Cost Clinic: Consolidating the Observability Stack in 2025

Observability budgets ballooned during the 2020 to 2023 boom as teams layered tools for logs, metrics, traces, and user analytics. With cloud infrastructure costs under the microscope and data growth fueled by AI workloads, 2025 is the year finance leaders demand consolidation. This clinic outlines how to benchmark spend, rationalize tooling, and maintain coverage without burning engineering time.

Executive Overview

• Companies with USD 100M to 500M ARR now spend 8 to 12 percent of cloud budgets on observability, according to the latest Grafana Labs survey.
• Redundant pipelines inflate costs. Many teams ingest the same logs and traces into two or three platforms just to satisfy niche dashboards.
• Consolidation must balance SRE, security, and product analytics requirements. Cutting too aggressively risks compliance gaps and slower incident response.
• A structured 90-day program can trim annual run-rate by 20 to 35 percent while improving signal quality.

Baseline Assessment

Start with a hard inventory and a benchmark that finance and engineering both trust.

1. Catalogue tools and contracts. List vendors, modules enabled, contract value, renewal dates, and overage terms.
2. Quantify telemetry volume. Capture daily GB ingested for logs, metrics, traces, and profiling. Split by environment and business unit.
3. Map critical dashboards and alerts. Tie each to the owning team and the incidents they support. Delete or consolidate stale alerts.
4. Benchmark unit costs. Calculate USD per GB ingested, per host, per seat, and per query. Compare against industry benchmarks like the FinOps Foundation monitoring study.
5. Flag compliance dependencies. Note controls covered (for example, SOX change logging, PCI DSS monitoring) so you do not break evidence collection.

Consolidation Playbook

Step 1: Align Requirements

• Stakeholders: SRE, platform engineering, security, compliance, product analytics, finance.
• Use cases: Incident response, release validation, performance tuning, capacity planning, user behavior analytics.
• Retention policies: Define how long each telemetry type is needed (hot, warm, cold tiers). Many teams over-retain debug logs.
• Success metrics: Target ingestion reduction, alert signal-to-noise, mean time to detect (MTTD), compliance coverage.

Step 2: Evaluate Tooling Options

Score each option against requirements and total cost of ownership. Model migration costs, including engineering hours and potential dual-running periods.

Step 3: Migration and Implementation

1. Design ingestion pipelines. Decide which agents, collectors, or sidecars standardize telemetry. Use OpenTelemetry collectors for cross-vendor routing.
2. Pilot on a golden service. Choose an application with steady traffic and engaged owners. Run dual pipelines for two to four weeks while comparing alert fidelity.
3. Automate provisioning. Codify observability resources (dashboards, alerts, retention policies) as code using Terraform providers or vendor APIs.
4. Phase rollout. Cut over by environment (staging, canary, production) or by service tier (customer-facing, internal). Keep rollback procedures ready.
5. Update runbooks. Document new dashboards, alert thresholds, and response flows. Train on-call teams before decommissioning legacy tools.

ROI Model

Build a simple spreadsheet or pull FinOps data into a BI tool. Focus on these components:

• Current spend: Annualized subscription fees plus overages.
• Projected spend: New license cost, storage, compute for self-hosted components.
• Migration investment: Engineering hours, consulting, dual-running period, deprecation effort.
• Savings timeline: Month-by-month cash impact, highlighting break-even data.
• Intangible benefits: Reduced tool sprawl, faster onboarding, cleaner audit evidence.

Example: A SaaS provider ingesting 80 TB of logs per month into two commercial platforms at USD 0.23 per GB can cut to one platform plus self-hosted cold storage. Result: USD 176K monthly reduction after six weeks of dual running and a 400 hour engineering investment.

Case Notes

• Growth-stage data platform consolidating on Datadog: Reduced monthly spend 28 percent by decommissioning a legacy logs platform and moving cold storage to S3 with Athena queries. Key enabler was automated log sampling tiers built with Lambda.
• Security-led consolidation using OpenTelemetry: A fintech combined traces, logs, and metrics into a self-managed stack on Grafana Cloud with per-tenant dashboards. Compliance evidence improved because dashboards now map directly to SOX controls.
• Hybrid model for AI workloads: An AI infrastructure startup kept Datadog for production customer SLAs but moved training telemetry to an OpenTelemetry plus ClickHouse pipeline, saving USD 1.2M annually.

Action Items and Checklist

• [ ] Complete telemetry inventory and unit cost benchmark.
• [ ] Align stakeholders on success metrics and compliance requirements.
• [ ] Choose consolidation approach and secure budget for migration.
• [ ] Stand up pilot environment and run dual telemetry for at least two weeks.
• [ ] Automate dashboards and alerts as code, then roll out by environment.
• [ ] Decommission redundant agents and shut down legacy ingestion to avoid double billing.
• [ ] Update governance policy to review observability spend every quarter.

Timeline Template

Sources and Further Reading

• Grafana Labs: State of Observability 2025
• Datadog: Observability Cost Optimization Guide
• FinOps Foundation Monitoring and Observability Playbook