Cloud cost optimization for capital markets.

Here is how each class is optimised, plus the data costs both share.

How do you optimise the low latency trading core?

The trading core runs continuously and predictably, which makes it the natural home for commitment coverage. Savings Plans, Reserved Instances, Reservations, committed use discounts, and Universal Credits all discount steeply against on demand pricing, and a workload that runs every trading day with stable shape can carry high coverage on a defensible forecast. The discipline here is precision, not maximisation: cover the durable base, keep a thin on demand margin for change, and avoid committing to instance families you may modernise away from. Current generation instances and efficient processor families deliver the performance this tier needs while lowering the rate. The cross cloud commitment logic in our cost optimization guide applies directly, sized to a workload that barely moves.

How do you handle bursty risk and analytics compute?

• Spot and preemptible for batch. End of day risk, backtesting, and parameter sweeps are fault tolerant and checkpointable, so they run well on spot or preemptible capacity that discounts compute steeply for interruption risk the job can absorb.
• Scale to zero between runs. Analytical clusters should exist only while a run is active. Standing capacity that sits idle between the close and the next morning is pure waste.
• Right sized grids. Compute grids are often sized for the worst case run and left there. Match grid size to the actual job and let it scale, rather than holding peak capacity permanently.
• GPU discipline for model work. Quantitative model training and AI workloads need GPU governance: reserve the steady base, burst the rest on demand or spot, and govern provisioned throughput and capacity reservations explicitly.

What about market data and egress?

Compute is the obvious line, but in capital markets data costs are often the quiet one. Large market data feeds, tick history, and reference datasets accumulate storage cost, and much of it is rarely accessed once a trading day closes, so tiering cold history to cheaper storage is a standing win. Data transfer is the other eater: moving data between regions, across availability zones, or out to external venues and counterparties incurs egress charges that surprise teams focused on compute. On AWS the Cost and Usage Report exposes these lines; Azure, GCP, and OCI each have their equivalents, and OCI egress is materially cheaper than the hyperscalers, which can matter for data heavy workflows. The point is to give data the same scrutiny as compute, because in this sector it carries a comparable share of the bill.

How does regulation shape the approach?

Capital markets firms operate under data retention, audit, and resilience obligations that constrain some optimization moves. Required retention periods mean certain data cannot simply be deleted, but it can be moved to cheaper archival tiers that still satisfy the obligation. Resilience requirements mean the trading core carries redundancy that is not optional, so optimization focuses on efficient sizing within the required posture rather than on removing it. Audit trails for financial data must be preserved, which reinforces the allocation discipline the rest of the program needs anyway. None of these obligations blocks optimization; they shape which lever applies where, and a buyer side advisor that understands the constraints optimises within them rather than against them.

A worked example

Optimise your capital markets cloud estate with us

We help trading firms and capital markets desks cut cloud cost across AWS, Azure, GCP, and OCI, separating the latency sensitive core from bursty analytics and bringing the same discipline to market data and egress. We take zero provider commissions and work on a Fixed Fee or a no risk Gainshare basis, guaranteed: we reduce your cloud spend or we reimburse our service fee. Book a strategy call to scope your estate.