An AI on-call engineer that actually reads your stack
VeloOps watches your logs and metrics, correlates the signals across services, and escalates with an explanation instead of a raw alert. Here is exactly what it does.
Real-time log & metrics monitoring
VeloOps connects to your existing telemetry — logs, metrics and traces — and builds a rolling baseline for every service it watches. Anomaly detection runs continuously against that baseline, so a latency spike, an error-rate climb, or a memory leak gets flagged within seconds of first appearing, well before it crosses a static threshold alert.
- Streaming ingestion from CloudWatch, Datadog, or your existing log pipeline
- Per-service baselines that adapt to normal daily and weekly traffic patterns
- Anomaly detection on logs, metrics and error rates, not just uptime checks
- No manual threshold tuning — baselines retrain automatically as traffic shifts
In practice
VeloOps streams your logs and metrics continuously and flags anomalies the moment they deviate from a service’s normal baseline — not five minutes later in a batch job.
Included from the Free plan up
AI root-cause analysis
Most incident time is spent not fixing the problem but finding it — cross-referencing a dashboard, a deploy log and a Slack thread by hand. VeloOps runs that correlation automatically: it lines up the anomaly against your deploy history, infrastructure changes and dependency graph, then generates a plain-English summary of the most likely root cause with the supporting evidence attached, before a human has opened a single dashboard.
- Correlates errors, deploys and infra changes into a single explanation
- Ranks likely root causes by confidence, with the evidence behind each one
- Understands service dependencies, so it can trace a failure upstream
- Powered by a hosted foundation model, isolated to your workspace
In practice
When something breaks, VeloOps correlates the error spike with recent deploys, config changes and upstream infra events, and explains the likely cause in plain English.
Included from the Team plan up
Smart alerting that cuts the noise
A single bad deploy can trigger alerts from a dozen different checks — error rate, latency, queue depth, downstream timeouts — all at once. VeloOps recognises when alerts share a common cause and groups them into a single incident with one page, instead of twenty. Alert routing and severity are configurable per service, so on-call only gets paged for what actually needs a human right now.
- Automatic grouping of related alerts into a single incident
- Deduplication so the same failure never pages the same person twice
- Configurable severity and routing rules per service or team
- Escalation policies that respect your existing on-call schedule
In practice
Related signals get grouped into one incident instead of paging your team twenty times for the same underlying failure.
Included from the Team plan up
Auto-generated incident timelines
While an incident is live, VeloOps builds a timeline of every relevant event automatically: the deploy that shipped, the first anomaly, each alert fired, and the point of recovery. When the incident closes, that timeline becomes the skeleton of a postmortem draft — summary, timeline, likely root cause and suggested follow-ups — ready for your team to review, correct and publish rather than assemble from scratch at 2am.
- Timestamped timeline built automatically as the incident unfolds
- Postmortem draft generated on resolution, in your team’s format
- Links every timeline event back to the underlying log or metric
- Exportable to Markdown, Confluence or your existing docs tool
In practice
Every incident gets a timestamped timeline of what happened and when — and a first-draft postmortem you edit instead of write from scratch.
Included from the Team plan up
Works inside your existing workflow
Incidents get pushed to the channel your team already watches — Slack or Microsoft Teams — with the root-cause summary attached inline, and escalate through PagerDuty using your existing on-call schedule. Nobody has to learn a new dashboard mid-incident; the information comes to them.
- Slack and Microsoft Teams incident channels with inline root-cause summaries
- PagerDuty escalation using your existing on-call schedules
- Two-way sync — acknowledge or resolve from Slack, VeloOps updates the record
- Webhooks and API access for anything not covered out of the box
In practice
Slack, Microsoft Teams and PagerDuty integrations mean your team responds where it already works, with no new tool to check.
Included from the Team plan up
One-click connect to your stack
Setup is a connector, not a migration. Authorise a read-only connection to CloudWatch or Datadog, or point VeloOps at your existing log pipeline, and it starts indexing telemetry and building service baselines within minutes. An optional lightweight agent adds deeper trace correlation later, but nothing blocks you from seeing value on day one.
- One-click CloudWatch and Datadog connectors, read-only by default
- Ingests from existing log pipelines (Fluent Bit, Logstash, Vector, etc.)
- Baselines start building within minutes of connecting a service
- Optional lightweight agent for deeper trace-level correlation
In practice
Point VeloOps at CloudWatch, Datadog, or your existing log pipeline and it starts building baselines immediately — no agents to deploy on day one.
Included from the Free plan up
Connect your stack → AI monitors 24/7 → Get root cause in seconds
No agents to deploy on day one, and a baseline that starts building within minutes of connecting a service.
- STEP 01
Connect your stack
One-click connect CloudWatch, Datadog, or your existing log pipeline. Nothing to migrate, and baselines start building within minutes.
- STEP 02
AI monitors 24/7
VeloOps watches logs, metrics and deploys continuously, building a baseline for every service and flagging anomalies the moment they appear.
- STEP 03
Get root cause in seconds
When something breaks, VeloOps correlates the signals and hands you a plain-English root-cause summary — before you have opened a dashboard.
Connects to your existing stack
Keep your monitoring source, your chat tool and your on-call schedule. VeloOps plugs into them rather than asking your team to move.
Business and Enterprise plans include API access and webhooks for anything not listed. Compare plans.
Watching your infrastructure is only useful if it’s safe
VeloOps handles your logs, metrics and deploy history, so the controls around that data matter as much as the anomalies it catches. Here is how it is protected — and what we will put in writing for a security review.
Enterprise agreements include a data processing addendum, configurable data residency, and support through your security review.
Encrypted in transit and at rest
Telemetry is protected with TLS in transit, and stored logs and metrics are encrypted at rest with managed keys. Each workspace is logically isolated from every other.
Your telemetry is not training data
Your logs, metrics and incident data are used only to monitor and analyse your own workspace. We do not use customer telemetry to train models shared across accounts, and neither do our model providers.
Access you can audit
Role-based access control and an audit log on Business and Enterprise plans, so you can see who changed what and when — the questions a platform team asks first.
Built to stay up
Redundant infrastructure across multiple availability zones, continuous monitoring, automated encrypted backups, and a documented recovery process.
Stop finding out about incidents from your customers
Connect your stack, let VeloOps build a baseline, and get your first AI root-cause summary this week. The Free plan needs no credit card.
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