works Instead?

• Start small, but invest local: Set up a focused team in a beachhead country like Singapore, Sydney, or Tokyo.
• Localize deeply: Not just your product, but also your contracts, compliance posture, and pricing.
• Hire experienced local leaders: They know how to navigate procurement, build relationships, and avoid cultural missteps.
• Be patient: APAC may take longer to ramp, but the payoff can be huge with the right execution.

Final Thoughts

The APAC region is full of opportunity — some of the world’s fastest-growing economies and most tech-savvy populations are here. But SaaS companies that underestimate the cultural, operational, and regulatory complexities often find themselves stuck.

Success in APAC isn’t just about market entry. It’s about deep localization, long-term thinking, and building genuine local trust.

Have thoughts or experiences expanding your SaaS business into APAC? Let me know or connect on LinkedIn.

Amazon recently introduced an innovative open-source tool called the Model Context Protocol (MCP) Server for Amazon EKS, designed to transform how developers build and manage Kubernetes-native applications with the help of AI.

What is the MCP Server?

The MCP Server acts as a context bridge between Kubernetes clusters (especially EKS) and modern AI coding assistants like:

• Amazon Q Developer CLI
• Cline
• Cursor (VS Code extension)

It provides real-time access to cluster context and APIs, enabling large language models (LLMs) to understand your deployment environment, Kubernetes objects, and infrastructure configurations.

Why It Matters

Traditional code assistants rely only on static code analysis. With MCP, developers now benefit from live, contextual information like:

• Current running pods, services, and deployments
• Configuration of namespaces and secrets
• Events and logs related to application issues
• Helm chart structures and Kustomize overlays

This enables context-aware AI responses such as:

• Real-time debugging guidance
• Deployment suggestions
• Helm/Kustomize optimization tips
• Live environment-aware YAML completions

How It Works

The MCP server:

1. Runs as a Kubernetes service connected to your EKS cluster.
2. Exposes a protocol endpoint (gRPC and HTTP) for AI tools to query.
3. Delivers context-specific data securely to authorized AI developer agents.
4. Can be deployed via Helm or manually using YAML manifests.

Benefits

• Accelerated development with AI-generated code that fits your live infrastructure
• Improved troubleshooting with AI visibility into live logs, events, and cluster state
• Tighter DevOps collaboration, as AI can help codify best practices directly in the tools devs use

Security and Privacy

The MCP server respects RBAC and IAM permissions, ensuring no unauthorized data is exposed. All requests made by AI assistants are scoped to what the developer or tool has access to.

Get Started

To deploy the MCP server to your EKS cluster:

helm repo add eks-mcp https://aws.github.io/eks-mcp
helm install mcp-server eks-mcp/mcp-server

Microsoft has announced sweeping changes to its Cloud Solution Provider (CSP) program, effective from July 2025. These updates will directly affect distributors, resellers, and especially small Microsoft partners, reshaping how they operate and deliver services to customers.

Key Changes in the CSP Program

• Revised Resale Price Calculations
  Microsoft will change how the maximum resale price is calculated, aiming to bring more transparency and alignment across the partner ecosystem.

• Simplified Monthly Billing
  Adjustments will be made to enhance billing flexibility and streamline monthly invoicing support for customers.

• Strategic Pivot to Cloud and AI
  The CSP framework will now heavily emphasize cloud-native services and AI capabilities, encouraging partners to align with Microsoft’s broader vision.

“These changes will enable partners to deepen customer relationships and focus on high-value cloud and AI outcomes,”
— Nicole Dezen, Chief Partner Officer, Microsoft.

Impact on Small Microsoft Partners

While these changes aim to modernize CSP, small partners may face several challenges:

• Operational Complexity
  New pricing structures may require updates to internal systems and customer contracts.

• Competitive Pressure
  Larger partners with more resources may adapt faster, potentially squeezing smaller players.

• Skills Gap
  Increased focus on AI and cloud may necessitate investment in training, certifications, or staff.

Recommendations for Small Partners

1. Audit Pricing Strategies
   Review current pricing models and ensure they align with Microsoft’s updated pricing frameworks.

2. Invest in Capabilities
   Focus on cloud migration, AI services, and managed services to remain competitive.

3. Leverage Microsoft’s Partner Resources
   Engage with Microsoft Partner Center and other support channels for training and migration support.

4. Partner Up
   Explore collaboration with ISVs, distributors, or other CSPs to scale offerings and share resources.

Final Thoughts

Microsoft’s CSP updates reflect a broader industry shift toward subscription services, automation, and intelligent cloud platforms. Small partners who adapt early and strategically will be best positioned to thrive in this evolving landscape.

For more details, read the full article on ARNnet.

Amazon ECS (Elastic Container Service) supports pulling images from external Docker registries such as Docker Hub, GitHub Container Registry (GHCR), Google Container Registry (GCR), and even private self-hosted registries. This flexibility is useful for teams who manage images outside of Amazon ECR or rely on open-source or enterprise images stored in third-party registries. The steps below explain how to configure ECS to securely pull images from an external registry, including support for authentication, permissions, and networking requirements.

1. Define your container image in the ECS task definition using the full image path.
   In your ECS task definition, the image field should include the full URL of the external image. For example, for a public image from Docker Hub, you can use "nginx:latest" or "docker.io/library/nginx:latest". If you’re using GitHub Container Registry, an image might look like "ghcr.io/myorg/myapp:latest". ECS will attempt to pull this image when the task is launched.

2. Store private registry credentials in AWS Secrets Manager.
   If the image is hosted on a private registry that requires authentication (e.g., a private Docker Hub repo or GHCR package), you’ll need to store credentials in AWS Secrets Manager. Create a secret with a JSON structure like this:
   {"username": "your-username", "password": "your-password-or-token"}.
   This secret will be referenced later in the ECS task definition so ECS can authenticate before pulling the image.

3. Reference the secret using the repositoryCredentials field in your task definition.
   In the same container definition where you specify the image, add a repositoryCredentials section that points to the full ARN of your Secrets Manager secret. For example:
   "repositoryCredentials": { "credentialsParameter": "arn:aws:secretsmanager:us-east-1:123456789012:secret:MyDockerHubSecret-Abc123" }.
   ECS will retrieve the secret at runtime and use it to authenticate against the registry.

4. Attach the correct permissions to the ECS task execution role.
   The IAM role associated with the ECS task execution (not the task role) must have permission to access the secret from Secrets Manager. Add the following permission to your task execution role:
   "Effect": "Allow", "Action": "secretsmanager:GetSecretValue", "Resource": "<your-secret-arn>".
   Without this permission, the task will fail to pull the image from the registry.

5. Ensure ECS tasks have internet access to reach external registries.
   ECS must connect to the external registry over the internet to pull the image. For EC2 launch type, make sure the instance has a public IP or outbound access via NAT Gateway. For Fargate, place the task in a public subnet or ensure the private subnet routes to a NAT Gateway. Without internet access, ECS won’t be able to download the container image and the task will fail.

6. Use GitHub Container Registry as an example for private registry authentication.
   If you’re using GitHub Container Registry (ghcr.io), generate a personal access token with read:packages scope from GitHub. Then, create a secret in AWS Secrets Manager with this structure:
   {"username": "your-github-username", "password": "ghp_xxxxxxxx"}.
   Reference this secret in your ECS task definition as described above, and ECS will use it to authenticate when pulling the image from GHCR.

7. Pin your images by digest instead of using tags for immutability.
   Tags like latest can change over time, which can lead to unpredictable behavior in production. To avoid this, use the image’s digest instead of a tag. For example:
   "ghcr.io/myorg/myapp@sha256:abcdef123456...".
   This ensures that the same image is pulled every time the task runs, regardless of future changes to the tag.

8. Deploy your ECS service or task and monitor for pull success.
   Once your task definition is updated and permissions are in place, deploy your ECS task or service. ECS will retrieve the credentials from Secrets Manager and attempt to pull the image from the external registry. If the task fails, check the ECS console and CloudWatch Logs for error messages related to image pulling or authentication. Common issues include missing permissions, incorrect secret formats, or network connectivity failures.

By following these steps, you can run container workloads in ECS using images stored outside of Amazon ECR. This makes it easy to reuse existing CI/CD pipelines, leverage third-party registry services, and adopt open-source containers without needing to migrate everything into ECR. Whether you’re working with public images or private packages that require authentication, ECS can securely connect to external registries and keep your containers running reliably.

As AI agents become increasingly powerful and diverse, the need for a universal way for them to communicate and collaborate has never been more pressing. Enter A2A (Agent-to-Agent) — an open-source protocol from Google that aims to standardize how AI agents talk to each other, regardless of their underlying frameworks or vendors.

What Is A2A?

A2A is designed to let different AI agents interact over a shared protocol, enabling tasks to be delegated, negotiated, and executed smoothly across systems. Whether you’re building with LangGraph, CrewAI, or something custom, A2A acts like the lingua franca for agents.

Key Components

• Agent Card: A JSON file (/.well-known/agent.json) describing the agent’s capabilities, endpoints, and authentication requirements — useful for discovery and introspection.
• A2A Server & Client: Servers expose capabilities via HTTP, while clients (which could be other agents) initiate tasks or communications.
• Task Lifecycle: From submitted to working, input-required, or completed, A2A tracks the state of work — ideal for long-running or interactive jobs.
• Messages, Parts & Artifacts: Messages between agents are broken into Parts (text, file, data), and results are returned as Artifacts.

Try It Out

Google has provided several helpful resources:

• Sample A2A client/server implementations in Python and JavaScript
• Multi-agent web app demos
• CLI tools to interact with agents
• Templates for building A2A-compatible agents in different frameworks

What’s Coming Next?

The roadmap for A2A includes:

• Richer discovery with built-in auth metadata
• Skill querying for dynamic capabilities
• Smarter UX negotiation (e.g., video/audio switching mid-task)
• More robust streaming and push notification support

Join the Movement

A2A is open for contributions, and the team welcomes feedback via GitHub or a dedicated form. As the AI landscape grows more complex, efforts like A2A are crucial to making intelligent systems interoperable, modular, and collaborative.

GitHub Repository: https://github.com/google/A2A

AWS has announced the general availability of Amazon VPC Route Server on April 1, 2025. This new feature significantly simplifies dynamic routing between virtual appliances within an Amazon VPC by introducing a managed Border Gateway Protocol (BGP)-based route propagation solution.

What Is Amazon VPC Route Server?

Amazon VPC Route Server is a new capability that allows you to advertise and receive routing information using BGP between your virtual appliances and the VPC route tables. Instead of relying on overlay networks, custom scripts, or virtual routers to keep your route tables updated, you now have a native AWS service that handles this dynamically and securely.

Key Benefits

Managed BGP Routing

You can now deploy Route Server endpoints inside your VPC and peer them directly with your virtual appliances using BGP. This allows appliances to dynamically advertise or withdraw routes, which are then automatically updated in the associated route tables.

Simplified Failover and High Availability

One of the most compelling use cases is high availability for virtual appliances (e.g., firewalls, SD-WAN hubs, NAT gateways). When a device fails or is replaced, the BGP session withdraws the route, prompting the Route Server to remove the affected path from your route table. This automatic propagation of route changes helps reduce downtime and operational overhead.

No More Overlay Networks

Previously, you might have relied on overlay network setups or scripting solutions to achieve dynamic routing. VPC Route Server eliminates the need for:

• Custom Lambda scripts for route updates
• Overlay routers like BIRD or FRRouting
• Complex automation pipelines for route table manipulation

Supported Regions

As of launch, VPC Route Server is available in the following AWS Regions:

• US East (N. Virginia)
• US East (Ohio)
• US West (Oregon)
• Europe (Ireland)
• Europe (Frankfurt)
• Asia Pacific (Tokyo)

Ideal Use Cases

• High availability pairs of virtual firewalls
• Dynamic SD-WAN connectivity
• Route propagation from third-party virtual appliances
• Seamless integration for transit VPCs

Getting Started

You can get started by navigating to the VPC Console, creating a Route Server endpoint, and establishing BGP peering with your appliances. You’ll also need to specify which route tables should receive the BGP routes.

For full documentation, refer to the AWS official documentation.

Final Thoughts

The launch of Amazon VPC Route Server addresses a long-standing gap in AWS networking. By offering native support for BGP-based dynamic routing, it simplifies architectures and boosts resiliency for customers building complex, hybrid, and cloud-native environments.

In the fast-moving world of Kubernetes security, misconfigurations and overlooked edge cases can expose entire clusters to serious risk. Recently, researchers from Wiz uncovered a set of high-impact vulnerabilities in the widely-used Ingress-NGINX controller—vulnerabilities that could allow attackers to bypass security controls, steal credentials, or take over workloads in multi-tenant environments.

Here’s a breakdown of what happened, what it means, and how to protect your clusters.

The TL;DR

• Vulnerabilities Found: Wiz disclosed multiple vulnerabilities in Ingress-NGINX, the most popular Kubernetes ingress controller.
• Impact: Attackers could exploit these flaws to:
  • Inject arbitrary code via annotations
  • Escalate privileges across namespaces
  • Access sensitive services or data
• Root Cause: Insufficient validation of user-supplied annotations and their interpretation in the NGINX configuration.

Background

Ingress-NGINX is the default ingress controller for many Kubernetes deployments. It provides a way to expose HTTP and HTTPS services to the outside world, route traffic, enforce SSL, and handle complex routing logic.

What makes this controller powerful—its flexibility through annotations—also introduces risk when those annotations are not properly validated.

The Vulnerability

Wiz discovered that certain annotations could be crafted in ways that trick Ingress-NGINX into injecting unsanitized configuration blocks directly into its internal NGINX configuration.

Specifically, the vulnerability stems from the nginx.ingress.kubernetes.io/server-snippet and related annotations. If these are not restricted at the admission level, users could inject arbitrary NGINX directives, effectively allowing:

• Unauthorized access to internal services
• Bypass of security policies
• Data exfiltration

In multi-tenant environments where untrusted users can define Ingress resources, this becomes a serious privilege escalation vector.

Real-World Impact

The issue becomes particularly dangerous in shared Kubernetes environments, such as:

• Internal developer platforms
• Multi-team CI/CD environments
• Kubernetes platforms offered by managed service providers

An attacker with permission to create or edit an Ingress resource could craft malicious annotations and compromise the entire cluster.

What You Should Do

The Wiz team coordinated disclosure with the maintainers, and patches are now available. Here’s how to protect your environments:

1. Upgrade to the latest Ingress-NGINX release
   Ensure you’re running a version with the vulnerability patched. Review the Ingress-NGINX changelog for details.

2. Audit and restrict annotation usage
   Limit the use of dangerous annotations (like server-snippet, configuration-snippet, and proxy-snippet) by applying admission controls or policies.

3. Enforce namespace boundaries
   Use Kubernetes RBAC and tools like OPA/Gatekeeper or Kyverno to ensure that users in one namespace can’t affect others.

4. Monitor for suspicious Ingress definitions
   Integrate cluster monitoring tools that can alert on new Ingress resources using unusual annotations.

Final Thoughts

This disclosure is a critical reminder that flexibility in configuration must be matched with rigorous security controls. Ingress controllers operate at the gateway of your cluster—they must be hardened, monitored, and tightly controlled.

If you’re managing Kubernetes in a multi-tenant or high-security environment, now is the time to review your use of Ingress-NGINX and lock down risky paths.

For more technical details, check out the original write-up by Wiz.

Stay secure,
Duo

Introduction

When deploying applications on OpenShift Container Platform (OCP), exposing services to external users requires either Routes or Ingress. Both provide external access to workloads, but they differ in implementation, configuration, and flexibility. This article explores the key differences between Routes and Ingress, helping you choose the right approach for your application needs.

What is a Route in OpenShift?

A Route is an OpenShift-specific resource that provides external access to services running within the cluster. OpenShift Routes leverage the built-in HAProxy-based router to expose applications to the outside world.

Features of OpenShift Routes:

• Layer 7 (HTTP/HTTPS) Load Balancing: Routes support HTTP, HTTPS, and TLS termination.
• Path-Based Routing: Different applications can be served based on URL paths.
• Custom Certificates: Routes allow custom TLS certificates for secure communication.
• Wildcard Subdomains: You can define wildcard routes (e.g., *.apps.example.com).
• Sticky Sessions: Supports session persistence using cookies.

Creating a Route:

A Route can be created using oc expose or by defining a Route YAML manifest:

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apiVersion: route.openshift.io/v1
kind: Route
metadata:
  name: my-app-route
spec:
  to:
    kind: Service
    name: my-app-service
  port:
    targetPort: http
  tls:
    termination: edge

What is an Ingress in OpenShift?

Ingress is a standard Kubernetes resource used to manage external access to services within a cluster. Unlike Routes, which rely on the OpenShift router, Ingress controllers (e.g., Nginx, Traefik, or OpenShift’s HAProxy) handle traffic based on Ingress rules.

Features of Ingress:

• Works Across Kubernetes and OpenShift: Ingress is Kubernetes-native and works across multiple platforms.
• TLS Support: Supports secure communication using secrets for TLS certificates.
• Multiple Ingress Controllers: OpenShift supports its HAProxy-based Ingress controller but can also use Nginx, Traefik, or Istio.
• More Advanced Routing: Ingress allows advanced traffic management, such as redirects and rewrites.

Creating an Ingress:

A basic Ingress resource can be defined in YAML as follows:

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apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: my-app-ingress
spec:
  rules:
  - host: myapp.example.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: my-app-service
            port:
              number: 80
  tls:
  - hosts:
    - myapp.example.com
    secretName: my-app-tls

Can You Use Ingress on OpenShift?

Yes, OpenShift supports Ingress, and you can use it instead of Routes. However, there are some considerations:

1. Using OpenShift’s Default Ingress Controller

• OCP 4+ includes an Ingress Controller managed via the IngressController CRD in OpenShift’s openshift-ingress-operator namespace.
• You can create Ingress resources like in a standard Kubernetes cluster.

Example Ingress YAML on OpenShift:

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apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: my-app-ingress
spec:
  rules:
  - host: myapp.apps.ocp-cluster.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: my-app-service
            port:
              number: 80
  tls:
  - hosts:
    - myapp.apps.ocp-cluster.com
    secretName: my-app-tls

2. Deploying a Custom Ingress Controller

If you want a different Ingress controller (e.g., Nginx, Traefik, Istio), you need to deploy it manually:

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oc apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/main/deploy/static/provider/cloud/deploy.yaml

Then, configure your Ingress objects to use this controller.

Key Differences Between Route and Ingress

When to Use Route vs Ingress

• Use Route if:

  • You are working in a pure OpenShift environment.
  • You need native OpenShift features like wildcard routes or sticky sessions.
  • You want built-in HAProxy load balancing and automatic TLS.

• Use Ingress if:

  • You need a Kubernetes-native solution that works across platforms.
  • You want flexibility in choosing an Ingress controller (e.g., Nginx, Traefik, Istio).
  • You require more complex traffic management beyond OpenShift’s Route capabilities.

Conclusion

Both Route and Ingress play vital roles in exposing applications on OpenShift. If you need OpenShift-native features like wildcard domains and built-in TLS, Routes are the better choice. However, if you are working across Kubernetes environments or need advanced traffic control, Ingress provides more flexibility.

Understanding these differences helps you make an informed decision when designing network exposure strategies on OpenShift.

Kubernetes employs Quality of Service (QoS) classes to manage and prioritize pod resources, thereby mitigating Out of Memory (OOMKilled) errors.

Understanding OOMKilled Errors

OOMKilled errors occur when a container exceeds its allocated memory, prompting the Linux kernel’s OOM Killer to terminate the process to maintain node stability.

Kubernetes QoS Classes

Kubernetes assigns pods to one of three QoS classes based on their resource requests and limits:

Guaranteed QoS Class

To qualify for the Guaranteed class, all containers in the pod must specify equal memory and CPU requests and limits. These pods get the highest priority and are the last to be terminated under memory pressure.

Example YAML:

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apiVersion: v1
kind: Pod
metadata:
  name: guaranteed-pod
spec:
  containers:
    - name: app
      image: nginx
      resources:
        requests:
          memory: "256Mi"
          cpu: "500m"
        limits:
          memory: "256Mi"
          cpu: "500m"

Burstable QoS Class

Pods are considered Burstable if at least one container has memory or CPU requests set, but not all containers match requests and limits. These pods have a medium priority and may be terminated if the node runs out of resources.

Example YAML:

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apiVersion: v1
kind: Pod
metadata:
  name: burstable-pod
spec:
  containers:
    - name: app
      image: nginx
      resources:
        requests:
          memory: "128Mi"
          cpu: "250m"
        limits:
          memory: "512Mi"
          cpu: "1000m"

BestEffort QoS Class

Pods that have no resource requests or limits defined fall under the BestEffort class. These pods receive the lowest priority and are the first to be terminated under memory pressure.

Example YAML:

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apiVersion: v1
kind: Pod
metadata:
  name: besteffort-pod
spec:
  containers:
    - name: app
      image: nginx

Preventing OOMKilled Errors

By appropriately configuring resource requests and limits, Kubernetes ensures that critical applications remain operational even under memory constraints, effectively reducing the occurrence of OOMKilled errors.

Want to learn more? Check out this detailed guide on diagnosing and preventing OOMKilled errors.

Introduction

As APIs continue to evolve, a new paradigm is emergingModel Context Protocol (MCP). While not yet a widely adopted standard, the concept of context-aware APIs is gaining traction as a transformative approach in API-driven ecosystems.

But what exactly is MCP, and why does it matter for the future of APIs? In this post, well explore how APIs are becoming smarter, adaptive, and more efficient by leveraging context-awareness.

The Shift to Context-Aware APIs

Traditional APIs follow a request-response model, where clients explicitly request data, and the API returns it. However, this model has limitations, including redundant API calls, inefficient data retrieval, and lack of adaptability to user needs.

Context-aware APIs change this by:

• Understanding user behavior APIs can track user preferences, past interactions, and context to deliver personalized responses.
• Reducing redundant API calls Instead of fetching full datasets repeatedly, APIs can remember past interactions and send only the needed data.
• Enhancing real-time adaptability APIs can dynamically adjust based on environmental factors, location, device type, and session data.
• Improving performance and efficiency Less data transfer means faster response times and lower bandwidth usage.

How Context-Aware APIs Work

To make APIs context-aware, different technologies and protocols are being adopted:

1. GraphQL with Persisted Queries

GraphQL allows clients to request only the data they need, and with persisted queries, APIs can store frequently used requests, improving performance.

2. gRPC with Streaming

gRPC enables bi-directional streaming, maintaining session context between API calls, reducing the need for repeated authentication or redundant queries.

3. WebSockets and Server-Sent Events (SSE)

Real-time applications benefit from WebSockets and SSE, where servers push updates based on context changes instead of clients polling for new data.

4. Hypermedia APIs (HATEOAS)

Hypermedia APIs guide users dynamically through next actions by embedding links within API responses, improving discoverability and navigation.

The Role of AI in Context-Aware APIs

AI and machine learning are playing a huge role in advancing context-aware APIs. With AI-powered decision-making, APIs can predict what data a user might need next and proactively deliver relevant responses. This is especially valuable in:

• Conversational AI and Chatbots APIs that maintain session context to provide seamless user interactions.
• Recommendation Systems APIs that adapt based on user behavior (e.g., e-commerce, streaming services).
• Fraud Detection APIs that analyze context in real-time to prevent security threats.

Is MCP the Future of APIs?

If Model Context Protocol (MCP) refers to an emerging context-aware API paradigm, it could be a major step forward. While it is not yet a formalized standard, the movement towards event-driven architectures, AI-powered APIs, and federated API management aligns with the context-aware vision.

As the demand for faster, smarter, and more adaptive APIs increases, it is likely that MCP or a similar protocol will become a key component of future API architectures.

Final Thoughts

The API industry is shifting from static, request-driven interactions to dynamic, context-aware experiences. Whether through GraphQL, gRPC, WebSockets, HATEOAS, or AI-driven enhancements, the next generation of APIs will be more intelligent and adaptive than ever.

Stay tuned for more insights on API trends and technology at Duo’s Blog.