How to Reduce Observability Costs by 70%

Observability costs are spiraling out of control. Engineering teams that once paid a few hundred dollars a month now face bills in the tens of thousands—sometimes hundreds of thousands. And the kicker? Most of that data is never looked at.

We've helped companies reduce their observability spend by 70% or more. Not by sacrificing visibility, but by being smarter about what data they collect, how they store it, and what platform they use.

Here's the playbook.

Why Observability Costs Explode

Before we fix the problem, let's understand it. Observability costs grow exponentially for three reasons:

1. Log volume scales with traffic — Every request generates log lines. 10x traffic = 10x logs.
2. Pricing is per-GB — Most vendors charge $0.10-$3.00 per GB ingested. Add cardinality charges for metrics, and costs compound.
3. Retention requirements grow — Compliance often requires 90+ days of retention, multiplying storage costs.

The result? A company processing 100GB/day of logs might pay:

Vendor	Log Ingestion (100GB/day)	30-Day Retention	Monthly Cost
Datadog	$0.10/GB	Included (15 days)	$3,000+
Splunk Cloud	$150/GB (indexed)	Extra cost	$15,000+
New Relic	$0.30/GB (pay-as-you-go)	Included	$9,000+
Self-hosted	Compute costs only	Storage costs	$500-2,000

Let's fix this.

Strategy 1: Intelligent Sampling

Not all data is equally valuable. A health check that runs every 5 seconds doesn't need the same treatment as an error in your payment system.

Head-Based Sampling for Traces

Sample traces at the entry point based on rules:

# OpenTelemetry Collector config
processors:
  probabilistic_sampler:
    sampling_percentage: 10  # Keep 10% of normal traces
    
  tail_sampling:
    decision_wait: 10s
    policies:
      # Always keep errors
      - name: errors
        type: status_code
        status_code: {status_codes: [ERROR]}
      # Always keep slow requests
      - name: slow-requests
        type: latency
        latency: {threshold_ms: 1000}
      # Sample everything else
      - name: default
        type: probabilistic
        probabilistic: {sampling_percentage: 5}

This approach typically reduces trace volume by 80-90% while keeping 100% of the traces that matter.

Log Level Filtering

Debug and info logs are useful during development but rarely in production:

# Keep errors and warnings, sample info/debug
processors:
  filter:
    logs:
      exclude:
        match_type: regexp
        bodies:
          - ".*health.*check.*"
          - ".*DEBUG.*"
        
  transform:
    log_statements:
      - context: log
        conditions:
          - severity_number < 9  # Below WARN
        statements:
          - set(attributes["sampled"], true) where Random() > 0.1

Strategy 2: Data Tiering

Not all data needs to be instantly queryable. Implement a tiered storage strategy:

• Hot tier (0-7 days) — Fast, expensive storage. Full query capabilities.
• Warm tier (7-30 days) — Slower queries acceptable. Compressed storage.
• Cold tier (30-365 days) — Archive. Restore before querying.

With ClickHouse (which powers Qorrelate), this is built-in:

-- Automatic data tiering by age
ALTER TABLE logs MODIFY TTL
  timestamp + INTERVAL 7 DAY TO VOLUME 'hot',
  timestamp + INTERVAL 30 DAY TO VOLUME 'warm',
  timestamp + INTERVAL 90 DAY TO VOLUME 'cold',
  timestamp + INTERVAL 365 DAY DELETE;

Cold storage on S3 costs ~$0.023/GB/month vs. $0.10+/GB/month for hot SSD storage—a 75% reduction for older data.

Strategy 3: Cardinality Control

Metrics cardinality is the silent killer of observability budgets. A single poorly-labeled metric can generate millions of unique time series.

Bad Example

# This creates a unique metric for every request ID!
metrics.increment('request_processed', 
    tags={'request_id': request.id})  # 💀 Infinite cardinality

Good Example

# Fixed set of labels = bounded cardinality
metrics.increment('request_processed',
    tags={
        'service': 'checkout',
        'endpoint': '/api/v1/order',
        'status': '200'
    })

Audit your metrics regularly:

-- Find high-cardinality metrics in ClickHouse
SELECT 
    metric_name,
    uniq(attributes) as unique_combinations
FROM metrics
WHERE timestamp > now() - INTERVAL 1 DAY
GROUP BY metric_name
ORDER BY unique_combinations DESC
LIMIT 20;

Strategy 4: Aggregate Before You Ship

The OpenTelemetry Collector can aggregate data before it leaves your infrastructure, dramatically reducing egress and ingestion costs:

processors:
  # Aggregate metrics every 60 seconds
  batch:
    timeout: 60s
    send_batch_size: 10000
    
  # Combine identical log entries
  groupbyattrs:
    keys:
      - service.name
      - severity
      - message
      
  # Pre-aggregate metrics
  metricstransform:
    transforms:
      - include: http.request.duration
        action: aggregate
        aggregation_type: histogram
        submit_every: 60s

Pre-aggregation can reduce metric volume by 10-50x depending on your cardinality.

Strategy 5: Choose the Right Platform

Platform choice matters more than anything else. Here's a realistic comparison for a mid-sized company (500GB/day logs, 100M metrics, 10M spans):

Platform	Monthly Cost	Notes
Datadog	$25,000-50,000	Per-host + ingestion + add-ons
Splunk Cloud	$40,000-100,000	Indexed GB pricing
New Relic	$15,000-30,000	User-based + data
Grafana Cloud	$8,000-15,000	Usage-based
Qorrelate	$2,000-5,000	ClickHouse-powered, transparent pricing

Why the Difference?

Traditional observability vendors use storage and compute architectures designed in the 2010s. They're optimized for flexibility, not cost.

Modern platforms like Qorrelate use ClickHouse, a columnar database that:

• Compresses log data 10-20x better than Elasticsearch
• Queries billions of rows in milliseconds
• Scales horizontally without vendor lock-in
• Supports OpenTelemetry natively (no translation overhead)

Strategy 6: Set Budgets and Alerts

You can't manage what you don't measure. Set up cost alerts:

# Cost alerting example
alerts:
  - name: log_ingestion_spike
    condition: rate(logs_bytes_ingested[1h]) > 2 * avg(logs_bytes_ingested[7d])
    action: slack_notify
    
  - name: approaching_monthly_budget
    condition: sum(logs_bytes_ingested[this_month]) > 0.8 * budget_bytes
    action: email_finance

Many cost overruns happen because a deployment introduced a new chatty log or a bug caused a retry storm. Catching these early saves thousands.

Real-World Case Study

A Series B startup came to us spending $42,000/month on Datadog. After our optimization:

• Sampling reduced trace volume by 85%
• Log filtering removed 40% of noise
• Cardinality fixes cut metrics by 60%
• Platform migration to Qorrelate

New monthly cost: $8,500—an 80% reduction with no loss in debugging capability.

Action Items for This Week

1. Audit your logs — Find the noisiest sources. Health checks? Debug statements? Eliminate or sample them.
2. Check metric cardinality — Look for unbounded labels. Fix the top 5 offenders.
3. Review retention — Do you really need 90 days of hot storage? Tier your data.
4. Calculate your real cost — Add up all observability vendors. Include hidden fees.
5. Evaluate alternatives — Get quotes from multiple vendors. The market has changed.

Conclusion

Observability is essential. Overpaying for it isn't.

By implementing intelligent sampling, data tiering, cardinality control, and choosing a modern platform, most companies can reduce their observability spend by 50-80% while maintaining—or even improving—their debugging capabilities.

Your infrastructure is growing. Your observability bill doesn't have to grow with it.