Platforms · Software · Providers · Markets
Devin Hartcastle
In week one, a cloud environment can be provisioned, configured and running a test feed while a colocation order is still waiting on a cage, power allocation or cross-connect. By week six, that cloud service may be live, but its route to a venue, performance under load and recovery process still need proving. A colocation build may have taken longer to start, yet now offer a tested, direct network path.
That timeline is not universal. It is a useful reminder that deployment speed and production readiness are different measures.
For brokers and prop firms, the choice is not simply cloud flexibility against colocation speed. It is a decision about where each part of the trading system runs, which latency matters, and who owns each failure domain.
Week one and week six are different problems
Cloud usually wins the initial setup race. Teams can create compute, storage and network resources without waiting for hardware delivery or a data-centre installation window.
Infrastructure as code makes repeatable test environments practical, and capacity can be resized without ordering another server. That is valuable for development, analytics, back-office services and workloads with variable demand.
Production readiness brings other dependencies. Exchange or liquidity-provider access may require approval, private connectivity, firewall reviews, session configuration and testing with counterparties.
Those tasks take time in either model. In colocation, add hardware procurement, rack space, power, cabling and remote-hands coordination. Typical lead times for a new colo deployment range from four to twelve weeks, but a cross-connect or venue approval can extend that.
Cloud changes the work rather than removing it. Teams still need to validate instance placement, CPU behaviour, network routes, time synchronisation and recovery. A virtual machine that starts in minutes does not prove that a trading session will meet its latency target at peak load. Track dependency lead times alongside compute provisioning when estimating a launch.
Compare the path, not the label
Latency figures only help when their endpoints are clear. A network round trip between two hosts is not the same as the time from market-data arrival to strategy decision, or order submission to acknowledgement. That end-to-end measure also includes the application, kernel or user-space networking stack, risk checks, gateways and the venue's own processing.
Typical planning ranges vary with location, hardware, carrier and measurement method. A same-hall colocation path can be in the tens of microseconds. Cloud host-to-host paths are often measured in tenths of a millisecond or more, with additional variation across zones or external connections.
These are broad market ranges, not service guarantees. Measure the route that the live workload will use.
Record p50, p99 and p99.9 latency, not just an average. A low median can hide queueing during bursts, noisy-neighbour effects, retransmissions or a route change.
Use hardware timestamping where available and check clock synchronisation, such as PTP, before comparing timestamps across systems. Measure under realistic message rates and include recovery tests, not only an idle benchmark.
Exchange proximity is not a universal advantage. A firm running a strategy that depends on reacting to a venue's feed may value a short, stable path.
A broker's client portal, reporting stack or risk analytics service may gain little from paying for the same physical proximity. The trading infrastructure coverage at Trading Technology provides further context on the systems that sit around these choices.
Cost and capacity have different shapes
The figures below are typical market planning ranges, not quotes or audited results for any provider. They assume a modest production footprint and exclude engineering labour, trading software licences and venue fees. Local power prices, redundancy requirements and reserved capacity can move costs substantially.
| Approach | Typical latency reference | Typical time to production | Typical recurring infrastructure cost |
|---|---|---|---|
| Colocation, same facility | 5-100 microseconds host-to-host, depending on path and equipment | 4-12 weeks | $3,000-$20,000 per month for rack, power and connectivity |
| Cloud, within one region | 0.1-1 millisecond host-to-host in a controlled placement | Several days to 2 weeks | $2,000-$25,000 per month for a small production compute and network footprint |
| Hybrid, cloud to colo over private connectivity | 0.2-2 milliseconds round trip across a metro path | 3-10 weeks, depending on circuit delivery | $1,000-$10,000 per month for private connectivity, plus both environments |
Colocation costs are less elastic. Hardware is a capital purchase, rack and power commitments persist, and spare capacity sits idle until needed. The trade is control over server selection, NICs, BIOS settings, cooling and physical network design. Cloud costs move with instance size, data transfer, storage, support and reservation choices. Scaling is quicker, but an unbounded test or high-volume egress can produce a bill that is harder to predict.
Resilience also has a price. A second site, diverse carriers, spare appliances and replicated state add cost in either model.
Cloud availability zones can separate some failures, but they do not automatically protect a firm from application bugs, bad configuration or a dependency shared across zones. Map the actual failure domains before counting a second environment as a disaster-recovery plan.
Choose by workload, not by slogan
Colocation is a stronger fit where the workload has strict, measurable latency needs, sustained high message rates, specialised network hardware or a required physical connection to a venue. It also suits teams that can operate hardware and maintain capacity plans. The case weakens if the venue is not nearby, the strategy tolerates more delay, or the cost of the footprint exceeds the value of predictable performance.
- Consider colo for market-data capture, latency-sensitive order gateways and services that depend on direct venue or liquidity-provider connections.
- Consider cloud for development, CI, analytics, client-facing services and workloads with variable demand or frequent environment changes.
- Check the boundary when traffic crosses between environments. A slow or poorly managed link can erase the benefit of placing one component close to the venue.
Cloud can support latency-sensitive systems, particularly with carefully selected instance families, dedicated capacity, placement controls and private connectivity. But these controls need testing and cost. Pinning processes, isolating CPU cores, avoiding oversubscribed paths and monitoring packet loss matter more than a generic claim about cloud performance. Conversely, colocation does not guarantee speed if the application is inefficient or the network is congested.
Teams comparing suppliers and platform components can also use Trading Technology's platform operations reporting as one input. The operational questions remain local: which team patches hosts, manages certificates, rotates secrets, monitors circuits and owns an incident at 03:00?
Build a hybrid path you can operate
Many firms use both. A common design keeps venue-facing gateways and latency-sensitive processing in colocation, while using cloud for build pipelines, research, reporting, client services or secondary environments.
The split is useful only if each service has a clear owner and the traffic between sites is understood. Avoid distributing one order path across environments unless the additional network hop and failure handling are deliberate.
Before committing, test a representative path with production-like hardware and traffic. Capture latency distributions and packet loss over several days, including busy periods. Inject a circuit failure, remove a host, restart a gateway and verify that sessions, sequence recovery and risk controls behave as expected. Compare the result with a documented service objective, not a vendor's best-case figure.
- Draw the order and market-data paths, including DNS, authentication, risk checks and external links.
- Set targets for p99 and p99.9 latency, packet loss, recovery time and capacity headroom.
- Price the full footprint: connectivity, power, support, data transfer, spares and on-call coverage.
- Run failover tests and confirm who can act, what state survives and how normal service is restored.
Revisit the placement when volumes, venues or strategies change. The mistake most readers will still make is choosing by headline latency or monthly price before measuring their own end-to-end path and failure modes.
Frequently Asked Questions
Is colocation always faster than cloud for trading?
No. Colocation can provide a shorter, more controlled path to a nearby venue, but application processing and network design still determine end-to-end performance. Measure the live path at p99 and p99.9 under realistic load.
How long does trading infrastructure colocation usually take?
A new colo deployment commonly takes four to twelve weeks, depending on rack availability, hardware, cross-connects and venue approvals. Cloud resources can be provisioned in days, although production connectivity and validation can take much longer.
Which trading workloads are a good fit for cloud?
Cloud often suits development, analytics, reporting, client-facing services and workloads with variable capacity needs. It can also run latency-sensitive components, but firms should test placement, network behaviour, tail latency and recovery before committing the order path.
