Your Observability Stack Has a Telemetry Pipeline Problem

The tool landscape has never been more fragmented - Controlling how telemetry moves between platforms is the new competitive edge for engineering teams
Mike Kelly
Bindplane
Learn more about Dynatrace

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively.

Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money. Every vendor-specific integration creates lock-in. And as AI-powered observability enters the picture, demanding constant, high-quality data streams, the cost of that neglect is about to rise sharply.

The Telemetry Tax Is Real - and Growing

Most engineering teams don't think about observability costs until a cloud invoice forces the conversation. By then, the damage is done: data is being ingested at full fidelity to premium platforms where only a fraction of it ever gets queried. In a February 2026 AWS Builder Center article, Masroor Ahmed states that "roughly 30% to 32% of total cloud spend is wasted on resources that are either oversized or left running when they aren't needed. This means that for every $1 million a company spends, at least $300,000 is vanishing without providing any business value. The movement of observability data is a significant contributor."

Teams that restructure their telemetry pipelines intelligently, routing high-value signals to premium platforms and high-volume, low-priority data to cheaper long-term stores, have reported cost reductions averaging 18% on cloud infrastructure. What's more, APMdigest itself reported, in an article from Splunk, that 57% of observability leaders have successfully reduced costs with OpenTelemetry by gaining control over what telemetry is collected, how it's routed, and where it goes. That's not a rounding error. It's budget that can fund the next platform evaluation or additional headcount.

OpenTelemetry Unlocked the Door. The Pipeline is Still Yours to Build

OpenTelemetry is a genuine step forward. Standardizing on the OpenTelemetry Protocol for metrics, traces, and logs means teams aren't trapped by proprietary SDKs and vendor-specific instrumentation. But OpenTelemetry standardized the signal format — it didn't solve the routing, transformation, and governance challenges that come after data leaves your application.

You still need to decide which signals go to which platforms and at what volume, how to transform schemas to match destination backends, and how to filter noise before it reaches expensive ingestion endpoints. These are pipeline architecture decisions, not tool selection decisions. Most teams are making them ad hoc — hardcoding destination configs, adding one-off integrations, and building brittle pipelines that are painful to modify when the vendor landscape shifts. Given how fast it shifts, that's a meaningful operational liability.

Observability Vendor Lock-In Is the Cost Nobody Budgets For

Lock-in in the observability space doesn't hit you when you sign the contract. It hits you when you try to leave, or when a competing platform offers capabilities your current vendor can't match. Observability vendors make it extremely easy to route everything their way. Their agents and collectors are optimized to funnel data to their ingestion endpoints. When your telemetry pipeline is essentially a direct line from your infrastructure to a single vendor, you're not architecting for flexibility, but trading optionality for short-term simplicity.

An estimated 69% of enterprises use multiple cloud providers specifically to avoid infrastructure lock-in. Engineering teams should apply the same logic to their observability stacks. Organizations getting this right treat telemetry pipelines as programmable infrastructure — vendor-agnostic and capable of routing different signal types to different destinations based on cost, capability, and business need. When a new AIOps platform arrives with ML-based anomaly detection your current vendor can't match, a flexible pipeline means a simple configuration change. A locked pipeline means a months-long integration project.

AI Observability Will Demand More from Your Pipeline

The observability use case for AI is moving in two directions simultaneously. The first is AI-powered observability: platforms using machine learning for anomaly detection, predictive alerting, and automated remediation. These tools often operate on windowed snapshots that get retrained. They need continuously refreshed data to keep baselines current across metrics, traces, logs, and continuous profiling data to build reliable baselines. If your pipeline is lossy or inconsistently filtered upstream, the ML models downstream will reflect that.

The second is the observability of AI systems themselves. As teams deploy models in production, they're responsible for monitoring inference latency, token throughput, model drift, and GPU utilization — none of which map cleanly onto traditional APM signal types. Gartner forecasts worldwide AI spending will reach $2.52 trillion in 2026, a 44% year-over-year increase, with AI infrastructure accounting for most of that figure. Engineering teams that haven't addressed their telemetry pipeline architecture will find themselves managing a new class of observability complexity on top of an already strained foundation.

What Programmable Telemetry Infrastructure Looks Like

Teams that have solved this problem don't think of it as a monitoring problem — they think of it as infrastructure, governed like any other production system. In practice, that means pipelines that route by signal type and destination fit (security logs to a SIEM, high-resolution metrics to a time-series platform, high-volume debug logs to cold storage), transform schemas in-flight so each backend gets data in the shape it expects, absorb backpressure when a destination is unavailable, and enable low-risk platform evaluation by routing a subset of telemetry to a new tool without a full migration.

The goal isn't to reduce observability coverage. It's to make routing decisions at the infrastructure level so each platform receives the signals it's designed to act on, at the cost profile that matches the value it delivers.

The Pipeline Is the Strategy

The APM and observability market will keep consolidating, expanding, and fragmenting. New platforms will emerge. Pricing models will shift. AI-native tools will challenge incumbents. Engineering teams that treat telemetry pipelines as fixed infrastructure will be rearchitecting their observability stacks every time the market moves.

Teams that build pipelines as configurable, vendor-agnostic layers will navigate that landscape differently — moving to new tools without starting from scratch, controlling costs without sacrificing coverage, and feeding AI systems the telemetry they need to function. The data is in motion. The question is whether your pipeline is built to keep up.

Mike Kelly is CEO and Co-Founder of Bindplane

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Your Observability Stack Has a Telemetry Pipeline Problem

The tool landscape has never been more fragmented - Controlling how telemetry moves between platforms is the new competitive edge for engineering teams
Mike Kelly
Bindplane
Learn more about Dynatrace

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively.

Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money. Every vendor-specific integration creates lock-in. And as AI-powered observability enters the picture, demanding constant, high-quality data streams, the cost of that neglect is about to rise sharply.

The Telemetry Tax Is Real - and Growing

Most engineering teams don't think about observability costs until a cloud invoice forces the conversation. By then, the damage is done: data is being ingested at full fidelity to premium platforms where only a fraction of it ever gets queried. In a February 2026 AWS Builder Center article, Masroor Ahmed states that "roughly 30% to 32% of total cloud spend is wasted on resources that are either oversized or left running when they aren't needed. This means that for every $1 million a company spends, at least $300,000 is vanishing without providing any business value. The movement of observability data is a significant contributor."

Teams that restructure their telemetry pipelines intelligently, routing high-value signals to premium platforms and high-volume, low-priority data to cheaper long-term stores, have reported cost reductions averaging 18% on cloud infrastructure. What's more, APMdigest itself reported, in an article from Splunk, that 57% of observability leaders have successfully reduced costs with OpenTelemetry by gaining control over what telemetry is collected, how it's routed, and where it goes. That's not a rounding error. It's budget that can fund the next platform evaluation or additional headcount.

OpenTelemetry Unlocked the Door. The Pipeline is Still Yours to Build

OpenTelemetry is a genuine step forward. Standardizing on the OpenTelemetry Protocol for metrics, traces, and logs means teams aren't trapped by proprietary SDKs and vendor-specific instrumentation. But OpenTelemetry standardized the signal format — it didn't solve the routing, transformation, and governance challenges that come after data leaves your application.

You still need to decide which signals go to which platforms and at what volume, how to transform schemas to match destination backends, and how to filter noise before it reaches expensive ingestion endpoints. These are pipeline architecture decisions, not tool selection decisions. Most teams are making them ad hoc — hardcoding destination configs, adding one-off integrations, and building brittle pipelines that are painful to modify when the vendor landscape shifts. Given how fast it shifts, that's a meaningful operational liability.

Observability Vendor Lock-In Is the Cost Nobody Budgets For

Lock-in in the observability space doesn't hit you when you sign the contract. It hits you when you try to leave, or when a competing platform offers capabilities your current vendor can't match. Observability vendors make it extremely easy to route everything their way. Their agents and collectors are optimized to funnel data to their ingestion endpoints. When your telemetry pipeline is essentially a direct line from your infrastructure to a single vendor, you're not architecting for flexibility, but trading optionality for short-term simplicity.

An estimated 69% of enterprises use multiple cloud providers specifically to avoid infrastructure lock-in. Engineering teams should apply the same logic to their observability stacks. Organizations getting this right treat telemetry pipelines as programmable infrastructure — vendor-agnostic and capable of routing different signal types to different destinations based on cost, capability, and business need. When a new AIOps platform arrives with ML-based anomaly detection your current vendor can't match, a flexible pipeline means a simple configuration change. A locked pipeline means a months-long integration project.

AI Observability Will Demand More from Your Pipeline

The observability use case for AI is moving in two directions simultaneously. The first is AI-powered observability: platforms using machine learning for anomaly detection, predictive alerting, and automated remediation. These tools often operate on windowed snapshots that get retrained. They need continuously refreshed data to keep baselines current across metrics, traces, logs, and continuous profiling data to build reliable baselines. If your pipeline is lossy or inconsistently filtered upstream, the ML models downstream will reflect that.

The second is the observability of AI systems themselves. As teams deploy models in production, they're responsible for monitoring inference latency, token throughput, model drift, and GPU utilization — none of which map cleanly onto traditional APM signal types. Gartner forecasts worldwide AI spending will reach $2.52 trillion in 2026, a 44% year-over-year increase, with AI infrastructure accounting for most of that figure. Engineering teams that haven't addressed their telemetry pipeline architecture will find themselves managing a new class of observability complexity on top of an already strained foundation.

What Programmable Telemetry Infrastructure Looks Like

Teams that have solved this problem don't think of it as a monitoring problem — they think of it as infrastructure, governed like any other production system. In practice, that means pipelines that route by signal type and destination fit (security logs to a SIEM, high-resolution metrics to a time-series platform, high-volume debug logs to cold storage), transform schemas in-flight so each backend gets data in the shape it expects, absorb backpressure when a destination is unavailable, and enable low-risk platform evaluation by routing a subset of telemetry to a new tool without a full migration.

The goal isn't to reduce observability coverage. It's to make routing decisions at the infrastructure level so each platform receives the signals it's designed to act on, at the cost profile that matches the value it delivers.

The Pipeline Is the Strategy

The APM and observability market will keep consolidating, expanding, and fragmenting. New platforms will emerge. Pricing models will shift. AI-native tools will challenge incumbents. Engineering teams that treat telemetry pipelines as fixed infrastructure will be rearchitecting their observability stacks every time the market moves.

Teams that build pipelines as configurable, vendor-agnostic layers will navigate that landscape differently — moving to new tools without starting from scratch, controlling costs without sacrificing coverage, and feeding AI systems the telemetry they need to function. The data is in motion. The question is whether your pipeline is built to keep up.

Mike Kelly is CEO and Co-Founder of Bindplane

Related Links

Hot Topics

Related Vendors

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

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Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

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Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

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While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

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For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

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Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...