Real-time anomalies
Most tools, the clouds' own included, tell you spend is up, days later. We alert you within minutes with what spiked, which customer, team or feature drove it, and what to do next.
AWS, Google Cloud, Azure and 13 more · Alerts in Slack, Teams and email
checkout-workers-asgc7i.4xlarge+$3,020checkout-cacher7g.2xlarge+$710nat-us-east-1aNAT data processed+$450checkout-svc 4.2 deploy at 08:55 tripled worker replicas for ACME Corp.Next step: cap replicas at 12 or roll back to 4.1.
#payments-alerts09:31
FinOps channel09:31A rolling sample of real cost spikes we caught and prevented in the last 24 hours.
Google Cloud · Oct 24, 2026
Prevented cost
$2,189,612
what every alert tells you
Every anomaly arrives with the context to act on it: who drove the spend, which resources moved, what likely caused it and what to do next. No digging through billing consoles.
See it on your own spend
prod-payments+$4,180Under reviewsupport-bot+$2,640Resolvedanalytics-prod+$910Needs reviewETL_WH+$530Resolvedeu-crm+$140Resolvedother tools tell you spend is up
A billing alert arrives days late with a percentage. Ours arrives within minutes with the workload, the owner and the likely cause, so the right team can fix it before it shows up on the invoice.
The deploy that caused it.
The owner, not a guess.
No end-of-month surprises.
Spikes caught, not reported.
signal, not noise
Models expected spend per service, with seasonality built in.
Info, Warning or Critical. Route each level where it belongs.
Related anomalies are grouped, so one spike pages once.
Small blips under a minimum spend never become alerts.
Tune how tight the expected range is, and how many alerts you get.
Track status and comments until each spike is closed.
no setup projects
Billing data plus real-time usage, such as AWS CloudTrail. No agents to install for detection.
A few days of usage sets the expected range for every service, weekends and peaks included.
Alerts go to Slack, Teams or email by severity, and can start a CloudFlow automatically.
We get alerted on any anomalous changes, oftentimes because someone misconfigured a job, or a workload is running in an unintended way. And real-time alerting just takes anomaly detection with DoiT to the next level.
Trevor Marshall, CTO & Co-Founder, Current
Thanks to real-time anomalies detection, we have more time to focus on other areas in our business, trusting that we'll get alerted whenever there is any deviation in cloud usage.
Raouf Ayoub, Head of FP&A, Camunda
Real-time anomalies detection helped me realize we were overspending on CloudWatch, and also helped me realize the cost impact of using S3 Deep Archive storage.
Meir Gabay, DevOps Engineer, binah.ai
every bill you pay
The same detection runs across your clouds, data platforms and AI providers, so a spike anywhere reaches the right team.
Real-time detection reads usage data, such as AWS CloudTrail events, and alerts within minutes of a spike. Billing-based detection covers every service and runs continuously as new billing data arrives.
Each anomaly lists the resources, SKUs, accounts and tags behind it. With our sensor in place, usage anomalies also break down by the customer, team or feature that drove the spend.
Models learn each service's normal spend, seasonality included. Spikes must clear a minimum spend, related anomalies are grouped into one incident, and you choose the sensitivity and which severities alert whom.
Slack, Microsoft Teams, email and the console. Any anomaly event can also trigger a CloudFlow, for example to open a Jira ticket or notify the owning team.
AWS, Google Cloud, Microsoft Azure and Oracle Cloud, plus Snowflake, Databricks, Datadog, OpenAI, Anthropic, ClickHouse Cloud, Cloudflare, Cursor, Elastic Cloud, GitHub, Grafana Cloud and Vercel.
Alerts within minutes, with the cause attached.