Case Studies
Realistic and high-upside ROI examples for every LogTrim plan
These modeled case studies show how LogTrim reduces billable Datadog log volume before ingestion using log-to-metric aggregation, sampling, deduplication, and pre-ingestion filtering.
Modeled assuming Datadog filters are already well-configured; savings come from reducing what reaches Datadog before ingestion.
S3 storage costs are not included because customers typically pay archive storage directly.
Reduction combines log-to-metric aggregation, sampling, deduplication, and pre-ingestion filtering.
Starter
Starter ROI scenarios
Realistic case
B2B SaaS API platform with 25 engineers and one shared on-call rotation.
Datadog bill before
$4,600
Net monthly savings
$1,449
Datadog volume reduction
38%
Monthly ROI after LogTrim cost
4.8×
485% return
Reduction mix
Log→metric
20%
Sampling
8%
Deduplication
5%
Filtering
5%
Challenge
- High request volume created repetitive success-path logs that rarely helped incident reviews.
- Datadog filters were already configured, but the team still paid ingestion on logs that never needed to reach Datadog.
- The team needed to lower spend without changing existing dashboards or alert workflows.
What they changed
- Converted repetitive HTTP success logs into request-count and latency metrics.
- Applied stable sampling to high-throughput success endpoints while preserving error logs.
- Deduplicated repeated application messages and dropped obvious pre-ingestion noise.
Outcome
- Datadog received fewer low-value logs while existing dashboards remained usable.
- On-call engineers kept full-fidelity errors, warnings, and incident-relevant events.
- Net monthly savings became visible during the first billing cycle.
- Billable Datadog volume moved from 1.3 TB/day to 0.8 TB/day.
- Gross monthly savings: $1,748; LogTrim monthly cost: $299.
High-upside case
API-heavy startup with high traffic, repetitive 2xx logs, and lean infrastructure.
Datadog bill before
$6,200
Net monthly savings
$2,677
Datadog volume reduction
48%
Monthly ROI after LogTrim cost
9.0×
895% return
Reduction mix
Log→metric
28%
Sampling
10%
Deduplication
5%
Filtering
5%
Challenge
- Most log volume came from successful API requests and cache hits rather than incidents.
- Engineers wanted request-rate and latency visibility, not millions of near-identical raw success logs.
- The team needed a strong ROI case before adding another observability tool.
What they changed
- Replaced repetitive success logs with route-level request, latency, and status metrics.
- Sampled normal traffic while preserving failed requests and unusual status patterns.
- Collapsed duplicate logs caused by retries and repeated middleware messages.
Outcome
- The team kept the operational signals they needed with far less Datadog ingestion.
- Successful request visibility moved from raw logs to cheaper metric streams.
- The reduction created a clear payback case for the Starter plan.
- Billable Datadog volume moved from 1.9 TB/day to 1.0 TB/day.
- Gross monthly savings: $2,976; LogTrim monthly cost: $299.
Growth
Growth ROI scenarios
Realistic case
Mid-market ecommerce stack with flash-sale spikes and multi-region workloads.
Datadog bill before
$11,800
Net monthly savings
$4,593
Datadog volume reduction
44%
Monthly ROI after LogTrim cost
7.7×
767% return
Reduction mix
Log→metric
24%
Sampling
10%
Deduplication
5%
Filtering
5%
Challenge
- Traffic surges during promotions produced large ingest spikes and unpredictable monthly bills.
- Duplicate application and edge logs were both reaching Datadog.
- Teams needed fast triage logs in Datadog and long-term raw retention outside Datadog.
What they changed
- Converted common success-path checkout and catalog logs into metrics.
- Enabled duplicate suppression between edge and application request streams.
- Applied intentional sampling on high-throughput success endpoints while keeping errors intact.
Outcome
- Promotion-week cost spikes flattened without reducing alert signal quality.
- Incident searches became cleaner because duplicate and success-path noise was reduced.
- Security and compliance teams could still use archive storage outside Datadog.
- Billable Datadog volume moved from 3.4 TB/day to 1.9 TB/day.
- Gross monthly savings: $5,192; LogTrim monthly cost: $599.
High-upside case
Marketplace platform with bursty search traffic, background jobs, and repeated edge logs.
Datadog bill before
$16,400
Net monthly savings
$8,421
Datadog volume reduction
55%
Monthly ROI after LogTrim cost
14.1×
1406% return
Reduction mix
Log→metric
32%
Sampling
12%
Deduplication
6%
Filtering
5%
Challenge
- Search and listing traffic generated millions of low-value successful request logs.
- Background job success logs created steady baseline volume even outside peak hours.
- The team wanted to preserve production error visibility while trimming routine operational noise.
What they changed
- Converted search, listing, and job-success logs into aggregate metrics.
- Sampled high-volume normal traffic but kept payment, auth, and error flows at full fidelity.
- Deduplicated repeated edge/application pairs and recurring worker messages.
Outcome
- Datadog became focused on incident-relevant logs and aggregate service health metrics.
- The team reduced steady-state volume and peak traffic bursts at the same time.
- The Growth plan delivered a strong payback without requiring an enterprise contract.
- Billable Datadog volume moved from 4.8 TB/day to 2.2 TB/day.
- Gross monthly savings: $9,020; LogTrim monthly cost: $599.
Scale
Scale ROI scenarios
Realistic case
High-scale fintech backend handling payment, ledger, and fraud events.
Datadog bill before
$23,500
Net monthly savings
$10,046
Datadog volume reduction
47%
Monthly ROI after LogTrim cost
10.1×
1006% return
Reduction mix
Log→metric
27%
Sampling
10%
Deduplication
5%
Filtering
5%
Challenge
- Several teams logged full payload snapshots by default, creating expensive low-value volume.
- Duplicate retries and idempotency events crowded incident searches.
- Finance needed predictable observability costs without compromising investigation depth.
What they changed
- Converted repetitive reconciliation and success-state logs into aggregate metric streams.
- Removed duplicate retry events while preserving first-occurrence context.
- Kept error, auth, ledger, and fraud flows at full fidelity in Datadog.
Outcome
- SRE teams saw cleaner searches and faster incident triage.
- High-risk flows remained visible while routine success traffic was reduced.
- Monthly savings stayed meaningful across normal and release-heavy periods.
- Billable Datadog volume moved from 7.2 TB/day to 3.8 TB/day.
- Gross monthly savings: $11,045; LogTrim monthly cost: $999.
High-upside case
Large event-driven SaaS platform with API traffic, workers, queue consumers, and retries.
Datadog bill before
$31,500
Net monthly savings
$17,901
Datadog volume reduction
60%
Monthly ROI after LogTrim cost
17.9×
1792% return
Reduction mix
Log→metric
36%
Sampling
12%
Deduplication
6%
Filtering
6%
Challenge
- Queue consumers and retry loops produced large volumes of repetitive operational logs.
- Most success events were useful as counts and rates, not as individually indexed logs.
- The platform team needed to reduce spend before expanding Datadog coverage to more services.
What they changed
- Converted worker success, queue throughput, and route latency logs into metric streams.
- Used sampling on normal traffic while preserving failed jobs and error traces.
- Collapsed repeated retry and timeout patterns into representative logs with counts.
Outcome
- Datadog volume dropped materially while service-health visibility improved.
- Operational patterns moved into metrics where they were easier to dashboard and alert on.
- The Scale plan provided enough headroom without forcing an enterprise negotiation.
- Billable Datadog volume moved from 9.5 TB/day to 3.8 TB/day.
- Gross monthly savings: $18,900; LogTrim monthly cost: $999.
Enterprise
Enterprise ROI scenarios
Realistic case
Global enterprise SaaS with regulated workloads and dedicated platform engineering.
Datadog bill before
$61,000
Net monthly savings
$25,220
Datadog volume reduction
52%
Monthly ROI after LogTrim cost
3.9×
388% return
Reduction mix
Log→metric
32%
Sampling
8%
Deduplication
5%
Filtering
7%
Challenge
- Regional teams had inconsistent filtering standards and no shared volume-control policy.
- Compliance required long retention, but indexing all logs in Datadog was financially inefficient.
- Executives needed measurable ROI before expanding data-plane coverage globally.
What they changed
- Standardized pre-ingestion routing and trimming policies across regions.
- Converted repetitive service heartbeat and routine success logs into central metrics.
- Used dedicated enterprise capacity with SAML SSO, audit trails, and full archive routing.
Outcome
- Regional teams aligned around one policy model for routing, retention, and Datadog ingestion.
- Dedicated infrastructure gave the platform team predictable throughput and isolation.
- Savings funded additional reliability and security observability initiatives.
- Billable Datadog volume moved from 14.5 TB/day to 7.0 TB/day.
- Gross monthly savings: $31,720; LogTrim monthly cost: $6,500.
High-upside case
Large regulated platform with 25 TB/day of logs, heavy service traffic, and global audit needs.
Datadog bill before
$110,000
Net monthly savings
$54,000
Datadog volume reduction
60%
Monthly ROI after LogTrim cost
4.5×
450% return
Reduction mix
Log→metric
38%
Sampling
10%
Deduplication
6%
Filtering
6%
Challenge
- Datadog filters were already mature, but the company still paid ingestion on logs that were never indexed.
- Platform leadership wanted Datadog to receive only index-worthy data while preserving full-fidelity archives.
- Security, compliance, and SRE teams needed separate policies without losing central governance.
What they changed
- Sent only index-worthy logs to Datadog while keeping full archival coverage outside Datadog.
- Converted high-volume success and heartbeat traffic into metrics before Datadog ingestion.
- Applied per-destination policies with audit trails, SAML SSO, and dedicated regional data planes.
Outcome
- Datadog shifted from a raw ingestion layer to a focused indexing and investigation layer.
- The enterprise kept full retention while materially reducing billable Datadog ingestion.
- The contract delivered large absolute savings while still leaving LogTrim room for dedicated infrastructure and support.
- Billable Datadog volume moved from 25.0 TB/day to 10.0 TB/day.
- Gross monthly savings: $66,000; LogTrim monthly cost: $12,000.