Modern observability platforms have revolutionized how enterprises monitor and manage complex systems. But as data volumes explode, the economics behind these platforms are coming under scrutiny.
In this exclusive VMblog Q&A, Andi Mann, CPTO at Apica, and a veteran of enterprise operations, product strategy, and observability, shares hard-earned insights from decades in the field and offers a roadmap for navigating the financial realities of cloud-native observability.
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VMblog: Andi, you’ve had a long career in operations and observability. What was your early experience like?
Andi Mann: Early on, I managed mainframe operations at a major financial institution. Picture three consoles, each with four screens. My job was to manually correlate log data by timestamp, jumping between displays, trying to catch issues before they escalated. We missed problems often. Not because we lacked skill, but because the volume of data made it impossible to catch everything manually.
VMblog: What was the moment that shifted your perspective on observability?
Mann: It happened during my time as an analyst at Enterprise Management Associates. Three founders from a startup, Splunk, drove out to meet me in person. That was rare; most briefings were virtual. They pitched a vision for machine data analytics that made me feel something I hadn’t felt in years: Hope. They understood that the old way, sampling data and retrofitting observability, was broken.
VMblog: You later worked inside major vendors. How did that shape your view?
Mann: At CA Technologies, I was in the CTO’s office, helping shape product strategy. We believed we were balancing customer value with business sustainability. As data volumes grew 30-50% annually, we scaled infrastructure and priced accordingly. It made sense until I saw the long-term impact. Later, at Splunk and other platforms, I noticed a growing gap between the promise of observability and its economic reality.
VMblog: Can you give an example of how this misalignment shows up in the real world?
Mann: Absolutely. In 2022, a major financial services firm received a quarterly observability bill for $65 million. That’s not annual, just three months. The vendor called it a “one-time spike,” which was technically true. But it exposed how consumption-based pricing, when applied to cloud-native telemetry data, can produce shocking outcomes.
VMblog: Are other companies facing similar challenges?
Mann: Yes. I advise clients spending $10-$20 million annually on observability. Banking clients expect costs to triple in two years. Manufacturing clients say observability is now their second-largest cloud expense after compute. These teams aren’t doing anything wrong; they’re following best practices. But the economics aren’t keeping pace with the architecture.
VMblog: What trade-offs are teams making to manage costs?
Mann: Many are forced to sample data. I spoke with one Ops team that missed a critical issue because they were sampling performance metrics. Average response times looked fine, but high-value customers were experiencing severe delays. Teams tell me, “We have to sample. We can’t afford full-fidelity data.” It’s a rational choice, but it creates an unsustainable trade-off between visibility and budget.
VMblog: Why speak out now?
Mann: Conversations with Fortune 500 CTOs revealed a pattern: Observability costs ballooning from single-digit millions to eight figures. They feel trapped, deeply integrated with vendors but unable to sustain the costs. It’s not about blame. It’s about a business model that worked for monitoring hundreds of servers but struggles with petabytes of telemetry data.
VMblog: What should companies do tomorrow morning to start addressing this?
Mann:
- Audit your true TCO. Include licensing, missed data, and architectural constraints.
- Map your dependencies. Know where vendor lock-in exists.
- Build optionality. Adopt open standards, insist on data portability, and keep functions modular.
- Question the pricing model. If your vendor profits when you generate more data, ask if that aligns with your long-term goals.
VMblog: Is there a path forward?
Mann: Definitely. The technology has delivered on its promise. But the economics need to evolve. The good news is that the industry is adapting-open standards like OpenTelemetry, modular architectures, and alternative pricing models are emerging. I’ve helped build these systems, and now I want to help others navigate them more effectively.
VMblog: Andi, you’ve spent decades inside the observability ecosystem. What led you to join Apica?
Mann: After years of watching the economics of observability spiral out of control, I realized the industry needed a reset. I wasn’t looking for another vendor. I was looking for a platform that challenged the status quo. Apica stood out. Our approach to telemetry pipelines, modular architecture, and cost transparency aligned with the kind of change I knew enterprises were desperate for.
VMblog: What makes Apica different from traditional observability vendors?
Mann: Apica doesn’t just collect data; we empower customers to control it. Our Flow product, for example, gives teams the ability to manage telemetry pipelines with precision, reducing waste and cost without sacrificing visibility. It’s not about sampling less; it’s about capturing smarter. That’s a huge shift from the “more data equals more cost” model we’ve seen dominate the space.
VMblog: How does Apica help address the economic challenges you’ve spoken about?
Mann: Apica’s architecture is built for flexibility. Whether it’s open standards, data portability, or modular deployment, we’re giving customers options, not lock-in. That’s critical when you’re trying to escape runaway costs. I joined Apica because we’re not just building tools. We’re building leverage for the customer.
VMblog: What’s your mission at Apica?
Mann: My goal is to help customers rethink what’s possible. Observability should be a strategic advantage, not a financial liability. At Apica, we’re proving that you can have full-fidelity visibility, scalable architecture, and economic sanity all at once. That’s the future I want to help build.
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