Every market data vendor quotes 99.9% uptime. On its own, it tells you almost nothing about whether a feed will hold up when your platform needs it to.
Modern market data across FX, metals, CFDs, and crypto runs continuously. There is no privileged window, no scheduled downtime the vendor can hide behind, no equivalent to a closing bell. Every second of every session has to work. A percentage like 99.9% treats availability as a running average across the year; a real platform experiences availability as a binary at every moment. The vendors who understand this don't lead with the uptime number. They lead with the architecture that makes it almost irrelevant, and back it with SLA terms that put money on the table.
The 8.76-Hour Problem
99.9% permits 8 hours 45 minutes of downtime per year. 99.95% is 4 hours 22 minutes. 99.99% is 52 minutes. Each is better than the last, and each is still time your feed is allowed to be down while remaining within contract.
But the number itself is the wrong thing to argue about. A serious market data vendor doesn't design to meet a percentage; they design so the percentage doesn't get tested. When a buyer treats 99.9% as a headline metric, they've already accepted the wrong frame. The right question is: why would this feed go down at all, and what stops it from reaching me if it does? We covered a related question, how our feed quality compares to the institutional benchmarks, in our comparison with Reuters and Bloomberg
Uptime Isn't the Failure That Hurts Most
The trouble with 99.9% is that it only measures whether the vendor's service is reachable. It says nothing about whether the data reaching you is actually right, whether it covers everything you need, or whether it arrives in time to be useful. And those are the failures that quietly cost you money.
Consider who inside a business is actually depending on a market data feed: A pricing engine turning ticks into client quotes. A sales team structuring a better rate for a corporate customer. A treasury desk deciding when to hedge. Every one of them is trusting the feed to tell the truth. When the feed lies without alerting anyone, the damage always lands somewhere.
Here are the failures that a basic uptime metric hides:
Stale Prices: A source freezes upstream, the same tick keeps streaming, and everything looks fine on the monitoring dashboard. Meanwhile, your pricing engine quotes rates no market would honor, and your risk desk runs its P&L against a mid that stopped being accurate ten minutes ago. Because everything looks technically healthy, nobody catches it until a client complains.
Partial Coverage Dropouts: One currency pair drops out while everything else keeps flowing. The vendor's status page shows 99.99% availability, but for the hour that USD/MXN was dark during a rate decision, your payments corridor into Mexico had a full outage at the worst possible moment. Aggregate uptime numbers never surface this kind of failure.
Latency Spikes: This is the failure mode clients exploit fastest. A feed running two seconds behind during a central bank surprise is still "up", but two seconds is forever when the market is moving. Sophisticated clients hit your stale quotes before you can pull them. The money going out of your P&L in those seconds goes straight into your clients' pockets.
None of this appears in a headline uptime number. All of it turns into lost revenue and unhappy clients.
The Real Story Is Architecture
Serious vendors don't try to hit 99.9% by being lucky. They engineer around the assumption that things will fail, and design so that the failure doesn't reach the customer. Three architectural commitments separate them from the rest:
Redundant infrastructure: Multiple servers across multiple geographic locations, with automatic failover measured in seconds. When one server or region has a problem, the feed continues from another without interruption.
Multiple data sources as active backup: A well-architected feed doesn't rely on a single upstream source. Multiple contributor panels, aggregation paths, and backup providers integrated into the feed mean that a failure at one source doesn't blank the customer. The vendor's operations team resolves it in the background.
Continuous validation: Redundancy is useless if the failover destination is silently corrupted. Serious vendors cross-check contributor quotes, compare derived crosses against direct pairs, and monitor spread behavior. When something is wrong at the source, the vendor knows before the customer does.
Zero-downtime maintenance: In 2026, a 24/7 service is expected to be up. Full stop. There is no defensible reason a modern market data vendor should require scheduled downtime for upgrades. Rolling deployments and version-aware routing let engineering teams push improvements continuously. Any vendor that quietly excludes "scheduled maintenance" from their uptime calculation is telling you their architecture requires them to.
Ask any vendor: what stops your feed from going down? If the answer is a number, they're describing the SLA. If the answer is a diagram, they're describing the architecture.
What a Real Market Data SLA Adds
A serious SLA does two things that a headline uptime percentage does not: it commits to response when something goes wrong, and it puts money on the line when the commitment isn't met.
Response times, not just uptime: When a customer calls at 3 a.m. because a rate looks wrong, the question isn't whether the vendor was "up", it's who at the vendor is engaged on the problem, and how fast. A meaningful SLA specifies named technical contacts and response commitments measured in minutes, not hours.
Warranties, not token credits: Most SLA credits are a small discount against future fees, applied after a claims process that takes weeks. A serious SLA offers warranty terms linked to refunds, structured so the consequence to the vendor is proportional to the impact on the customer.
Why the 7–14 Day Trial Misses the Point
Many evaluation processes begin with a free trial. It validates real things: that endpoints work, that documentation is accurate, that the SDK integrates cleanly. However, it validates almost nothing about the questions that matter for a production platform:
- Does the vendor have redundant infrastructure, or is the feed sitting on a single server that hasn't failed yet?
- What happens during a real market stress event, an NFP surprise, or a flash move?
- If something goes wrong at 3 a.m., who picks up, and how fast?
- What financial recourse exists if the feed fails during a moment that damages your platform?
Two weeks of clean tick data during a quiet period tells you the API works; it tells you nothing about how the vendor behaves under stress. A serious buyer runs both: a trial to validate the integration, and an SLA review to validate the commitment.
The Real Test
An SLA is not primarily a document about risk transfer. It's a credibility signal. A vendor willing to commit to specific response times and warranty terms that return fees is telling you they've built an operation confident enough to put money behind.
99.9% is a floor. It tells you nothing about why their feed would stay up, what they've done to make sure it does, or what happens when it doesn't.
Architecture Meets Accountability at TraderMade
TraderMade has operated for three years without a customer-impacting outage, and without ever taking scheduled maintenance downtime. Verify last year history on our status page. That record isn't luck; it's the result of redundant servers across regions, multiple data sources as active backup, continuous validation, and rolling deployments.
TraderMade provides real-time and historical market data across FX, metals, CFDs, and crypto, designed for continuous 24/7 availability. Our SLA terms include named response commitments and warranty structures backed by refunds against failure, not discounts on future invoices. If you're renewing an existing provider or evaluating alternatives, we're happy to walk through our SLA and architecture against the questions in this article.