With businesses rapidly exploring and implementing AI use cases, cloud computing is playing a critical role in supporting scalable, efficient and outcome-driven operations. Gartner says that worldwide end-user spending on public cloud services will total $723.4 billion in 2025, with 90% of organizations adopting a hybrid cloud approach through 2027. The increasing complexity of cloud environments demands a modern approach to deployment and management. This is where Infrastructure as Code (IaC) and automation become critical.
Traditionally, setting up infrastructure was a manual, time-consuming, and error-prone process. However, with IaC, we can define and provision infrastructure using code, enabling repeatability, version control, and automation. A 2025 report found that 89% of organizations surveyed had adopted IaC to some degree, but only 6% had all their infrastructure defined in code.
Let’s look at how IaC and automation streamline Azure deployments and accelerate time to value. Azure fosters a strong DevOps culture, providing tools and services that enable continuous integration, continuous delivery (CI/CD), and infrastructure automation. By embracing DevOps principles, organizations can automate their Azure deployments, reduce manual errors, and achieve faster release cycles. This agility helps businesses stay competitive and respond quickly to changing needs. Tools like Terraform further enhance this by enabling consistent, version-controlled management of Azure resources.
How Tiger Analytics’ DevOps Accelerator Simplifies Infrastructure Provisioning
Based on our work with several Fortune 500 clients, we at Tiger Analytics developed an accelerator that addresses this need for automation. Our DevOps Accelerator (DAC) is a self-service automated infrastructure provisioning solution for Azure and AWS, built on Terraform. Let’s break down what sets it apart:
- Self-Service Provisioning: DAC empowers development teams to provision their own infrastructure through a user-friendly interface, reducing dependencies on operations teams and accelerating development workflows.
- IaC with Terraform: By leveraging Terraform, DAC enables the definition of Azure infrastructure as code. This allows for version-controlled infrastructure configurations, just like application code, ensuring consistency and auditability.
- Pre-built Templates: DAC provides ready-made templates for common cloud architecture patterns and CI/CD pipelines. This significantly reduces the time and effort required to set up new environments.
- Key Features for Efficiency: DAC offers a suite of features designed to enhance efficiency and control:
- User Onboarding: Seamless integration with Active Directory/IAM for authentication and authorization.
- Q&A Mode for Infra Input: Simplified infrastructure requests through an intuitive Q&A interface.
- CI/CD Integration: Streamlined integration with CI/CD pipelines for automated deployments.
- Cost Estimation: Built-in cost estimation capabilities to help teams understand and manage cloud spending.
- Workflow Approval: Governance and control through workflow approval processes.
- Audit Trail: Comprehensive audit trails for tracking infrastructure changes.
- Infrastructure Decommissioning: Automated infrastructure tear-down to optimize resource utilization.
Benefits of Tiger Analytics’ DevOps Accelerator for Scalable Cloud Management
From a DevOps standpoint, tools like DAC are essential because they:
- Accelerate Development: By automating infrastructure provisioning, developers can spin up environments quickly and efficiently, enabling faster development and testing cycles.
- Improve Collaboration: IaC fosters better collaboration between development and operations teams, as infrastructure is defined and managed in a shared codebase.
- Enhance Reliability: Automation reduces the risk of manual errors and ensures consistent infrastructure deployments.
- Enable Continuous Delivery: IaC is a cornerstone of CI/CD, enabling the automated deployment of applications and infrastructure changes.
In addition to efficient deployment, managing cloud costs is paramount. A 2025 State of Cloud report found that 84% of organizations are concerned about managing cloud spend. Hence, FinOps practices, such as those facilitated by DAC, are crucial. With infrastructure as code, we gain better visibility and control over resource allocation, making it easier to optimize spending and avoid waste. Features like DAC’s cost estimation capabilities help teams proactively manage budgets and make informed decisions about resource provisioning. To understand the impact of these practices, let’s look at real-world examples.
Case Study 1: Building PTP’s Modern Data Ecosystem with DevOps at the Core
Malaysia’s premier transshipment port, Pelabuhan Tanjung Pelepas (PTP), was looking to scale the agility of its operations and respond quickly to market changes with a comprehensive view of all its data systems. This called for a centralized repository and modern data systems that are well-governed, secure, and accessible. We collaborated with PTP on enabling an end-to-end Azure services deployment. The initiative involved:
- End-to-End Azure Services Deployment: Comprehensive deployment of Azure services.
- Landing Zone Design: Designing a robust landing zone for multiple environments, incorporating backup and recovery strategies for all Azure PaaS services.
- Azure DevOps CI/CD Pipelines: Deploying Azure services using Azure DevOps CI/CD pipelines, including PaaS, IaaS, ADLS, Databricks, Data Factory, VNet (Hub & Spoke), and Azure VPN.
- Hybrid Deployment: Establishing hybrid deployment with on-premises connectivity to access on-premises databases and workloads using Azure VPN.
Here is how automation and DevOps practices enabled the rapid and efficient deployment of complex Azure environments:
- Infrastructure as Code (IaC): Using Terraform and pre-built templates, we automated provisioning of version-controlled Azure infrastructure (Resource Groups, ADF, ADLS, Azure SQL, Azure Databricks, Key Vault, and Networking components). This led to faster, repeatable, and consistent infrastructure deployment across DEV, UAT, PROD environments and reduced human error and configuration drift.
- Continuous Integration and Continuous Deployment (CI/CD): We implemented automated CI/CD pipelines in Azure DevOps to streamline the deployment of ADF pipelines and code migrations across environments. These pipelines included approval gates and environment promotion mechanisms for controlled production releases. As a result, feature delivery cycles were significantly faster with minimal manual intervention, while deployment quality improved through rollback capabilities and enhanced change traceability.
- Monitoring: We strengthened the monitoring framework with the instrumentation of Azure Resources using Azure Monitor, Log Analytics, and Application Insights, and alerting mechanisms created via DevOps pipelines. This enabled real-time visibility into deployment health, data pipeline failures, and user activity, along with faster incident response and root cause analysis.
- Governance and Access Control: Governance and access control were established through Role-Based Access Control (RBAC) and Azure Policy definitions applied as code, with DevOps workflows used to enforce Just-in-Time (JIT) access, tagging, and resource limits. This resulted in audit-ready access trails and tighter security boundaries and improved alignment with Legal/Security Audit requirements for change and access control.
The new platform centralized data from across 10+ operational sources with scalable storage and processing capabilities that could handle increasing data volumes without affecting performance. With a modern data ecosystem that enabled real-time operational reporting and facilitated the development of more sophisticated analytics and predictive models, PTP could uncover new insights and optimize operations.
Case Study 2: Self-Service Infrastructure Provisioning for a Global Financial Services Leader
A leading asset and wealth management firm observed an opportunity to empower its business and research teams with a self-service infrastructure provisioning platform that supported data and analytics research while ensuring strong governance, security, and operational oversight. The firm partnered with us to build and deploy a platform that met these requirements using Azure tools and technologies. Our approach can be broken down into the following core components:
- Modern tech stack: The platform was built using React UI, Python FastAPI, Terraform for IaC, and Azure DevOps for CI/CD automation to enable rapid, reliable, and repeatable provisioning of Azure services such as Databricks, Synapse, ADF, SQL Server, and Key Vault for data and analytics workloads.
- Self-service user management: The platform enabled self-service onboarding with role-based access control for efficient and secure access. It integrated directly with the client’s Active Directory for authentication and authorization. Administrators could assign roles and monitor user activity, maintaining operational visibility and compliance.
- Automated infrastructure provisioning: Pre-configured Terraform templates and custom configuration options allowed users to request and deploy infrastructure efficiently and consistently. The platform improved reliability across different provisioning scenarios with built-in parameter validation and resource dependency management.
- Integrated CI/CD pipelines: The solution enabled automated creation and monitoring of deployment pipelines using Azure DevOps for repeatable, reliable, and traceable infrastructure changes, reducing risk and expediting release cycles.
- Cost transparency: Before provisioning any infrastructure, users could view cost estimates with actual spend data made available post-deployment. Combined with approval workflows, these mechanisms could help teams control budgets while maintaining compliance.
- Audit and governance: The platform helped maintain comprehensive audit trails of all user actions and infrastructure changes, along with enforcing best practices and resource tagging standards for stronger governance and audit readiness.
The new platform streamlined the user experience and helped the financial services leader reduce provisioning time from days to a few hours. It standardized infrastructure deployment, enhanced governance with approval workflows, and improved cost control through monitoring.
Cloud platforms like Azure offer the flexibility and capabilities required for organizations looking to make the most of advanced analytics and AI. However, this is possible only when paired with the right practices: automation, governance, and a DevOps mindset. Based on our work with Fortune 500 clients across industries, we’ve observed that self-service platforms, automated pipelines, and codified governance are essential to scaling AI initiatives securely and sustainably. The future of cloud infrastructure is powered by automation, secured by policy-as-code, and driven by business outcomes. Our DevOps Accelerator is one step toward that vision, helping businesses experiment faster, deploy more confidently, and respond to business needs in real time.
References
https://www.gartner.com/en/newsroom/press-releases/2024-11-19-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-total-723-billion-dollars-in-2025
https://www.firefly.ai/state-of-iac-2025
https://info.flexera.com/CM-REPORT-State-of-the-Cloud
