Vercel

Vercel exposes several object types through its REST API: deployments, projects, team members, domains, and access groups. This integration focuses exclusively on deployments, which represent the fundamental unit of delivery in Vercel and the only object type that carries direct per-engineer attribution via the creator field. Projects are team-owned and do not expose an individual creator at the list endpoint level; domains and access groups reflect infrastructure configuration rather than engineering activity. Five KPIs were retained from eleven candidates, selected against three criteria: ability to attribute to an owner, resistance to gaming, and balance between leading and lagging indicators.

Delivery velocity: reading frequency against speed

Production Deployments counts the number of deployments that reached a ready state in the production environment, attributed to the engineer who triggered them. Average Lead Time measures the elapsed time between triggering a deployment and the moment it becomes production-ready. These two indicators correspond directly to the first two DORA metrics — deployment frequency and lead time for changes — and their relationship reveals more than each measures independently. A high Production Deployments count combined with a long Average Lead Time identifies a pattern of frequent, slow releases: the engineer ships often but the pipeline introduces meaningful latency before code reaches users. The inverse pattern, low frequency with short lead time, may indicate infrequent but well-optimized releases, or an underutilization of Vercel's deployment capabilities. The combination frames the deployment cadence more precisely than either indicator alone.

Delivery quality: neutralizing the gaming risk on frequency

Production Deployments carries a direct gaming risk: an engineer incentivized on deployment frequency can artificially inflate the count by splitting changes into many small, low-risk deployments or by re-triggering deployments unnecessarily. Three quality indicators are paired with this leading metric precisely to neutralize this risk.

Deployment Success Rate measures the proportion of deployment attempts that succeed, excluding canceled deployments from the denominator. Canceled deployments are excluded because CI systems often cancel in-flight builds when a new commit supersedes them, attributing a cancellation to the engineer would penalize standard continuous integration behavior rather than quality. Build Error Count isolates failed deployments as an absolute count, which complements the ratio: an engineer with a low error rate but high volume may still generate a meaningful absolute number of errors. Rollback Count captures incidents after the fact, when a production deployment is explicitly reverted to a prior version. Where Success Rate and Error Count measure what fails before reaching users, Rollback Count measures what fails after — it is the closest approximation to mean time to recovery that Vercel data supports. Reading the three together maps the full quality profile of an engineer's deployment activity: frequency of failure, severity of failure, and frequency of incident-driven recovery.

Scope and limits of the integration

Vercel captures the deployment layer of the engineering value chain and nothing upstream of it. Code review, pull request cycle time, sprint planning, and feature scoping are invisible to this integration; a complete picture of engineering performance requires complementary data from tools such as GitHub or Linear. Within its scope, Vercel records deployment events but does not measure the quality of what is deployed: a successful production deployment may correspond to a robust feature or to a trivial configuration change, and the API does not distinguish between the two. Average build duration was evaluated as a candidate KPI and rejected: it is gameable through adversarial splitting of commits to reduce measured per-deployment duration, making it unsuitable for OKR use. The reliability of all five KPIs depends on consistent use of the production target environment by the team. Teams that deploy to production via alternative mechanisms outside Vercel — direct infrastructure commands, CI pipelines that bypass Vercel — will produce incomplete attribution, and the indicators will undercount the actual delivery volume.