In today's digital landscape, enterprises face a perfect storm of increasing application complexity and soaring observability costs. As organizations continue to embrace microservices, containerization, and heterogeneous technology stacks, the volume of telemetry data required to effectively monitor these environments has expanded exponentially, and so have the bills from commercial observability vendors.
Let's explore how this challenge emerged and how solutions like pureIntegration's Unified Observability Platform offer a sustainable path forward.
The Growth of Complexity in Modern Applications
The last five years have witnessed a dramatic transformation in enterprise application architecture. Gone are the days of monolithic applications built on standardized technology stacks. Today's reality is far more diverse:
This decentralization of technology decisions has delivered significant benefits in terms of developer productivity, recruitment, and innovation. However, it has also created much greater complexity in monitoring and maintaining these heterogeneous environments.
The Telemetry Data Explosion
This shift toward more decentralized, modular systems comes with a tradeoff: greater complexity introduces far more moving parts to observe, diagnose and optimize. The more services and layers you introduce, the more telemetry you need to collect to keep systems reliable, performant and secure.
Modern applications generate vast amounts of telemetry data across three key dimensions: Metrics, Distributed Traces and Logs. According to IDC, the average enterprise now collects over 10TB of observability data per day — a five-fold increase from 2019.
This explosion in data stems from several factors: the aforementioned increase in service components to monitor, higher cardinality of attributes with the data, shorter collection intervals, and the greater need to instrument across all layers of the software and infrastructure stack.
The Commercial Observability Cost Crisis
While this deeper instrumentation has enabled better insights into service assurance,it also brings a hidden ‘feature’: more data means more dollars to monitor. Enterprises aren’t just collecting more telemetry, they’re paying more to store, process an analyze it, especially when using commercial vendor tools.
A 2023 Forrester Research study found that enterprises' annual spending on observability tools increased by an average of 212% between 2019 and 2023. For many organizations, annual observability costs now exceed $1 million, with some large enterprises spending $10 million or more.
Most vendor pricing models are based on: volume of data ingested, length of data retention, number of instances of architecture components monitored, and features and capabilities enabled.
These pricing factors of commercial tools, combined with increases in data volumes, have led to several counterproductive behaviors that reduce overall visibility and reliability:
An Alternative to Commercial Tools
Responding to this crisis, many forward-looking organizations have turned to open source observability solutions. The open source ecosystem is vast, with solutions covering many aspects of observability.
While open source solutions offer a path to cost control without sacrificing visibility, they bring their own challenges:
The pureIntegration Unified Observability Platform
This is where pureIntegration's Unified Observability Platform comes in. We’ve taken over 15 years’ experience working with both large, Fortune 500 companies as well as smaller enterprises to develop opinionated strategies and hardened, supported configurations that allow enterprises to confidently make the transition away from cost prohibitive commercial vendors. The platform leverages best-of-breed open source tools while addressing the key challenges through:
The Business Impact
Organizations that have taken our Unified Observability approach have gained significant benefits:
Discover how pureIntegration's Unified Observability Platform can revolutionize your monitoring approach, reduce costs, and enhance system reliability.