The volume of telemetry data is exploding, fueled by rapid AI deployments and a sprawl of cloud infrastructure, microservices, and IoT devices. It's getting to the point where enterprises are starting to drown in their own telemetry.
If organizations don't proactively cut costs and take control of their data pipeline, insights will be buried, expenses will get out of hand, and IT budgets will become strained. To address this, companies have traditionally faced a simple but limiting choice: buy or build their observability stack.
But as the observability market is projected to hit $14.2 billion by 2028, it's becoming increasingly crowded with over 40 vendors competing, resulting in even more cost pressures and complexity for buyers. Today, companies are already desperate to cut these costs without sacrificing data insights, but this old "either/or" framework is failing DevOps and platform engineering teams and hurting the bottom line.
The Reality of Building Data Pipelines from Scratch
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 (OTel). 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.
Without the proper guidance and frameworks, companies that do this risk creating systems that fail under production load or creating pipelines that are unable to adapt to evolving needs.
This complexity, however, extends beyond the initial building phase. Maintaining the different elements of a DIY pipeline involves significant work with regular updates, problem-solving, and integration efforts. To keep pipelines running smoothly, expertise in health monitoring, quality troubleshooting, and performance optimization is required. Pipelines require dedicated monitoring, alerting, on-call rotations, and capacity planning, all of which is overhead that is rarely accounted for in initial build budgets.
Additionally, as operations scale, platform teams remain blind to which collectors are running which configurations with the limited visibility DIY pipelines typically have. These pipelines also offer no way to preview changes against real telemetry, which makes testing difficult and routine changes risky to deploy. Additionally, when the few engineers who built the pipeline leave, organizations are often left with systems that are difficult to understand, maintain, or troubleshoot.
Without this operational maturity, organizations risk failure and will struggle to derive meaningful data insights.
The Vendor Trap of Buying Pipelines
Organizations can avoid building complexity by buying third-party tools to manage their data. While this helps companies in the short-term, it creates long-term challenges. Cloud costs that seem reasonable at first inflate exponentially as data volumes grow. When vendors charge by ingestion volume, telemetry growth turns observability into a prohibitive expense rather than a business asset.
Additionally, vendors are sending data to numerous locations. When it remains unfiltered, this could result in organizations paying for storage, processing, and management of unnecessary data and duplicates that provide little business value.
It's also common for companies to find themselves constrained by existing vendor relationships, unable to pivot without substantial rework and expense. This prevents organizations from keeping pace with fast-evolving AI tools and data needs as businesses adapt. Once an organization's data outgrows the proprietary vendor solution, transitioning to new platforms is complex and expensive.
Total Data Control Without the Operational Drag
The old choice was to either manually build and take months doing it or rely on a proprietary data pipeline as data grows and costs skyrocket is a challenging choice, but now organizations no longer need to make that trade-off.
A hybrid approach is emerging. Organizations can now build and own their pipelines through an OpenTelemetry-based control plane that combines the flexibility of open source with the speed, simplicity, and operational control needed to manage telemetry at scale. This route allows organizations to move beyond the 'either/or' framework and get the best of both worlds by building a scalable foundation that reduces costs and complexities.
To successfully implement this hybrid approach and achieve the speed of a "bought" solution with the data ownership and control of a "built" one, organizations should focus on open-source data collection. By building pipelines on open standards, organizations can define what data their collectors gather, how it's processed, and where it's delivered, all in a vendor-neutral way. This makes organizations' systems more interoperable and flexible.
When building this pipeline, organizations should also adopt a control plane that is built on an open source. Doing so can simplify deployment of the pipeline, giving organizations the flexibility and ownership of open source without the operational burden of building and maintaining everything themselves.
Build the Pipeline, Buy the Operational Layer
By taking this hybrid approach organizations can take control of their data pipelines with more ease. This allows enterprises to filter and route their data more intelligently, allowing them to route lower value data to low-cost storage. This will help organizations cut costs and drive more efficiency in their data management and backend monitoring solutions.
Standardizing the ingestion process with tools built on OpenTelemetry across multi-vendor environments provides flexibility that counteracts the constraints of any single log management tool.
A business's telemetry is too critical for vendor control. What data is collected, how it's processed, where it flows, which backends it lands in, all should stay in the organization's hands. The operational layer that handles fleet management, safe deployments, and health monitoring can be bought. This approach guarantees total data ownership without the engineering burden of maintaining pipeline infrastructure.
This approach allows organizations to future-proof their observability for operational resiliency. Organizations that take ownership of their telemetry and leverage open standards to maintain control of their data in motion will pave the way for scaling AI and cloud operations, without their budgets scaling alongside them or their teams drowning in the noise.
