Before investing in a centralized telemetry pipeline, most organizations go through a predictable journey. They start by ingesting everything directly into observability platforms, then patch together DIY solutions, and eventually reach a breaking point where complexity, cost, and risk become unmanageable.
If you’re evaluating telemetry pipeline solutions, this guide will help you understand where you are in that journey and whether you’re actually ready for a centralized approach.
The Three Phases Companies Experience
Phase 1: “Just Ingest Everything”
In the early stages, teams send logs, metrics, and traces directly to platforms like Splunk, Datadog, New Relic, Elastic, or CloudWatch. Instrumentation is inconsistent and often proprietary. Small attempts at filtering exist, but nothing systematic.
Symptoms of this phase:
Uncontrolled cost growth as data volume increases, limited visibility into what data is actually being generated, significant duplication across teams, and engineering teams operating in silos.
Phase 2: The DIY Pipeline Era
Before buying a purpose-built solution, organizations almost always experiment with DIY approaches using:
Log shippers and agents (Fluentd, Fluent Bit, Filebeat, Vector, Logstash) for basic reduce and transform operations, though these struggle with validation, governance, and scale.
Message buses (Kafka, Kinesis, Pub/Sub) to buffer and distribute telemetry, which work for fan-out but require custom transformation microservices and carry significant operational overhead.
ETL tools (Fivetran, Airbyte, NiFi, Spark) adapted from data engineering, which introduce high latency and struggle at log/trace scale.
OpenTelemetry Collector for sampling and basic transforms, though configurations become unmanageable at scale and lack visibility into dropped data.
Phase 3: The Internal “Frankenstein” Pipeline
Eventually, organizations combine multiple tools into a fragile architecture: Fluent Bit on hosts, Vector sidecars, a Kafka cluster, several OTel Collectors, Lambda functions for transforms, and legacy Logstash instances. This is the moment when cost, complexity, and operational risk become unacceptable. Worse, this patchwork creates vendor lock-in as teams become dependent on specific platforms and proprietary formats, making it difficult to switch tools or optimize without major re-architecture.”
Common triggers that push organizations toward purpose-built solutions:
Costs often jumping 20–40% year-over-year, Kafka maintenance becoming painful, thousands of agents with inconsistent configurations, security and compliance demanding central governance, leadership pressure to reduce observability costs, and the desire to standardize on OpenTelemetry.
When NOT to Buy a Telemetry Pipeline
Be honest about whether a centralized pipeline makes sense for your organization.
Don’t buy if:
Telemetry flows to a single backend, cost isn’t currently a constraint, DIY changes are cheap and safe to implement, governance requirements are informal, or your organization size still allows trust-based coordination.
Do buy when at least two of these are true:
Explicit cost pressure exists, multiple non-negotiable backends are required, DIY changes feel increasingly risky, governance and compliance are mandatory, or telemetry reliability directly impacts incident response. You need vendor flexibility to route data to multiple destinations or switch backends without re-instrumentation.
What’s Next?
If you’ve recognized your organization in Phases 2 or 3, the next step is understanding what readiness actually looks like. In our next post, we’ll walk through a comprehensive 10-point checklist that will help you prepare for a successful telemetry pipeline implementation—covering everything from defining clear objectives to establishing governance policies.
The difference between a pipeline that reduces cost and accelerates innovation versus one that becomes yet another layer of complexity comes down to preparation. Don’t miss it.
Learn more about Apica Flow telemetry pipeline here: https://www.apica.io/flow/
