By this point, you've solved a number of hard problems.
Financial and operational views are aligned. Workflows are connected across systems. The platform focuses attention on what actually matters. Work is structured so that insights lead to defined responses. And the people closest to the business can evolve the model without waiting on engineering.
What remains is speed. And it matters more than most ITFM conversations acknowledge.
There's a tempting version of "high-velocity" that's really just faster page loads. That's not what we're describing.
The real delay in most ITFM programmes isn't how quickly results render. It's the end-to-end cycle time: from data arriving, to insight being available, to a decision being made and acted on. Each step in that cycle introduces latency. And in cost management, latency compounds. By the time a decision is made on data that took three days to process, align, and review, the underlying situation has already moved on.
The traditional response to this was to batch. Get the model ready on Friday. Run the calculations over the weekend. Publish results on Monday morning. That cycle made sense when data volumes were manageable and cost structures were relatively static. It doesn't hold up anymore.
The data scale problem in modern ITFM is easy to underestimate, because the inputs don't look large in isolation.
Consider labour costs. You have a headcount. You have salaries. You allocate them to cost centres. Simple enough — until you need to distribute shared labour across every server in the estate. Thousands of physical and virtual nodes, each with a fractional allocation, each changing as people move teams, projects end, or infrastructure is decommissioned. That straightforward cost allocation becomes a dense, high-cardinality calculation at scale.
Now add cloud. FOCUS-format cost and usage records from major cloud providers are not small. A large organisation's cloud billing data can arrive as hundreds of millions of rows, covering every service, every account, every region, every tag dimension, across monthly, daily, and hourly granularity. The detail that makes FOCUS useful — the same detail that enables accurate showback and chargeback — is the detail that makes it expensive to process.
Then add observability data. As organisations scale AI agents and workloads, the telemetry those systems generate, metrics, traces, logs, usage signals, becomes a cost data source in its own right. Understanding the true cost of an AI workload means joining billing data with operational signals that arrive continuously, at high frequency, across dimensions that weren't designed to align with financial taxonomies.
None of these are exotic data sources. They're standard inputs for a mature TBM or FinOps programme. But together, they create a data processing challenge that batch-weekend architectures were never built to handle, and that naive approaches to real-time processing will simply buckle under.
Yarken is built on PostgreSQL, ClickHouse, Redis, and Cube.js — with blob and bucket storage handling large-scale data at rest. Each component is doing the job it's designed for, and the layering matters.
Transactional and relational data — cost model structure, allocation hierarchies, organisational metadata, rule definitions — lives in PostgreSQL. This is the authoritative record: consistent, durable, and queryable across the complex joins that underpin cost modelling.
The analytical workload sits in ClickHouse. Time-series cost data, multi-dimensional usage records, FOCUS datasets from cloud providers — the kind of aggregations that power cost trend analysis, variance reporting, and anomaly detection at scale. Columnar storage and vectorised execution mean queries that would take minutes in a row-oriented database return in seconds across billions of rows. This is where the data volume problem gets absorbed.
Redis sits on top of ClickHouse. Pre-computed results, frequently-accessed aggregations, and low-latency reads for the real-time surfaces users interact with. The cost of computing an answer once and caching it is far lower than recomputing it on every request — particularly when underlying data hasn't changed and a user is simply navigating a cost view.
Cube.js provides the semantic layer: consistent definitions of metrics, dimensions, and hierarchies that ensure "cloud spend by service" means the same thing whether it's queried from a scheduled report, an ad hoc analysis, or an API call. Pre-aggregations computed at ingestion time mean common query patterns are answered from materialised results rather than re-derived on demand.
Underneath all of this, blob and bucket storage handles data at rest before it enters the analytical layer. Large-scale cloud billing exports, observability streams from AI workloads, bulk ingestion from enterprise sources — these land in storage first, processed and promoted into the analytical layer on a cadence that matches the source. That separation means a spike in inbound data volume doesn't create pressure on query performance. Ingestion and serving are decoupled by design.
This is also not a fixed stack. The demands on ITFM data infrastructure keep growing — new cloud providers, new AI workloads, new telemetry sources pushing both volume and variety. Yarken is constantly evaluating and adopting new technologies to keep pace. That's possible because the architecture is designed for it, and so is the way we build and ship. When a better tool exists for a given layer, we adopt it. The design absorbs the change without disrupting what's built on top of it.
Cost data ingested in the morning is available for analysis before noon — not Monday. Ad hoc queries against granular cloud billing data return in seconds, not minutes. Scheduled reports that previously required overnight batch runs are served from pre-aggregated results the moment they're requested. And as data volumes grow — more cloud providers, more AI workloads, more observability telemetry — the architecture scales with them, without requiring the programme to restructure how it operates.
The result is that the cycle from data to decision compresses. Teams work with current information. Anomalies are visible before they become problems. Cost reviews stop being a reconciliation exercise and start being a decision forum.
High-velocity isn't a standalone capability. It's what emerges when the rest of the platform is working.
When data is unified, there's no reconciliation step to slow things down. When workflows are connected, insights route directly to the people who act on them. When context is native, attention isn't wasted on signals that don't matter. When execution is structured, response follows immediately from finding. When change doesn't require engineering, the model stays current.
Remove any of those, and speed erodes. Restore them, and the end-to-end cycle — from data arriving to action taken — becomes a genuine competitive capability.
That is what we mean by high-velocity. Not faster screens. A system engineered to compress the distance between information and its consequence, at every layer, at the scale modern ITFM demands.