Getting observability right in a microservices world isn’t optional—it’s table stakes. OpenTelemetry (OTel) has become the standard for capturing traces, metrics, and logs, but actually implementing it? That’s where teams hit roadblocks.
Instrumentation overhead, performance trade-offs, and managing huge volumes of telemetry data are some of the biggest friction points. Too much data slows everything down. Too little, and you’re flying blind. Finding the right balance is the challenge.
This guide covers the real-world obstacles developers face when rolling out OpenTelemetry in microservices and the solutions that make it easier—including zero-code instrumentation, eBPF-based tracing, and smarter data collection strategies.
Microservices complicate observability. Unlike monolithic applications, where logs and traces live in one place, distributed services create an explosion of telemetry data across multiple runtimes, environments, and dependencies.
Three major challenges tend to come up:
| Challenge | Why It Happens | Impact |
|---|---|---|
| Instrumentation Overhead | Manual SDK-based tracing requires modifying application code. | Increases developer workload and slows adoption. |
| Data Volume & Management | Each microservice generates massive amounts of logs, traces, and metrics. | Leads to high storage costs, slow queries, and noise that hides real issues. |
| Performance Overhead | Poorly optimized tracing adds latency and consumes system resources. | Slows down applications, reducing efficiency and responsiveness. |
Let’s break these down and look at how to solve them.
Traditional OpenTelemetry instrumentation requires developers to manually inject tracing code into their applications. Every new microservice needs custom instrumentation, adding friction to development.
Instead of modifying every service manually, zero-code instrumentation automatically detects and traces applications without requiring changes to the codebase.
We designed Odigos as a zero-code solution so that you can:
Here’s the process flow when zero-code instrumentation is deployed:
Instead of developers adding tracing logic to each microservice, the instrumentation layer detects services and attaches telemetry automatically. This eliminates time-consuming manual work and makes scaling OpenTelemetry much easier.
When to Use It: If your team is spending too much time on manual OpenTelemetry SDK implementation or struggling with polyglot environments (Go, Python, Java, Node.js, etc.), zero-code instrumentation is the answer.
Microservices generate a massive volume of telemetry data. If you collect everything, costs explode, and queries become sluggish. But if you collect too little, you lose critical insights.
Instead of collecting every trace and log, use adaptive sampling and intelligent aggregation to keep only what matters.
| Strategy | How It Works | Why It Helps |
|---|---|---|
| Head-Based Sampling | Decides whether to trace a request at the start. | Reduces load but may miss important traces. |
| Tail-Based Sampling | Keeps traces only if an anomaly occurs (like errors or latency spikes). | Captures only the most valuable data. |
| Dynamic Sampling | Adjusts sampling rates based on service load and request types. | Ensures better coverage with less noise. |
Instead of collecting every request, only slow or failing requests get traced, keeping telemetry data meaningful and manageable.
Tail-based or adaptive sampling is especially helpful as you start to see storage costs are getting out of hand, queries slowing down, and you’re drowning in unhelpful data.
Adding observability shouldn’t slow down your application. But if you’re using traditional tracing methods, you’re injecting latency and CPU overhead into every request.
Instead of tracing at the application level, eBPF (Extended Berkeley Packet Filter) traces at the kernel level with almost zero overhead.
Here’s why eBPF-based tracing provides a significant advantage:
(source: https://ebpf.io/what-is-ebpf/)
With eBPF, telemetry data is collected passively without modifying applications, ensuring minimal performance hit while gaining deep visibility into system behavior.
eBPF-based tracing is optimal because applications shouldn’t have to incur tracing overhead but still need full observability, Using eBPF gives insights into query-level and code-level activity gives the context we need to really have end-to-end observability.
Rolling out OpenTelemetry in a microservices environment isn’t always smooth, but the right approach makes a huge difference. We’ve worked extensively with the open source community to build Odigos to solve these challenges.
| Challenge | Best Fix |
|---|---|
| Manual Instrumentation | Zero-Code Instrumentation (automatic language detection, no SDK changes) |
| Overwhelming Data Volume | Tail-Based & Dynamic Sampling (capture only critical traces) |
| Performance Overhead | eBPF Tracing (low-latency kernel-level tracing) |
With zero-code instrumentation, smart sampling, and eBPF-based tracing, teams can implement OpenTelemetry without the usual struggles—getting the visibility they need while keeping applications fast and scalable.
For more hands-on OpenTelemetry insights, check out Odigos' blog or dive into the OpenTelemetry documentation.
