The original story for this post was written by our technical partner Three Tree Tech and is based on their engagement with Monks. You can read the original piece on their site.
For Monks, a global digital services company helping the world's most ambitious brands turn complexity into opportunity, the irony wasn't lost. Their own internal data stack was doing exactly the opposite.
As Monks scaled its Technology Services and AI capabilities, their legacy observability platform had become a liability. The cost to ingest and retain data was so high that engineers were forced to heavily sample what they kept — creating security blind spots, slowing incident resolution, and blocking the cost-neutral AI adoption they needed to grow.
The results speak for themselves:
- 40% reduction in observability spend
- 65% faster Mean Time To Resolution (MTTR)
- 95% data compression with far longer retention
- 100% data visibility — no sampling, no blind spots
Here's how it happened.
The problem: Data as a tax
Monks' security and engineering teams were spending more time managing logging clusters than extracting value from them. When something went wrong, engineers searched across multiple systems, sometimes waiting hours to rehydrate logs from cold storage — only to find critical data was missing because it had been sampled away in the first place.
Alexander Wold, Global Director of CyberSecurity, put it plainly:
Our previous observability stack was doing the opposite — it was turning our data scale into a financial burden. We viewed logging as a tax we paid for compliance.
The problem compounded as Monks.Flow — their AI-driven workflow platform — began generating massive event streams. The legacy stack couldn't absorb this scale without cost penalties that would have made cost-neutral AI adoption impossible.
The vetting process: Five Filters
Rather than simply recommending a tool, Three Tree Tech applied their Five Filters methodology to rigorously de-risk the decision for Monks' leadership. The framework evaluates technology partners across five dimensions:
- Technology — Does the architecture eliminate tech debt or create a new layer of it?
- Process — Do the vendor's product practices align with the customer's culture?
- Financial — Is the vendor financially viable for the long term?
- People — Is there cultural and operational alignment between teams?
- You — Does this solution fit your specific business context and goals?
Axiom stood out immediately on the technology filter: its schema-less ingestion and serverless architecture represented a genuine architectural shift rather than a lateral move. The financial filter was equally important — Monks' leadership needed confidence they wouldn't be trading one unstable vendor for another. Axiom's funding trajectory and positive EBITDA validated their long-term viability.
3 Tree Tech didn't just find us a tool, they vetted a partner. — Alexander Wold
The architecture: Built for AI scale
With Axiom selected, Three Tree Tech designed an implementation around three principles: future-proof for AI workloads, secure by default, and zero management overhead.
Future-proofing for Monks.Flow meant architecting the pipeline around Axiom's ability to ingest petabyte-scale event streams cost-effectively, eliminating the trade-off between visibility and budget that had previously blocked AI initiatives.
Security and governance were built in from day one. Strict, need-to-know access controls ensured sensitive client data was protected, while engineers retained zero-sampling visibility for debugging. No compromise between security and observability.
Seamless integration standardized on OpenTelemetry for microservices and the Axiom Events API for custom security tooling, with native integrations for Cloudflare and AWS — ingesting data from anywhere with no configuration overhead.
Drew Gowan, Global Principal Security Engineer, described the result:
Axiom provided the AI Readiness we needed for Monks.Flow. We achieved a modernized, serverless architecture that improved our security instantly, giving us data governance at scale without sacrificing the visibility our engineers require.
The impact: From expense to asset
The transformation was felt across every level of the organization.
For the security leadership team, the 40% cost reduction freed up budget to expand Monks' client-facing Security Services, converting an internal cost center into a competitive differentiator. "This efficiency allows us to pass value back to our clients," Wold noted, "offering them superior protection without the premium price tag usually associated with this level of visibility."
For engineers on the ground, the shift was immediate. Sergio Rovira, Sr. Application Security Engineer, described the day-to-day change:
With zero sampling, I never have to say we missed an error or go searching through individual systems.
The combination of power, querying billions of events in seconds, and usability meant the entire team could get value from Axiom from day one, without a steep learning curve or dedicated tooling engineer.
What makes this model work
Three Tree Tech's engagement with Monks illustrates something important: the technology is only part of the equation. A rigorous, structured vetting process that aligns a solution to business objectives, rather than just technical requirements, is what separates a good tool from a transformative partnership.
Axiom's architecture made the technical case easy. But it was the financial viability, team alignment, and long-term partnership model that gave Monks' leadership the confidence to fully commit.
Ready to turn your observability data from a cost center into a strategic asset?
Sign up for free or contact us at sales@axiom.co to talk with the team about what's possible at your scale.
