Implementing OpenTelemetry successfully is less about “turning it on” and more about making a set of deliberate architectural choices: where telemetry is generated, how it is transported, what data you keep, and how you operate the pipeline over time. For IT administrators and system engineers, the value proposition is straightforward: a consistent, vendor-neutral way to collect traces, metrics, and logs across heterogeneous stacks, without rewriting your monitoring strategy every time a team changes languages or tooling.
OpenTelemetry (often abbreviated OTel) is an open standard and a set of SDKs, APIs, and components for producing and exporting telemetry. It standardizes how services describe and emit telemetry signals, and it provides a reference pipeline component—the OpenTelemetry Collector—to receive, process, and export that telemetry to one or more backends.
This article walks through a practical implementation approach. It starts with core concepts and design decisions, then moves into step-by-step deployment patterns and instrumentation, and finally covers operational controls (sampling, cardinality, security, scaling, and governance). Along the way, you’ll see real-world scenarios that mirror typical enterprise environments: a Kubernetes microservices cluster, a hybrid Windows/Linux estate with .NET and Java services, and a migration from an APM agent-centric model to a collector-centric model.
What you get with OpenTelemetry (and what you still need to design)
OpenTelemetry gives you three signal types—traces, metrics, and logs—and a shared model for resources and attributes. A trace represents an end-to-end request broken into spans (timed operations). Metrics represent aggregated measurements over time (counters, gauges, histograms). Logs are timestamped events, often unstructured, that become more useful when correlated with traces and metrics.
What OpenTelemetry does not give you by default is a finished “monitoring system.” You still need:
A telemetry backend or several backends (for example: Prometheus for metrics, Grafana Tempo or Jaeger for traces, and Elasticsearch/OpenSearch/Loki for logs). OpenTelemetry can export to many, but you must choose.
A pipeline architecture. You must decide where to deploy collectors (agent vs gateway), how to route data, and how to apply processing (sampling, filtering, redaction, batching).
Governance. You need conventions for naming, attribute usage, and service identity, otherwise your telemetry becomes high-cardinality noise.
A useful mental model is that OpenTelemetry standardizes the “edges” (how telemetry is produced and shipped) and the collector standardizes the “middle,” but you still define the “destination” and the operational policies.
Core building blocks: SDKs, OTLP, and the OpenTelemetry Collector
A typical OpenTelemetry implementation consists of instrumented applications (using SDKs or auto-instrumentation), a transport protocol (usually OTLP), and collectors that forward telemetry to storage/analysis backends.
The OpenTelemetry Protocol (OTLP) is the default transport format. OTLP can run over gRPC or HTTP. In practice, OTLP/gRPC is common for high-throughput internal traffic, while OTLP/HTTP can be easier through proxies and firewalls. Many organizations standardize on OTLP/gRPC inside clusters and OTLP/HTTP for cross-network or DMZ paths.
The OpenTelemetry Collector is a vendor-neutral service that can receive telemetry (OTLP and other protocols), process it (batching, sampling, filtering, resource detection, attribute manipulation), and export it to one or more destinations. It exists in two primary deployment modes:
Agent: runs close to the workload, typically as a DaemonSet on Kubernetes nodes or as a service on VMs. It reduces the blast radius of application egress, simplifies local traffic, and can perform light processing.
Gateway: a centralized collector tier (Deployment/StatefulSet or a VM pool) that receives telemetry from agents and apps. It’s the right place for heavier processing, cross-tenant routing, authentication, and backpressure management.
Most production rollouts end up with a hybrid: agent collectors for local reception and a gateway layer for policy and export.
Designing your telemetry architecture before you instrument anything
Before adding SDKs or deploying collectors, decide how data should flow and how you will operate it. These choices are hard to change later because they affect sampling, attribute conventions, and network paths.
Choose your initial backend targets and define success criteria
OpenTelemetry is backend-agnostic, but your first implementation needs a destination. Many teams start with existing tooling:
Metrics: Prometheus or a managed Prometheus-compatible service.
Traces: Jaeger, Grafana Tempo, or a managed tracing backend.
Logs: Loki, Elasticsearch/OpenSearch, or a managed logging platform.
Success criteria should be operational. For example: “We can trace 95% of requests across API gateway, auth service, and payments service,” or “We can alert on p95 latency and error rate per service and correlate to a trace sample within 2 minutes.” Criteria like these force you to implement correlation and consistent service identity early.
Decide on agent vs gateway collection (and why most people use both)
If you run Kubernetes, an agent collector per node (DaemonSet) is usually the simplest way to receive OTLP from pods without opening egress to external systems. The agent can forward to a gateway service inside the cluster or to an external gateway.
If you run VMs, you can run a collector as a systemd service (Linux) or Windows service. In hybrid estates, a gateway collector becomes the “hub” where you enforce policy consistently.
A real-world pattern that works well is:
Applications export OTLP to a local agent collector.
Agent collectors apply batching and light enrichment (resource detection).
Gateway collectors apply sampling, filtering/redaction, routing by environment/tenant, and export to backends.
This separation matters because sampling decisions ideally happen after the system has enough context to make good choices (for example, keep error traces and slow traces, sample the rest).
Establish service identity and attribute conventions up front
OpenTelemetry uses resource attributes (such as service.name, service.version, and deployment.environment) to identify where telemetry came from. If you let every team invent these, your dashboards and queries become unreliable.
Standardize at least:
service.name: stable, human-readable service identifier (avoid including replica IDs or dynamic values).
deployment.environment: prod, stage, dev (or your equivalents).
service.version: a build version or git SHA.
cloud.region / cloud.availability_zone where relevant.
Also decide how you will name spans and metrics. OpenTelemetry provides semantic conventions (e.g., HTTP attributes, database attributes). Using them consistently makes cross-service queries possible.
Plan for data volume: sampling, metric cardinality, and log strategy
Telemetry cost and performance issues rarely come from “too many services” and almost always come from “too much data per service.” Plan these controls early:
Tracing sampling: choose a default strategy (head-based probabilistic vs tail-based) and define keep rules for errors and latency outliers.
Metric cardinality: avoid unbounded label values (user IDs, request IDs). Decide what attributes you allow on high-volume metrics.
Logs: decide whether OpenTelemetry will carry logs end-to-end, or whether you will keep log shipping separate and focus OTel on trace correlation (injecting trace IDs into logs).
A common, effective approach is to implement traces and metrics first, then decide on logs once trace-log correlation is stable.
Deploying the OpenTelemetry Collector: a practical baseline
A collector configuration is a pipeline made of receivers, processors, and exporters, plus optional extensions. You can run the “core” collector distribution or a vendor distribution. For most administrators, the upstream collector is a good starting point because it is transparent and well-documented.
Baseline collector configuration (gateway)
The following example shows a gateway collector that receives OTLP (gRPC and HTTP), batches data, enforces memory limits, and exports traces to Jaeger (via OTLP), metrics to Prometheus remote write, and logs to Loki. Adjust exporters to match your backends.
# otelcol-gateway.yaml
receivers:
otlp:
protocols:
grpc:
endpoint: 0.0.0.0:4317
http:
endpoint: 0.0.0.0:4318
processors:
memory_limiter:
check_interval: 1s
limit_mib: 2048
spike_limit_mib: 512
batch:
send_batch_size: 8192
timeout: 5s
exporters:
otlp/jaeger:
endpoint: jaeger-collector.observability.svc.cluster.local:4317
tls:
insecure: true
prometheusremotewrite:
endpoint: http://prometheus-remote-write.monitoring.svc:9090/api/v1/write
loki:
endpoint: http://loki.monitoring.svc:3100/loki/api/v1/push
service:
pipelines:
traces:
receivers: [otlp]
processors: [memory_limiter, batch]
exporters: [otlp/jaeger]
metrics:
receivers: [otlp]
processors: [memory_limiter, batch]
exporters: [prometheusremotewrite]
logs:
receivers: [otlp]
processors: [memory_limiter, batch]
exporters: [loki]
This is intentionally minimal. You will almost certainly add resource detection and attribute controls later, but starting minimal makes validation simpler.
Kubernetes deployment pattern: agent + gateway
In Kubernetes, the operational sweet spot is often an agent collector as a DaemonSet and a gateway collector as a Deployment.
The agent receives OTLP from local pods and forwards to the gateway. This reduces east-west traffic and isolates application pods from backend endpoints. It also lets you enforce mTLS and auth between agent and gateway if you need it.
An agent configuration typically looks like:
yaml
# otelcol-agent.yaml
receivers:
otlp:
protocols:
grpc:
endpoint: 0.0.0.0:4317
http:
endpoint: 0.0.0.0:4318
processors:
memory_limiter:
check_interval: 1s
limit_mib: 512
spike_limit_mib: 128
batch:
timeout: 2s
exporters:
otlp:
endpoint: otelcol-gateway.observability.svc.cluster.local:4317
tls:
insecure: true
service:
pipelines:
traces:
receivers: [otlp]
processors: [memory_limiter, batch]
exporters: [otlp]
metrics:
receivers: [otlp]
processors: [memory_limiter, batch]
exporters: [otlp]
logs:
receivers: [otlp]
processors: [memory_limiter, batch]
exporters: [otlp]
From here, you deploy the agent as a DaemonSet with host networking or cluster networking. Most teams keep it simple and use a ClusterIP service per node via DaemonSet + hostPort, or they run it as a sidecar for sensitive workloads. The right choice depends on your network policies and whether workloads can reach a node-local endpoint reliably.
VM deployment pattern: systemd service on Linux
For Linux VMs, running the collector as a systemd service is straightforward. Many orgs standardize on “agent mode” collectors on VMs, forwarding to a central gateway.
A high-level installation approach:
1) Place the collector binary and configuration in /etc/otelcol.
2) Create a systemd unit that starts the collector.
ini
# /etc/systemd/system/otelcol.service
[Unit]
Description=OpenTelemetry Collector
After=network-online.target
Wants=network-online.target
[Service]
Type=simple
User=otelcol
Group=otelcol
ExecStart=/usr/local/bin/otelcol --config /etc/otelcol/config.yaml
Restart=on-failure
RestartSec=5
LimitNOFILE=65536
[Install]
WantedBy=multi-user.target
Once deployed, you can validate connectivity by checking collector logs and ensuring the gateway sees incoming traffic. For administrators, the operational win is that VM workloads can standardize on OTLP export without learning the details of each backend.
Windows deployment pattern: Windows service
On Windows, you can run the collector as a Windows service using a service wrapper or native service support depending on how you package it. The core operational requirement is the same: keep the collector configuration in a predictable location, configure it to start automatically, and restrict who can modify it.
PowerShell can be used to create a service if you’re using a wrapper executable:
powershell
New-Service -Name "otelcol" `
-BinaryPathName "C:\otelcol\otelcol.exe --config C:\otelcol\config.yaml" `
-DisplayName "OpenTelemetry Collector" `
-StartupType Automatic
Start-Service otelcol
In hybrid estates, Windows-based .NET services can export OTLP to a local collector, which then forwards to the gateway. This reduces firewall complexity because only the collector needs outbound rules.
Instrumentation strategy: start with auto-instrumentation where safe
Instrumentation is how telemetry gets generated. You have two broad options:
Manual instrumentation via OpenTelemetry SDK APIs. This provides the most control, but it requires code changes and a consistent approach across teams.
Auto-instrumentation (agent-based or runtime-based) for supported languages and frameworks. This can generate high-value HTTP, database, and messaging spans with minimal changes.
A pragmatic rollout plan is to begin with auto-instrumentation for a small set of services to validate the pipeline, then selectively add manual spans and metrics in the parts of the codebase where you need business context.
Service-level configuration via environment variables
Across languages, OpenTelemetry supports a consistent set of environment variables. Standardizing these for your estate makes day-2 operations easier.
Key variables you will use often:
OTEL_SERVICE_NAME: sets service.name.
OTEL_RESOURCE_ATTRIBUTES: sets resource attributes such as environment, region, and version.
OTEL_EXPORTER_OTLP_ENDPOINT: where to send OTLP data (collector endpoint).
OTEL_EXPORTER_OTLP_PROTOCOL: grpc or http/protobuf.
OTEL_TRACES_SAMPLER: sampling strategy (for example, parentbased_traceidratio).
OTEL_TRACES_SAMPLER_ARG: sampler parameter (for example, 0.1).
For Kubernetes, this typically becomes part of your Deployment manifests or Helm values.
Example snippet:
yaml
env:
- name: OTEL_SERVICE_NAME
value: "orders-api"
- name: OTEL_RESOURCE_ATTRIBUTES
value: "deployment.environment=prod,service.version=2026.01.15"
- name: OTEL_EXPORTER_OTLP_ENDPOINT
value: "http://otelcol-agent.observability.svc.cluster.local:4318"
- name: OTEL_EXPORTER_OTLP_PROTOCOL
value: "http/protobuf"
- name: OTEL_TRACES_SAMPLER
value: "parentbased_traceidratio"
- name: OTEL_TRACES_SAMPLER_ARG
value: "0.2"
Even if you later move to tail sampling in the gateway, keeping deterministic head sampling for baseline control can still be useful in high-volume systems.
Example scenario 1: Kubernetes microservices with an ingress and two downstream services
Consider an e-commerce platform in Kubernetes: frontend, orders-api, and payments-api, fronted by an ingress controller. The platform’s initial pain point is intermittent latency spikes. Existing metrics show p95 latency rising, but the team can’t pinpoint whether the delays are in the ingress, the orders service, database calls, or payment provider requests.
With OpenTelemetry, you implement:
Ingress spans (via ingress controller integration if available, or via the first service layer).
Auto-instrumentation for HTTP and database calls in orders-api.
Manual spans around the external payment call in payments-api, capturing provider name and response code (carefully avoiding sensitive content).
Once traces flow to the backend, the team can follow a single request from ingress through orders to payments, and see the slow segment. The key operational point is that you standardize service.name and propagate context (trace IDs) across HTTP calls. That context propagation is what turns “per-service monitoring” into “end-to-end monitoring.”
Context propagation and why it is the difference between isolated spans and useful traces
Distributed tracing only works if services propagate trace context between each other. Context is typically carried in HTTP headers (for example, W3C Trace Context using traceparent and tracestate). Most OpenTelemetry instrumentations use W3C Trace Context by default.
For administrators, the key is to ensure that:
Your service mesh, gateway, or proxies do not strip trace headers.
Cross-origin and security policies do not block required headers.
Any message queue or asynchronous processing system is instrumented so trace context follows the message.
When context is lost, traces fragment, and you see separate traces per service rather than one end-to-end trace. This problem often appears first at boundaries: ingress to service, service to legacy system, or synchronous to asynchronous transitions.
Sampling in production: keeping the traces you need without melting your backend
Sampling is how you control trace volume. There are two common categories:
Head-based sampling: decide at the start of a trace whether to sample it. This is simple and efficient but can miss rare errors.
Tail-based sampling: decide after seeing the whole trace (or parts of it) whether to keep it. This is more powerful for keeping errors and slow traces but requires more collector resources and introduces buffering.
For many production environments, a common progression is:
Start with head-based sampling in SDKs at a moderate rate (for example, 10–20%) to validate instrumentation and pipeline.
Move to tail-based sampling in the gateway collector once stable, to capture all errors and high-latency outliers while reducing noise from normal traffic.
Tail sampling in the gateway collector
Tail sampling is configured in the collector with a tail_sampling processor (when available in your collector build). Policies can keep error traces, slow traces, or traces matching attributes.
A conceptual example:
yaml
processors:
tail_sampling:
decision_wait: 10s
num_traces: 50000
expected_new_traces_per_sec: 2000
policies:
- name: keep-errors
type: status_code
status_code:
status_codes: [ERROR]
- name: keep-slow
type: latency
latency:
threshold_ms: 1000
- name: probabilistic-rest
type: probabilistic
probabilistic:
sampling_percentage: 5
This configuration illustrates the operational goal: keep what matters, reduce what doesn’t. You still need to size the gateway for buffering and ensure you have backpressure controls.
Metrics with OpenTelemetry: choosing between pull and push models
Metrics in OpenTelemetry can be exported in two broad patterns:
Pull model: Prometheus scrapes metrics endpoints. In OpenTelemetry deployments, this often means running a collector with a Prometheus exporter that exposes /metrics for scraping.
Push model: the collector remote-writes or pushes metrics to a backend.
Administrators often prefer the pull model inside clusters because it aligns with existing Prometheus operations. Push models can be easier across network boundaries and for managed services.
A key point is that OpenTelemetry metrics are not “just Prometheus metrics.” They have their own data model, but the collector can translate/export them. When translating, be mindful of label cardinality and naming.
Controlling metric cardinality
Cardinality refers to the number of unique label combinations. High-cardinality metrics can overwhelm Prometheus-like systems. OpenTelemetry makes it easy to add attributes, so governance matters.
As a baseline policy:
Never add user IDs, email addresses, session IDs, or request IDs as metric attributes.
Be cautious with URL paths. Prefer route templates (e.g., /orders/{id}) over raw paths.
Keep a tight set of allowed attributes for high-volume metrics like HTTP request duration.
If you need per-user debugging, use traces and logs, not metrics.
Logs and trace correlation: making logs navigable without reinventing logging
Logs are often where on-call engineers start, but logs alone can be hard to navigate in distributed systems. OpenTelemetry can help by correlating logs with traces using trace IDs and span IDs.
You can approach logs in two ways:
Continue using your existing log shipper (Fluent Bit, Filebeat, Windows Event Forwarding) but update log formats to include trace context.
Adopt OpenTelemetry logging and export logs via OTLP to the collector.
The first approach is frequently easier operationally because it doesn’t disrupt logging pipelines. You still get correlation as long as trace context is injected into log entries.
In practice, many teams standardize on adding fields like trace_id and span_id to structured logs. When the log backend supports it, you can click from a log line to the related trace.
Securing telemetry transport: TLS, authentication, and multi-tenancy
Telemetry often contains sensitive operational data: internal endpoints, error messages, and sometimes identifiers that you must control. Security controls should be built into the transport and collector layers.
TLS for OTLP
OTLP over gRPC or HTTP should be protected with TLS when crossing trust boundaries. Inside a single cluster network, some teams start with plaintext and move to TLS when stabilizing. In regulated environments, you may need TLS from day one.
At minimum:
Use TLS from agents to gateways if networks are shared or untrusted.
Use TLS from gateways to backends, especially if backends are managed services.
If you run mTLS (mutual TLS), you also get client authentication, which is helpful for multi-tenant routing.
Authentication and authorization
The collector can be placed behind an ingress or service mesh policy that enforces authentication. Another approach is to restrict collector endpoints to private networks and use network policies.
For multi-tenant clusters (multiple teams or business units), consider separate pipelines or separate gateway collectors per tenant. This makes it easier to enforce retention and attribute policies.
Enrichment and normalization: making telemetry queryable across teams
Once telemetry is flowing, the next challenge is consistency. Without enrichment and normalization, you end up with a mix of naming conventions and missing attributes. Collectors are a good place to enforce policies.
Resource detection
Resource detection identifies where a signal originated (cloud provider, host, Kubernetes metadata). This is crucial for operations teams because it enables queries like “show errors by namespace” or “p95 latency by node pool.”
Collector processors can enrich telemetry with Kubernetes attributes when running in-cluster. The exact configuration depends on your collector build and permissions, but the operational concept is stable: add consistent metadata at the collector tier rather than relying on each app team to set it correctly.
Attribute filtering and redaction
Attributes can contain sensitive data if teams add them indiscriminately. Filtering and redaction in the collector reduces risk.
Common policies include:
Drop headers like authorization and cookie from span attributes.
Drop query strings or full URLs if they can contain tokens.
Allowlist specific attributes for high-volume spans.
Administrators should treat these policies like firewall rules: explicit, reviewed, and version-controlled.
Real-world example 2: Hybrid .NET and Java services with a central collector gateway
A typical enterprise scenario involves a mix of Windows-hosted .NET services and Linux-hosted Java services, with teams using different APM agents historically. The organization wants a single ingestion path and the ability to route telemetry to both an existing Prometheus deployment and a new tracing backend.
A pragmatic rollout looks like this:
First, deploy a gateway OpenTelemetry Collector in a central network segment with strict inbound rules (only from known subnets) and outbound access to backends.
Second, deploy lightweight agent collectors on Windows and Linux hosts. These agents are configured with the same OTLP exporter endpoint (the gateway) and the same resource attributes for environment and region.
Third, start with auto-instrumentation in one .NET service and one Java service that are known to have frequent incidents. Keep sampling moderate and focus on consistent service naming.
The operational win shows up quickly: the on-call team can correlate spikes in Java service latency with downstream calls into a .NET dependency and confirm whether the issue is inside the dependency or caused by upstream load. Previously, each team’s APM tool showed only its own portion of the request.
From an admin perspective, the critical point is that the gateway becomes the enforcement point. Even if teams differ in how they instrument, the organization can normalize attributes, apply tail sampling, and route to multiple destinations without giving every app direct access to backends.
Instrumenting common platforms: pragmatic notes for admins
OpenTelemetry supports many languages and frameworks. The exact steps differ, but administrators can still standardize delivery by providing templates and platform guidance.
.NET considerations
For .NET services, OpenTelemetry SDK integration is commonly done via NuGet packages and configuration. Auto-instrumentation is also available for certain deployment models.
From an admin angle, ensure:
Services set OTEL_SERVICE_NAME and environment attributes.
HTTP client and ASP.NET instrumentation is enabled.
Database instrumentation is enabled where relevant, but SQL statement capture is configured carefully to avoid sensitive data exposure.
Java considerations
Java services can use the OpenTelemetry Java agent for auto-instrumentation in many cases. Operationally, this is attractive because it reduces code changes.
Admin focus areas:
Standardize JVM arguments to include the agent and OTLP endpoint.
Ensure the process can reach the local collector (agent) endpoint.
Watch memory overhead and sampling rates for high-throughput services.
Node.js and Python considerations
Node.js and Python services often use SDK-based instrumentation. They benefit from consistent environment variable configuration, and they can emit high-cardinality attributes if not governed.
Admin focus areas:
Provide a baseline library version policy (avoid drift across services).
Encourage semantic conventions for HTTP route templates.
Prefer collector-based filtering over per-service ad-hoc filters.
Validating telemetry end-to-end: a controlled rollout method
Rolling out OpenTelemetry across a fleet is easiest when you treat it like any other infrastructure change: stage it, validate it, and expand in increments.
Step 1: Validate the collector pipeline with synthetic telemetry
Before instrumenting applications, validate the collector and backend integration. Some teams use a simple test app or a known-good demo service that exports OTLP.
The objective is to answer basic operational questions:
Do the collector receivers accept OTLP on the expected ports?
Can the collector reach the backend endpoints?
Do you see traces/metrics/logs in the backend with expected service identity?
This prevents a common failure mode where application teams “instrument correctly” but the pipeline drops data due to network policies or misconfigured exporters.
Step 2: Instrument one service end-to-end and confirm trace continuity
Choose a service with a clear upstream/downstream path. Instrument it, then instrument its immediate dependency, and confirm you get a single trace across both.
This is where context propagation issues appear. Fix them early, because once you have dozens of services, it becomes much harder to identify where trace context is being lost.
Step 3: Standardize deployment templates and onboard services
Once you have a working pattern, capture it in templates:
Kubernetes Helm values for OTEL env vars and endpoints.
Systemd unit templates for VM collectors.
Baseline collector config fragments for agent and gateway.
At this stage, success is about consistency. The more you can standardize service identity and exporter endpoints, the less work you do later to normalize telemetry.
Operating the collector at scale: sizing, backpressure, and reliability
After initial rollout, collector operations become the main concern. The collector is a critical dependency in your monitoring pipeline, and its failure modes can be subtle.
Batching and memory limiting
Batching reduces backend write amplification and improves throughput. Memory limiting prevents the collector from consuming excessive RAM during spikes.
In production, always use batch and memory_limiter processors. Then tune:
Batch size and timeout based on signal volume and acceptable latency.
Memory limits based on node sizing and expected spikes.
The gateway layer is where you are most likely to need larger memory limits due to tail sampling buffers.
Horizontal scaling
Collectors scale horizontally, but you need to consider stateful processors. Tail sampling, for example, benefits from consistent routing so that spans for a trace end up at the same collector instance.
A common solution is to use a load balancer that hashes by trace ID (where supported) or to use a dedicated load balancing exporter/receiver strategy within collector tiers. If you cannot ensure trace affinity, tail sampling becomes less effective or requires additional architecture.
Even without tail sampling, horizontal scaling matters because telemetry can spike during incidents—exactly when you need observability most.
Reliability and data loss expectations
Telemetry pipelines should be designed with the assumption that some data loss is acceptable but must be controlled and visible. Decide:
Is it acceptable to drop traces under sustained overload? Usually yes, if metrics and logs continue.
Do you need guaranteed delivery for audit logs? If yes, logs may require a different pipeline.
Do you need buffering to disk? Some environments use queueing mechanisms external to the collector.
Administrators should monitor the collectors themselves (CPU, memory, queue length, export errors). OpenTelemetry can instrument the collector, and many backends also expose collector health metrics.
Real-world example 3: Migrating from vendor-specific agents to OpenTelemetry without losing visibility
A common driver for OpenTelemetry adoption is reducing vendor lock-in. Suppose an organization currently runs a proprietary APM agent on all JVM services, but it cannot be installed on certain hardened hosts, and it does not integrate cleanly with newer container workloads. They want to standardize on OpenTelemetry while still sending data to the existing APM backend during transition.
A migration plan that avoids a “flag day” looks like this:
Deploy OpenTelemetry Collectors as gateways and configure them with two exporters: one to the existing APM vendor endpoint (using a supported exporter) and one to a new tracing backend.
Instrument a small set of services with OpenTelemetry (agent-based for Java, SDK for others). Route their telemetry through the collector to both backends.
Compare data fidelity: span names, attributes, service maps, and latency distributions. Adjust semantic conventions and attribute policies so the OpenTelemetry data matches operational expectations.
Once confidence is high, expand instrumentation to more services, and gradually reduce reliance on the vendor agent. Because the collector handles export, you can switch destinations without forcing every service to change configuration.
The operational insight here is that OpenTelemetry adoption is often a pipeline project more than an application project. If you get the collector tier and conventions right, the rest becomes incremental.
Alerting and SLO-oriented monitoring with OpenTelemetry signals
OpenTelemetry itself does not provide alerting; your backend does. But the way you instrument and export telemetry affects how actionable your alerts are.
A practical model is:
Use metrics for alerting (latency percentiles, error rates, saturation).
Use traces for investigation (why a specific request is slow, where errors originate).
Use logs for detailed context (error messages, stack traces, business event details), correlated via trace IDs.
This division of labor helps control cost. Metrics are cheap and aggregate; traces and logs are richer but higher volume.
When instrumenting HTTP services, ensure you can compute the “golden signals” per service: latency, traffic, errors, and saturation. OpenTelemetry semantic conventions and standard instrumentation libraries often provide baseline metrics/spans, but verify that the metrics you need are actually exported and that they have stable dimensions.
Governance: keeping your OpenTelemetry implementation maintainable
Once multiple teams contribute telemetry, governance is what prevents entropy.
Versioning and compatibility
OpenTelemetry SDKs and collector components evolve. You need a controlled upgrade process.
For administrators, a workable policy is:
Standardize a supported collector version (and distribution) per environment.
Provide a baseline SDK version range per language.
Maintain configuration in Git and deploy via CI/CD.
Test changes in staging with representative load.
This avoids a situation where one team upgrades an SDK and introduces attribute changes or new metrics that break dashboards.
Attribute and naming policy
Treat attribute policy as a contract:
Define a required set (service.name, deployment.environment, service.version).
Define an allowlist for custom attributes on spans and metrics.
Document naming conventions for service names and span names.
This does not need to be bureaucratic. Even a one-page standard prevents many expensive cleanup projects later.
Data retention and cost controls
Retention is primarily a backend concern, but OpenTelemetry impacts it through volume. Tail sampling policies, metric cardinality controls, and log strategy determine how much data you store.
A good operational pattern is to revisit:
Sampling rates after you have baseline coverage.
Which attributes you keep on high-volume spans.
Whether you need full logs in the same backend or if correlation is enough.
These decisions often change after the first few incidents where traces prove their value.
Putting it together: an implementation path that works in most enterprises
At this point, the pieces should fit together as a coherent rollout.
Start by deploying a gateway collector and sending data to one backend per signal. Keep the configuration minimal but production-safe (batching, memory limits). Then deploy agent collectors in the environments where local reception reduces complexity (Kubernetes nodes, VM hosts).
Once the pipeline is stable, instrument one or two services end-to-end and confirm trace continuity. Use that success to standardize service identity and environment attributes across the fleet. Only after you have consistent identity should you invest heavily in dashboards, because dashboards built on inconsistent attributes are fragile.
As you scale, move sampling decisions to the gateway layer where you can keep error and slow traces without ingesting everything. Enforce attribute policies and redaction in the collector, because that’s where administrators can apply controls consistently without relying on every development team to remember them.
Finally, operationalize the collector tier: monitor it, scale it, and manage it like any other critical platform component. When you do, OpenTelemetry becomes the backbone of application monitoring rather than another tool teams experiment with and abandon.