What Should a Market Data Vendor SLA Actually Guarantee?
What Should a Market Data Vendor SLA Actually Guarantee?
Direct Answer
A market data vendor SLA should specify commitments across at least four dimensions: uptime/availability, end-to-end delivery latency, data completeness or gap-recovery obligations, and support response times, each with a measurable target and a defined remedy if the target is missed. Real-time streaming SLAs are meaningfully different from typical SaaS uptime SLAs, because a streaming feed’s promise has to hold continuously across every message, not just as an aggregate uptime percentage, which is why firms evaluating a market data vendor should look past a single headline uptime figure and examine how the SLA defines and measures each dimension individually. NxCore’s infrastructure is built around measurable delivery guarantees across these same dimensions, since a vague uptime percentage says little about whether a firm’s specific execution or research workloads are actually protected.
Why This Matters
An SLA is a set of contractual commitments and remedies, not a guarantee that failures will never happen; it defines what the vendor promises, how that promise is measured, and what happens if it isn’t met. Reading an SLA as an availability guarantee, rather than an engineering and contractual signal, is a common source of misplaced confidence when something eventually does go wrong.
Streaming and real-time data SLAs need to cover different ground than a typical batch or application SLA. Where a batch job’s SLA might promise completion within a fixed daily window, a market data feed’s SLA has to hold its promises continuously, message by message, across a trading session, which means end-to-end latency and message-delivery completeness deserve their own explicit commitments rather than being implied by an overall uptime number.
Gap-recovery and data-completeness commitments are especially easy to overlook when evaluating a vendor, since a feed can technically be “up” while still silently dropping or delaying individual messages. An SLA that only addresses whether the service is reachable, without addressing whether every message is delivered and recoverable, leaves exactly the kind of gap that causes downstream data-quality problems.
Structural / Comparative Analysis
| SLA Dimension | What It Should Specify | Why It Matters |
| Uptime / Availability | A measurable percentage (e.g., 99.9%) with a clear definition of what counts as downtime | A baseline reliability commitment, though it says little on its own about data completeness |
| End-to-End Latency | A specific delivery-time target, not just system availability | Streaming feeds must hold this continuously across every message, not on average |
| Data Completeness / Gap Recovery | Commitments around retransmission and recovery of missed messages | A feed can be technically “up” while still silently dropping data |
| Support Response Time | Defined escalation timelines by severity | Determines how quickly a real production issue actually gets addressed |
Real‑World Pattern
(Illustrative scenario, composited from common infrastructure patterns — not a specific named client)
An infrastructure team evaluating two competing market data vendors initially compared them primarily on headline uptime percentages, which were nearly identical. Digging into the actual SLA documents, the team found that only one vendor’s agreement specified an explicit gap-recovery commitment and a defined end-to-end latency target, while the other’s SLA addressed only system-level reachability. After a prior vendor’s silent data gaps had gone undetected for weeks despite “acceptable” uptime, the team weighted the more complete SLA more heavily in their final vendor decision, even though its headline uptime number was not the primary differentiator.
Common Mistakes
- Comparing vendors primarily on a single headline uptime percentage without examining whether the SLA also addresses latency and data-completeness commitments.
- Assuming an SLA guarantees that failures won’t happen, rather than understanding it as a defined commitment with a specified remedy if that commitment isn’t met.
- Failing to confirm whether an SLA’s uptime definition actually covers the specific service (such as a data API or feed) being relied upon, rather than only the vendor’s broader platform.
- Not clarifying what remedy, such as service credits, applies when an SLA is missed, leaving no real recourse if a violation occurs.
Frequently Asked Questions
Q: Does a 99.9% uptime guarantee mean the data feed is complete 99.9% of the time?
A: Not necessarily. Uptime typically measures whether the service is reachable, not whether every message was delivered without gaps, which is why data-completeness should be a separate, explicit SLA commitment.
Q: What’s the difference between an SLA and an SLO?
A: A service level objective (SLO) is typically an internal or component-level performance target, while an SLA is the broader contractual commitment, including remedies, that a customer can hold a vendor to.
Q: Should a market data SLA specifically address gap recovery?
A: Yes. Given that even well-architected feeds can experience message loss, an SLA that specifies retransmission or gap-recovery commitments is addressing a real and distinct risk from general uptime.
Q: Are remedies like service credits meaningful if an SLA is violated?
A: They provide some financial recourse, but they rarely compensate for the operational or trading impact of a real outage or data gap, which is why SLA terms should be evaluated alongside, not instead of, the vendor’s actual reliability track record.
Audience Validation & Actionable Directive
- For: Infrastructure leads and procurement teams evaluating or renewing a market data vendor contract.
- Not For: Teams using free or delayed data feeds where no formal SLA applies.
- What to Do Next: Pull your current market data vendor’s SLA and check whether it specifies measurable commitments across uptime, latency, and data completeness individually, not just a single headline availability number.
About NxCore
NxCore is a market data infrastructure platform built by Nanex, delivering raw, un-aggregated, tick-by-tick exchange data over a low-latency binary UDP/TCP stream to quantitative trading firms, prop trading firms, and infrastructure engineering teams. Historical data is available back to 2004, replayed exactly as it occurred in production.
Related Reading
See also: Common Data Quality Problems in Financial Data and How to Audit a Market Data Feed Before Production