Topic starter
11/11/2026 6:04 pm
TL;DR: Telemetry volume keeps driving Datadog ingestion costs up when teams forward everything and filter too late, according to Apica. Upstream control reduces noise before storage, which improves signal quality and limits unnecessary data movement.
Editorial analysis by NHI Mgmt Group, based on content published by Apica: “Why Datadog Users Are Turning to Telemetry Pipelines and How Apica Flow Cuts Costs”.
By the numbers:
- Apica customers who apply pipeline filtering consistently see observability spending drop by 40%.
Key questions
Q: Why does sending every log to an observability platform create waste?
A: Sending every log to an observability platform creates waste when low-value, duplicate or noisy events are stored, indexed and queried at full cost.
Q: How can upstream filtering improve log quality?
A: Upstream filtering improves log quality by dropping duplicate, debug-level or malformed records before they consume ingestion, indexing and retention capacity.
Q: How does a telemetry pipeline reduce observability spend?
A: A telemetry pipeline reduces observability spend by shaping data before it reaches expensive storage and query layers.
Practitioner guidance
- Set ingestion rules before the first full rollout Start with one high-volume log source, define what should be dropped, and validate the reduction in a staging or preview view before expanding to more services.
- Prioritise the noisiest low-value sources first Target cart service logs, background jobs, verbose health checks, and low-utility Kafka events before touching streams that support active investigations.
- Strip unnecessary labels from structured logs Review fields added for old debugging needs and remove those labels upstream so they never count toward storage or ingestion costs.
Bottom line: Telemetry cost problems often start upstream, where teams forward more log data than they can reasonably use or justify.
Glossary terms: Observability, Ingestion Volume, Telemetry pipeline, Log Filtering
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11/11/2026 6:07 pm
Pipeline control is now a governance control, not an optimisation accessory. Once telemetry volumes grow across services and platforms, the decision about what gets ingested becomes a policy decision about cost, retention, and exposure. Teams that leave that choice to downstream tools are not just overspending, they are surrendering control over what enters their observability estate. The practitioner takeaway is to govern telemetry before it becomes billable data.
A question worth separating out:
Q: When should teams filter telemetry before ingestion?
A: Teams should filter telemetry before ingestion when the source emits high-volume noise, when only specific event classes are needed for detection or audit, or when storage and query costs are out of proportion to the data's value. Doing so at the agent, forwarder or collector limits duplicate events, malformed records and unnecessary sensitive data exposure.
👉 Read our full editorial: Upstream telemetry filtering turns observability cost into governance