ARE YOU THINKING ABOUT
AI for SRE?
You could have AI Assistants investigating your alerts in one afternoon. You could roll out "ChatGPT for your environment" for self service investigations tomorrow.
These automated investigations are 1/100th the cost of doing them by hand. They are backed by a massive library of public AI SRE tools your team can extend.
Team too busy to automate? We offer SREs-as-a-Service to help teams through a rough patch. Analysis, advice, automation and even just extra helping hands to get ahead.
The RunWhen platform
STEP 1: Roll Out "ChatGPT" For Engineering Investigations
RunWhen feels like ChatGPT, but it is backed by thousands of automations ("tools") from the industry's largest repository of AI SRE tools.
Give RunWhen to your developers for self-service in pre-production. Give it to your SREs so anyone can do 10x faster root cause analysis and remediation in production.
Get started with a kubeconfig for containers or cloud credentials for VMs and serverless. The platform will import and configure thousands of our default, read-only AI SRE tools in minutes.
STEP 2: Let AI Assistants Investigate Alerts
As your engineers get comfortable using RunWhen for their investigations, let them build their own AI Assistants that use it autonomously.
Connect AI Assistants to any part of your stack that sends notifications -- Observability tools, pipeline notifications, chat channels and ticketing queues come up often.
They perform autonomous triage, research and remediation. When an AI Assistant does not have the automation to resolve an issue by itself, it generates a write-up for Jira, ServiceNow, Github, etc.
Assistants reduce the number of alerts needing human attention by 90%+.
STEP 3: Coach and Extend
While RunWhen is designed to work "out of the box" for modern environments, it is the only AI SRE platform designed to be extended by your team.
Tribal knowledge? AI Assistants take "coaching tips" from your experts that change the course of their investigations.
Customized environments? Add your own tasks with Python, Bash, Ansible, SQL or REST. You can also count on RunWhen to help by providing experienced SREs as-a-Service.
Make it work for every app in every environment in every large enterprises. Even mainframes? Yes.
STEP 4: Measure What Matters
Measure the effectiveness of your reliability program using the same metrics that are used in world's top teams.
RunWhen features hundreds of Service Level Objectives (SLOs) out of the box and the ability to "one-click" add SLOs grounded in production data from your cloud resources, applications (logs), REST APIs, etc.
When teams address items on the Reliability To-Do lists in between incidents, watch your SLOs go up.
Together, this makes for a best-in-class reliability program with measurable, continuous improvement,
You can see the results in a 3-4 week PoV.
Thousands of AI SRE tools configured for your environment in minutes
~60% of the tools in our public library are auto-configured with a kubeconfig alone. Another 25% are auto-configured from cloud and git credentials. This is enough for most teams to get to at-scale production use across Kubernetes, VMs and Serverless environments.
The last 15% can be added over time to integrate with your observability stack, git repositories, application APIs, etc.
Can my team deploy RunWhen?
We work in the strictest financial services, health care and government environments in the industry
Need help with a business case?
Our team can help you build a business case for production environments, non-production environments, or both.
We typically do this after a 30 day PoV so we can use real production data in your environment.
Developer Productivity
“Developers ask us 10 questions per day. Each one implies they were blocked for about an hour. If they ask RunWhen AI Assistants, we get back 10 developer hours per day.”
Reliability vs Cloud Cost Trade-Offs
“RunWhen SLOs say this service is healthy 99.99% of the time. What if we drop to a 98% target and scale replica counts down by half?”
Scale Faster Than Headcount
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“We have multiple cloud environments scaling up… I need either one more person per cloud environment or one person with ten RunWhen AI Assistants to cover both.”
Developer Self-Service
“Developers ask us 10 questions per day. Each one implies they were blocked for about an hour. If they ask RunWhen AI Assistants, we get back 10 developer hours per day.”
Reduce Downtime
“RunWhen can do a minor incident RCA in 2 minutes that typically takes about an hour. Assuming one minor incident per month…”
Reduce Observability Spend
“We can gradually cut back our observability bills in non-prod environments as teams get used to asking RunWhen AI Assistants questions instead of using dashboards.”
Reliability Program Value
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“In between incidents, we followed the RunWhen Reliability To-Do list on our tier-1 services. Our top SLOs went from 96% to 98%, on track for 99% before year end...”
How are other teams using AI?
24/7 developer self service
This team is reducing developer escalations by 62%, giving dev teams their own specialized Engineering Assistants to troubleshoot CI/CD and infrastructure issues in shared environments.
Bring on-call back in-house
This team is reducing MTTR and saving cost, replacing an under-performing outsourced on-call service. They are giving Engineering Assistants to their expert SREs that respond to alerts by drafting tickets.
A (paid) community?
Interested in turning your hard-earned production experience into AI-ready automation? Expert authors in our community receive royalties and bounties when RunWhen customers use their automation. Note - expect rigorous human and AI code reviews and continuous testing requirements to join the program.
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Reduce observability costs? Let us show you how.
Unlike AI SRE tools built exclusively on observability data, our system leverages automation that pulls LLM-ready insights directly from your environment.
This means less observability spend rather than more, and less token spend processing data that was not built with LLMs in mind.
