Enterprise Software Licensing
Enterprise Software Licensing Solutions
License Optimization & Cost Control
Identify inactive subscriptions, overlapping tools, and unnecessary software spend across enterprise environments.
Renewal & Compliance Management
Improve visibility into renewals, true ups, and licensing activity before costs and compliance risks increase.
Software Asset Visibility
Track software usage and licensing activity across departments to support better planning and purchasing decisions.
Client OutcomeManaging Rising Licensing Costs
The Situation
The Outcome
Solution Components: Microsoft Licensing Assessment | License Optimization | Renewal Planning | AI Software Readiness | Subscription Cleanup | Usage Analysis | Compliance Review
Customer Success Highlight
How We Help Build the Right
Licensing Strategy for You
Kim Carello
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FAQsSoftware Asset Management (SAM)
The answer is a shared module library that teams adopt incrementally, not a forced rewrite. We audit the existing Terraform across teams to identify duplicated patterns, modules that are already good, and those that need to be rebuilt to a shared standard. The consolidated library is built from the best existing work, plus new modules where none of the existing implementations were strong enough. Teams adopt the shared modules at their own pace, typically during planned refactors or new module creation, rather than being forced to migrate existing working infrastructure immediately. Governance is enforced through CI/CD policies that require new modules to reference the shared library, so the standard becomes the path of least resistance rather than an imposed requirement.
State management at scale requires a remote state with explicit ownership boundaries and careful use of state references between modules. We typically implement remote state in backends such as Azure Storage, AWS S3, or Terraform Cloud, with state isolation by environment and by functional domain. Cross-module dependencies are managed through data sources or remote state references rather than monolithic state files, which limits the blast radius of any single state corruption event. State locking is non-negotiable, as is backup and versioning of state files. For organizations running Terraform at a significant scale, we often recommend Terraform Cloud or Terraform Enterprise to provide the governance, run history, and access controls that raw backend storage does not. The goal is for state management to become invisible rather than a constant operational concern.
IaC integration is almost always additive rather than disruptive if designed correctly. We add IaC pipelines as separate stages in your existing CI/CD framework, typically triggered by infrastructure code changes rather than application code changes. The plan stage runs on every pull request to give reviewers visibility into what will change. The apply stage runs after merge approval with manual approval gates for production environments. Application deployment pipelines remain unchanged because infrastructure and application lifecycles are deliberately separated. Where application teams own their own infrastructure through self-service platforms, we implement an abstraction layer that lets them consume infrastructure via defined interfaces rather than writing Terraform code directly. The existing deployment processes keep working while the IaC discipline is built around them.
FAQsPolicy as Code & Guardrails
Policy drift almost always comes from one of three root causes: policies in audit mode that were never moved to enforcement, policy assignments scoped too narrowly to catch every resource, and an exception process that became the default path rather than the exception. We audit the current policy state to identify which policies are preventing what, which are only logging violations, and which are being worked around through exceptions. Policies that matter get moved to enforcement mode on a defined timeline, with remediation of existing violations coordinated with the affected teams. The assignment scope is widened to catch every resource the policy should apply to. The exception process is tightened so exceptions require explicit approval, carry expiration dates, and are reviewed quarterly. The result is a policy framework that actually enforces the standards it defines.
Multi-cloud policy enforcement requires an abstraction layer above the native policy frameworks, not underneath them. We typically implement the Open Policy Agent as the unifying layer, allowing policy to be defined once and enforced across Azure, AWS, and Google Cloud via their respective admission controllers or API gateways. Native policy frameworks like Azure Policy and AWS Config remain in place for platform-specific controls that do not translate across clouds, while cross-cutting policies such as tagging standards, encryption requirements, and network security baselines are defined centrally and enforced consistently. The governance model defines which policies belong in the cross-platform layer versus the platform-native layer, preventing the fragmentation that occurs when each cloud has its own, independently managed policy framework.
This is the central tension in any policy-as-code program, and it is solved through process, not through weakening the policies. We implement a defined exception process that lets engineering teams request temporary exceptions with documented business justification, compensating controls, and a remediation timeline. Exceptions are reviewed by a small governance committee rather than a single gatekeeper, which prevents bottlenecks. Policies that trigger exception requests frequently are flagged for review, because a policy that is always being excepted is usually either too strict, too broad, or missing a legitimate use case that should be accommodated in the policy itself. The goal is a policy framework that teams can work within rather than around, with a clear path for the edge cases that every environment has.
FAQsContainer Orchestration & Kubernetes
Drift between clusters is almost always managed through GitOps, which makes the git repository the source of truth and continuously reconciles the actual cluster state against the declared state. We typically use Argo CD or Flux as the reconciliation engine, with cluster configuration, application manifests, and policy definitions stored in Git. Drift is automatically detected and either remediated or surfaced as a violation, depending on its severity. For existing clusters with drift, the first step is to capture the current state as a declared configuration, which gives you a baseline to work from. From there, drift is addressed incrementally as clusters are brought into alignment with the shared configuration standards. The rebuild option is necessary only when the drift is so severe that aligning the existing cluster is more expensive than replacing it.
Multi-tenant Kubernetes security requires defense-in-depth across namespace isolation, network policies, pod security standards, and admission control. Namespaces provide the first layer of separation, with resource quotas and limit ranges preventing one tenant from starving others. Network policies enforce communication rules between namespaces and with external services, typically implemented through Calico or Cilium. Pod security standards enforce baseline security requirements across every pod, with stricter policies applied to namespaces that handle sensitive workloads. Admission controllers like OPA Gatekeeper or Kyverno enforce policy at deployment time, preventing non-compliant workloads from reaching the cluster. The governance model defines which teams can deploy to which namespaces, which security profiles apply to each namespace, and how exceptions are handled. This framework scales to dozens of teams on shared clusters without creating security incidents
The tradeoffs are primarily operational rather than technical. Managed Kubernetes services provide a control plane-as-a-service with minimal operational overhead, but their platform capabilities beyond core Kubernetes are limited. You get pod scheduling and basic networking, but you build everything else yourself, which means ingress, service mesh, developer experience tooling, CI/CD integration, and monitoring stacks all become individual implementation decisions. OpenShift provides those capabilities as an integrated platform, reducing the amount of platform engineering work required to reach a production-ready environment, but introducing platform-specific patterns and licensing costs that managed Kubernetes does not. The right answer depends on your platform engineering team size and maturity, the consistency requirements across your environments, and whether the additional capabilities OpenShift provides justify the cost and the reduced portability. We model this decision against your specific environment rather than defaulting to a recommendation.
FAQsMulti Cloud Governance
Unification happens through a layered governance model, not through environment rebuilds. We define a baseline governance standard that applies to every cloud, covering tagging, encryption, network baseline, identity integration, and logging. Each platform’s native tools are configured to enforce the baseline in the way each platform supports it: Azure Policy in Azure, AWS Config and Control Tower in AWS, and Organization Policies in Google Cloud. A cross-platform governance tool, typically HashiCorp Terraform Cloud or a cloud management platform like Morpheus, sits above the native tools to provide unified visibility, reporting, and policy management. Existing environments are brought into compliance incrementally, starting with the baseline controls and expanding into deeper governance as the foundation stabilizes. The rebuild option is rarely needed because the governance layer can be applied to existing environments without touching the workloads running in them.
Unified cost visibility requires a cost management platform that normalizes data across clouds, not reliance on native billing exports. We typically implement platforms such as Flexera, Apptio Cloudability, or FinOps capabilities in Azure Cost Management, along with equivalent tools in AWS and GCP, with the data consolidated into a single reporting platform. The normalization model maps cost data to consistent categories across clouds, making cross-cloud comparisons meaningful rather than apples-to-oranges. Tagging discipline becomes critical because unified cost allocation depends on consistent tagging across all platforms, which means the governance baseline must enforce tagging rigorously. The reporting layer is where business value is realized, but the foundation lies in tagging discipline and data normalization. Without those, unified cost visibility is directional at best.
This is the federated governance model, where central IT defines the standards and business units own the execution. Central IT is responsible for baseline policies, identity and access controls, network architecture, and universal security standards. Business units are responsible for operating within those standards in their own subscriptions or accounts, including workload deployment, application architecture, and business unit-specific policy extensions. The tooling enforces the central standards automatically via policy-as-code, so central IT does not have to review every change. Business units retain autonomy over decisions that affect their own workloads, while central IT retains control over decisions that affect the entire organization. This model scales cleanly because central IT is not a bottleneck for every business-unit decision, yet governance remains consistent because the standards are enforced automatically rather than reviewed manually.
FAQsPlatform Engineering & Self Service
Start with the highest-volume provisioning requests that currently go through central IT, as those represent the clearest value for both engineering teams and operations. Typically, that means application environments, databases, and container workloads rather than infrastructure primitives like networking or identity. The initial platform scope covers high-volume workflows with opinionated defaults that automatically encode governance, so teams get speed, and IT gets consistency. The scope expands over time as the platform matures and new use cases are ready for self-service. What we avoid is the ambitious scope that tries to cover everything at once, which produces a platform that is incomplete in every area rather than excellent in the areas that matter most. Platform engineering is iterative, and the first version should solve real problems for real teams within a defined timeline, not deliver a complete platform eighteen months from now.
The balance is achieved through tiered abstraction, where the platform offers opinionated defaults for common use cases and escape hatches for edge cases that do not fit the defaults. Common workflows are provisioned through simple interfaces with built-in governance, giving most teams speed without requiring them to understand the underlying infrastructure. Edge cases that require more flexibility use lower-level interfaces with additional governance gates, which preserves speed for the common case while handling the exceptions through a different path. Teams that genuinely need something outside the platform entirely have a defined path to request it, with the expectation that legitimate edge cases will be incorporated into the platform over time if they become common. This tiered model scales because most teams use the opinionated path, and the platform governance framework handles the exceptions without breaking down.
Platform engineering value is measured along three dimensions: time to production, operational overhead, and governance consistency. Time to production measures how quickly engineering teams can move from a new request to running infrastructure or applications, which should improve significantly for workflows that the platform supports. Operational overhead measures the support burden on the platform team and the incident rate from platform-managed infrastructure, which should be lower than equivalent manual provisioning. Governance consistency measures the compliance rate for platform-managed resources versus manually provisioned ones, which should approach 100% for platform-managed resources. If the platform is not meaningfully improving at least two of those three dimensions, it is not delivering enough value to justify the operational cost. We help you establish the baseline measurements before the platform is built so the value case is quantifiable rather than assumed.
FAQsSecrets & Configuration Management
Consolidation requires a parallel operation period, not a bulk migration. We start with a full secrets inventory that identifies every location where secrets currently live, what consumes them, and their rotation and expiry states. Vault is deployed with the appropriate auth methods, policies, and lease configurations before any secrets migrate. Applications are migrated one at a time, starting with non-production workloads, with the secret maintained in both the old location and Vault during the transition. Once the application is validated against Vault, the old secret is decommissioned. This approach is slower than a bulk migration but eliminates the production incidents that bulk migrations typically cause. For applications that cannot be easily refactored to read from Vault, we implement a Vault agent as an injection layer that provides secrets to legacy applications without code changes.
Legacy applications and third-party integrations are the hardest part of any secrets program because they were often designed around the assumption that credentials are static. For applications that cannot be refactored to handle rotation, we implement rotation at a layer they can consume, typically through Vault templated configuration files or sidecar patterns that present rotating credentials as static endpoints to the application. For third-party integrations where credentials cannot be rotated frequently due to vendor limitations, we implement the longest acceptable rotation cycle, with strict access controls and audit logging, to minimize residual risk even if the rotation cadence is not ideal. Rotation frequency is risk-based rather than uniform, with critical systems rotating frequently and legacy systems rotating at whatever cadence they can support without breaking.
Multi-environment secrets management works best with a centralized Vault deployment accessed from every environment, rather than independent instances in each location. The architecture typically uses Vault Enterprise with performance replicas in each region or cloud, which provides local read performance while maintaining centralized management and audit logging. Authentication is handled through each environment’s native identity provider, which means workloads in Azure authenticate to Vault through Azure AD, workloads in AWS through IAM, and on-premises workloads through Kubernetes service accounts or similar mechanisms. The result is a single logical Vault deployment that any workload can access with appropriate authentication, rather than fragmented secret stores that must be manually synchronized. Network connectivity and disaster recovery require careful design, but the operational simplicity of a unified architecture significantly outw