The era of “collect everything and analyze later” is officially over. As we move through 2026, organizations are finally facing the true cost of observability data at scale - and the numbers are sobering.
The Data Explosion Reality
According to recent industry surveys:
- 38% of companies produce between 500GB and 1TB of telemetry data daily
- 15% generate more than 10TB per day
- Storage and ingestion costs are climbing faster than infrastructure investment
- Organizations aren’t actually using 80% of the data they send to observability systems
We’ve been operating under the assumption that more data equals better insights. In reality, we’ve been paying premium prices for noise.
What Is Adaptive Telemetry?
Adaptive Telemetry is the shift from indiscriminate collection to intelligent filtering. It:
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Analyzes how telemetry is actually used - Which metrics appear in dashboards? Which logs trigger alerts? Which traces are ever queried?
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Classifies data by value - High-value data gets full retention; low-value data gets aggregated, sampled, or dropped
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Recommends optimizations - Rather than requiring manual analysis, it generates actionable recommendations
The Grafana Approach (First Complete Solution)
Grafana recently became the first platform to offer adaptive capabilities across all four observability pillars:
| Component |
What It Does |
Typical Savings |
| Adaptive Metrics |
Aggregates underutilized metrics |
30-50% cost reduction |
| Adaptive Logs |
Drops unused log patterns |
40-60% volume reduction |
| Adaptive Traces |
Intelligent tail sampling |
Capture what matters at 1-10% volume |
| Adaptive Profiles |
Dynamic profiling based on workload |
Variable based on usage |
The Key Insight
Organizations using adaptive telemetry report they can:
- Keep 50-80% less data while retaining full visibility
- Reduce alert fatigue by filtering noise before it reaches dashboards
- Lower MTTR by focusing on signals that matter
- Cut observability costs by 50% without losing visibility
What Changes for Your Team
- Mindset shift: From “capture everything” to “capture what’s valuable”
- Tooling investment: Platforms that can analyze usage patterns
- Process change: Regular reviews of what’s actually being used
- Governance: Clear policies on retention tiers and sampling rates
The Caveat
This isn’t a magic solution. Overly aggressive optimization can:
- Hide critical signals in discarded data
- Increase MTTR for novel issues
- Create compliance gaps
The goal is intelligence, not blind reduction.
What’s your current approach to telemetry optimization? Are you still in “collect everything” mode, or have you started implementing intelligent filtering?
Rachel, this is exactly the economics conversation I’ve been having with the board.
The Observability Budget Squeeze
In my previous thread about Datadog costs, we discussed the 98% savings available from platform migration. Adaptive telemetry adds another dimension: you can also save 50%+ regardless of platform by simply not storing data nobody uses.
The CFO’s Question
My CFO now asks: “If you’re not using 80% of this data, why are we paying for it?”
The honest answer was: “Because we didn’t have tooling to identify what’s valuable.”
Now we do.
What I Present to the Board
| Scenario |
Annual Observability Cost |
Coverage Level |
| Current (collect everything) |
$400K |
100% data, 20% useful |
| Adaptive telemetry |
$180K |
50% data, 90% useful |
| Platform migration + Adaptive |
$60K |
50% data, 90% useful |
The bottom row is our 2026 target.
The Strategic Imperative
Adaptive telemetry isn’t just cost optimization - it’s operational improvement:
- Less noise = faster incident response
- Lower alert fatigue = higher team morale
- Focused data = better insights
The teams that figure this out first will operate more efficiently than competitors who are still paying to store noise.
One Warning
As Rachel mentioned, this isn’t about blind reduction. We need governance frameworks that protect critical signals. More on that in a separate thread.
We’ve been implementing adaptive telemetry for the past quarter. Here’s what the rollout actually looks like.
Phase 1: Usage Analysis (2 weeks)
Before cutting anything, we spent two weeks understanding our current usage:
- Which metrics appear in active dashboards? (Answer: 23%)
- Which logs are ever searched? (Answer: 15%)
- Which traces get queried more than once? (Answer: 8%)
The numbers were sobering. We were storing 10x more data than anyone looked at.
Phase 2: Classification (1 week)
We categorized all telemetry into tiers:
| Tier |
Criteria |
Retention |
Sampling |
| Critical |
In alerts or incident playbooks |
90 days |
100% |
| Active |
In dashboards or monthly queries |
30 days |
100% |
| Occasional |
Queried in last 90 days |
14 days |
50% |
| Unused |
Never queried |
7 days |
10% |
Phase 3: Gradual Rollout (4 weeks)
We didn’t drop everything at once. Each week:
- Reduced retention/sampling for one tier
- Monitored for complaints or gaps
- Adjusted policies based on feedback
Results After 3 Months
- Data volume: Down 62%
- Storage costs: Down 58%
- Query performance: Up 40% (less data to search)
- Alert noise: Down 35%
- Incidents caused by missing data: 0
The Unexpected Win
Our dashboards are faster. Less data means faster queries. Engineers actually use them now because they don’t time out.
Team Resistance
Initially, engineers worried about losing data. We addressed this by:
- Starting with obviously unused data
- Making tier changes reversible
- Showing real cost savings per team
Once they saw the numbers, buy-in followed.
The privacy and compliance angle of adaptive telemetry is underappreciated.
Telemetry as a Liability
In 2026, observability data is increasingly being treated like PII:
- GDPR implications: User identifiers in logs/traces may constitute personal data
- CCPA requirements: California residents can request deletion of their data - including telemetry
- SOC 2 audits: Questions about what data you retain and why
Keeping data you don’t need isn’t just wasteful - it’s a compliance risk.
The “Less Data” Security Advantage
Adaptive telemetry supports security in several ways:
- Reduced attack surface - Less stored data means less data to exfiltrate
- Clearer audit trails - Focused data is easier to review
- Faster breach response - Smaller datasets to analyze during incidents
- Compliance simplification - Deletion requests are easier when you store less
What We’re Implementing
Our security team is working with the platform team to ensure:
- Security-relevant logs are never in the auto-drop category
- Sampling policies don’t affect authentication/authorization traces
- Retention policies meet regulatory minimums
- Data classification includes compliance requirements, not just usage
The “Never Delete” List
Some telemetry should never be subject to adaptive reduction:
- Authentication events
- Access control decisions
- Data export/download actions
- Administrative operations
- Error traces from security-sensitive endpoints
The key is building these protections into the governance framework before enabling adaptive features.