Cost Optimization (FinOps + Observability)
Cost Optimization (FinOps + Observability)
Spending on observability tools and cloud infrastructure grows faster than the visibility it generates. Organizations accumulate unused licenses, retain logs nobody queries, and don’t know which team or service is driving the largest cost in their cloud account. The result: surprise invoices, blown budgets, and technical decisions made without financial data.
Apiwan combines deep Datadog platform expertise with a structured FinOps practice so that every dollar invested in observability and infrastructure delivers real impact and full traceability.
Who is this service for?
Ideal for organizations that:
- Receive Datadog invoices that grow month after month without understanding exactly what’s driving them.
- Have cloud spend (AWS, Azure, GCP) that’s hard to allocate to teams, products, or cost centers.
- Need to justify Datadog’s ROI to finance leadership or the technology department.
- Are scaling and want to put cost controls in place before spend becomes unmanageable.
- Want to implement showback or chargeback for observability and infrastructure by team or business unit.
What does the service include?
Datadog License Optimization
Current consumption audit We analyze actual usage of every contracted Datadog product: Infrastructure (hosts and containers), APM (instrumented services), Logs (ingested and indexed volume), RUM (sessions), DBM, Synthetics, and Serverless. We identify what’s being used, what’s oversized, and what isn’t being used at all.
Comprehensive consumption dashboard We implement a centralized Estimated Usage dashboard that consolidates consumption across all products in real time, with alerts configured to catch anomalies before they hit the monthly invoice.
Log pipeline optimization Logs are typically the largest component of Datadog spend. We design and configure Observability Pipelines to filter, enrich, and route logs before ingestion: only what has diagnostic value gets indexed, while the rest is archived in low-cost storage (S3, GCS, Azure Blob) with Flex Logs for occasional querying.
APM and trace control We configure Ingestion Controls and Trace Sampling to manage APM trace volume without losing visibility into critical services. We implement differentiated retention rules by environment (production vs. development) and by trace type.
Custom metrics audit Custom metrics are a frequent source of unexpected costs. We identify which custom metrics are being generated, which ones are actually necessary, and which can be eliminated or replaced with standard Agent metrics.
Budget planning We develop consumption forecasting models that anticipate monthly spend based on infrastructure growth and changes in observability scope. This turns the Datadog bill into a predictable, manageable number.
Cloud Cost Management (Extended FinOps)
We implement Datadog Cloud Cost Management (CCM) to unify cloud infrastructure spend with observability data, correlating costs with services, teams, and specific technical decisions.
Unified multi-cloud visibility We integrate AWS spend (via Cost and Usage Report), Azure, and Google Cloud into a single Datadog panel. No need to export data to separate tools: costs live alongside performance metrics in the same context.
Cost allocation by tag We implement a tagging strategy that allocates cloud spend to real business dimensions: team, environment (production/staging/development), service, application, product, or customer. Cost stops being an aggregate number and becomes actionable information.
Identifying idle and oversized resources We detect underutilized instances, orphaned resources (no tag, no owner), stale snapshots, and development-environment services left running by mistake. These findings typically translate into immediate, concrete savings.
Showback and chargeback dashboards We design dashboards that show each team or business area how much their infrastructure is costing. This enables financial-accountability conversations within the organization and makes it easier to implement chargeback models where applicable.
Cost / performance correlation One of Datadog CCM’s most powerful capabilities: linking a service’s spend to its performance metrics. How much does this microservice cost per request? Does the increase in latency justify adding more instances? These questions now have data-backed answers.
Custom and on-prem costs Beyond native cloud providers, Datadog CCM lets you incorporate Kubernetes costs (via OpenCost/Kubecost), external SaaS, or on-premises infrastructure, creating a truly unified view of IT spend.
Anomaly alerts and forecasts We configure cost monitors that alert when spend for a service, team, or account exceeds defined thresholds, allowing you to react before the impact shows up on the month’s invoice.
