What Is a Telemetry Pipeline? Everything You Need to Know

From raw data to useful insight

As digital infrastructure grows more distributed and complex, observability becomes essential. But collecting logs, metrics, and traces isn't enough on its own. You need a way to route, process, and optimize that data before it overwhelms your observability stack or your budget.

A telemetry pipeline sits upstream of tools like Datadog, Splunk, and New Relic, giving teams a way to automatically manage, filter, and optimize telemetry data before it reaches billing meters.

This guide covers what a telemetry pipeline is, how it works, where it fits in observability architectures, and how telemetry pipeline management tools like Sawmills simplify the process.

What is a telemetry pipeline? The short version

A telemetry pipeline is a configurable system that collects, processes, and routes observability data from sources like services and applications to destinations such as monitoring or storage tools.

It is the backbone of modern observability. Rather than collecting and sending everything to your backend systems, a well-designed pipeline filters, deduplicates, enriches, and selectively routes telemetry based on value.

For teams using OpenTelemetry, this often involves the OpenTelemetry Collector, which standardizes how telemetry flows through different stages and ensures compatibility with vendors and systems alike.

How a telemetry pipeline works

The typical telemetry pipeline includes several stages:

Data ingestion: Metrics, logs, and traces are generated by instrumented services or collected via agents.

Processing: The pipeline transforms and filters data: aggregation of high-volume metrics, sampling traces, dropping noisy log lines, or throttling spiking data before it causes overages.

Routing: Data is then sent to various backends, such as Prometheus for metrics, Elasticsearch for centralized logging, Datadog for APM, or a SaaS observability platform for traces. The pipeline controls what you're sending to each destination, and more importantly, what you're not sending.

Optimization: Advanced pipelines go beyond routing. They handle cost optimization automatically by identifying unnecessary data, detecting anomaly patterns in volume, recommending reductions, and enforcing policies that keep spending under control. For example, a pipeline optimizing Datadog usage might detect that 60% of log events being sent are debug-level and never queried.

These stages are often managed through configuration files and policies. Telemetry pipeline management platforms like Sawmills offer a UI and AI-powered recommendations to manage them at scale across multiple environments.

Where are telemetry pipelines used?

Telemetry pipelines are used anywhere observability is needed across complex systems. They're most common in environments like:

• Kubernetes clusters where ephemeral workloads create unpredictable telemetry volume across multiple clusters.
• Multi-cloud or hybrid architectures that need standardized, cross-platform observability and data governance across providers.
• SaaS platforms with high SLAs and globally distributed traffic that must handle massive logging and metrics volume.
• Security operations requiring centralized log management, audit trails, and compliance standards.
• Organizations using Datadog, Splunk, or New Relic that want to reduce observability costs without switching tools or losing visibility.

Who benefits from telemetry pipelines?

Anyone managing system observability benefits from a telemetry pipeline:

• SREs get consistent, well-structured data that aligns with SLOs across the full observability stack.
• DevOps teams simplify pipeline configuration, reduce noise, and control monitoring costs.
• Platform engineers manage multiple pipelines and collectors from a single pane, enforcing standards across teams.
• Cloud architects ensure observability data flows meet compliance, governance, and performance needs.
• Security engineers gain centralized control of audit logs and traceable workflows.

Challenges of managing a telemetry pipeline

Telemetry pipelines can introduce their own complexity. Common challenges include:

• Overcollection: Without filtering, telemetry pipelines become expensive and noisy. Teams often don't realize how much unnecessary data they're shipping until the bill arrives.
• High cardinality: Labels and dimensions explode time-series cardinality, especially in Prometheus and Datadog. Pipelines need to handle high cardinality metrics before they reach your backends.
• Cost overruns: Observability spending can spike without warning. Organizations using Datadog, Splunk, or similar platforms need cost optimization solutions that automatically throttle spiking volumes and help lower their bill. Splunk license overages alone cost some enterprises millions annually.
• PII exposure: Logs and traces can unintentionally contain personal or sensitive data, requiring data governance policies at the pipeline level.
• Vendor lock-in: Hardcoded exporters and formats make switching to alternatives costly. OpenTelemetry-based pipelines built on open standards reduce this risk.

Best practices for telemetry pipelines

To get the most from your telemetry pipeline:

• Standardize your instrumentation. Use OpenTelemetry to ensure consistent formats and compatibility across your observability stack.
• Filter early. Drop, sample, or use aggregation before data hits your backends. This is the single biggest lever for cost optimization.
• Separate value from volume. Route high-value telemetry to premium backends and low-value data to cheaper storage, or drop it entirely.
• Automate policy management. Tools like Sawmills let you define and enforce rules that automatically handle spiking volumes, throttle noisy sources, and keep spending predictable.
• Simplify configuration. Choose platforms with no-code interfaces that let DevOps teams manage multiple pipelines without writing YAML or maintaining custom scripts.

Telemetry pipeline vs observability platform: what's the difference?

Telemetry pipelines and observability platforms solve different problems.

An **observability platform** (Datadog, Splunk, New Relic, Dynatrace, Grafana) is where you analyze data: dashboards, alerts, queries, and troubleshooting. These are your destinations.

A **telemetry pipeline** sits upstream of those platforms. It collects, processes, and routes data before it reaches your observability tools. Think of it as the control layer between your applications and your monitoring stack.

The top telemetry pipeline solutions include open-source options like the OpenTelemetry Collector (OTel Collector) and commercial platforms like Sawmills, Cribl, Edge Delta, Bindplane, and Chronosphere. The differences between these tools come down to how they handle cost optimization, ease of configuration, support for multiple destinations, and whether they offer AI-powered suggestions for reducing wasteful data. Cribl focuses on general routing, Edge Delta on edge processing, Honeycomb on tracing-first observability, and Sawmills on AI-powered optimization. Choosing between them depends on your use case and which features matter most to your company.

Organizations that use a telemetry pipeline alongside their existing observability platform typically reduce costs by 40-80% without losing visibility, because the pipeline automatically filters, aggregates, and routes data based on its actual value.

How to reduce observability costs with a telemetry pipeline

The biggest driver behind telemetry pipeline adoption is cost. Observability spending at mid-market and enterprise companies has grown 30%+ year-over-year, driven by increasing data usage and per-GB pricing from vendors like Datadog and Splunk. Reducing this spending without changing your existing tools requires strategies that target data volume before ingestion.

A telemetry pipeline tackles this by:

• Dropping low-value data before it's ingested. Verbose debug logging, duplicate attributes, and noisy health checks can account for 50-70% of volume.
• Aggregation at the edge. Instead of shipping every raw metric, aggregate into summaries that preserve the signal without the cost.
• Smart routing. Send high-value logs to your premium observability platform and route the rest to low-cost storage like S3 for compliance.
• Automated policies. Define spend thresholds and let the pipeline automatically throttle or sample when volumes spike. This is one of the most effective methods for enterprises to save on observability costs.

Sawmills takes this further with AI-powered optimization that identifies wasteful data patterns automatically and finds optimization opportunities you'd miss manually: which logs to summarize, which metrics to aggregate, which tracing data to sample. Instead of sending everything to your observability platform and paying for usage you don't need, Sawmills gives you strategies for reducing spending while keeping full visibility. It can also help lower Splunk license costs or Datadog ingest fees by handling volume reduction upstream.

Forget pipelines. Smart telemetry management is the future.

Sawmills adds intelligence and control on top of traditional telemetry pipelines. With a real-time Telemetry Explorer, users can visualize data flows, identify inefficiencies, and apply fixes without writing YAML. Built-in processors let you filter logs, drop cardinality-heavy metrics, or standardize formats. Sawmills also includes AI-powered recommendations that detect waste and suggest actions before data leaves your system.

You get compatibility with OpenTelemetry, multi-pipeline support, and automated policy enforcement to keep telemetry aligned with your system's value.

Frequently asked questions

What is the difference between a telemetry pipeline and an observability pipeline?

They're the same thing. "Observability pipeline" and "telemetry pipeline" are used interchangeably. Both refer to the system that collects, processes, and routes logs, metrics, and traces from your applications to your monitoring and analytics tools.

Can a telemetry pipeline reduce my Datadog or Splunk costs?

Yes. A telemetry pipeline sits before Datadog, Splunk, or any observability platform and controls what data gets ingested. By filtering unnecessary logs, using aggregation on metrics, and sampling traces, organizations typically cut observability costs by 50-80% without losing the visibility they need.

What are the top telemetry pipeline tools?

The leading options include Sawmills (AI-powered, built on OpenTelemetry), Cribl (general-purpose data routing), Edge Delta (edge processing), Bindplane (Google-backed, compatible with multiple backends), Vector (open-source, high-performance routing), and the OpenTelemetry Collector (OTel Collector). A comparison of these tools comes down to cost optimization capabilities, ease of configuration, support for multiple environments, and whether the tool can automatically recommend and implement changes. Sawmills differentiates by using AI to find optimization opportunities and make pipeline management easier than manual configuration.

How is a telemetry pipeline different from a SIEM?

A SIEM (Security Information and Event Management) focuses on security log analysis, threat detection, and compliance. A telemetry pipeline is broader: it handles all observability data (logs, metrics, traces) and focuses on routing, optimization, and cost reduction. That said, telemetry pipelines often feed data into SIEMs, and can reduce SIEM licensing costs by pre-processing and filtering data before sending it, saving on ingest-based pricing.

Do I need a telemetry pipeline if I already use OpenTelemetry?

OpenTelemetry provides the standards and instrumentation layer. The OpenTelemetry Collector handles basic routing and processing. But for cost optimization, governance, policy enforcement across multiple teams, and AI-powered recommendations, a telemetry pipeline management platform like Sawmills on top of OpenTelemetry fills the gap.

How does a telemetry pipeline handle high cardinality metrics?

High cardinality is one of the biggest cost drivers in observability. A good telemetry pipeline can automatically detect high-cardinality dimensions, apply aggregation or label reduction, and throttle spiking metrics before they reach your backends. Sawmills uses AI to identify cardinality issues and recommend fixes with one-click implementation.

Can I integrate a telemetry pipeline with my existing tools?

Yes. Modern telemetry pipelines are designed to work with your current observability stack. Whether you're sending data to Datadog, Splunk, Elastic, Grafana, Honeycomb, Chronosphere, or others, the pipeline sits between your applications and those destinations. OTel-based pipelines are vendor-neutral by design, so you can switch or add backends without changing your instrumentation or handling complex migrations.

What are some tips for getting started with a telemetry pipeline?

Start by identifying your highest-volume data sources, since these are where you'll see the biggest savings. Run a comparison of what you're currently sending versus what's actually being used in dashboards and alerts. Most organizations find that 50-70% of their telemetry data is never queried. From there, implement adaptive sampling, log summarization, or routing strategies to cut costs while preserving the data that matters.

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