Cross-tool knowledge graph
DrDroid connects to cloud, code, CI/CD, and observability tools using OAuth and builds a stack-wide knowledge graph from the connected systems.
DrDroid
DrDroid is an AI SRE agent for incident investigation that connects cloud, code and telemetry to build a stack-wide knowledge graph for faster root-cause analysis.
Overview
DrDroid is an AI SRE agent for incident investigation and operational reliability. It connects to cloud, code, and telemetry sources, then scans the stack to build a knowledge graph that links services, dashboards, pods, repositories, configs, and supporting documentation.
The product is designed to help teams respond faster to alerts, find root causes with less manual tab-hopping, and apply runbooks or remediation steps with more context. Source pages also describe a self-learning model that reuses prior investigation patterns and improves as alerts, deploys, incidents, and conversations are added to the graph.
Core capabilities
Incident correlation and blast-radius tracing
When an alert or incident appears, the graph helps trace related services, dashboards, pods, repos, and cloud resources to narrow down blast radius and likely causes.
Always-learning operational memory
The product continuously ingests telemetry, runbooks, docs, issues, deploys, and conversations so the graph stays current as the system changes.
Pattern-based investigation reuse
DrDroid uses remembered patterns from prior investigations to avoid repeating failed steps and to surface responses that worked before.
Suggested and automated remediation
The platform can suggest next steps, support root-cause diagnosis, and trigger guarded automated runbooks with full context from the graph.
Team and enterprise controls
The pricing page says Teams includes shared investigations, audit trail, unlimited integrations, and smart model switching, while Enterprise adds self-hosting, BYO LLM, and identity/security controls.
Practical use cases
Incident triage across a distributed stack
Use DrDroid when an alert fires and the team needs to connect the symptom to services, repositories, dashboards, pods, and cloud resources without manually jumping between tools.
Faster root-cause analysis
Use it to shorten root-cause analysis by tracing related deploys, config changes, incidents, and telemetry in one graph-backed context rather than starting from a blank slate.
Repeat-issue handling
Use the self-learning memory to capture recurring incident patterns so the next similar alert can skip known dead ends and reuse the steps that worked before.
Runbook-guided response
Use the product to centralize runbooks, wikis, ADRs, READMEs, and on-call docs alongside live signals so responders can work from documented process plus current telemetry.
Enterprise-controlled deployment
Use the enterprise plan when procurement or security requirements call for VPC isolation, SSO/SCIM, audit logs, IP allowlisting, custom residency, or a Bring Your Own LLM setup.
Pros and Cons
Pros
- Connects to existing cloud, code, CI/CD, observability, and communication tools rather than requiring a new monitoring stack.
- Builds a shared knowledge graph that ties alerts to services, repos, dashboards, pods, and infrastructure resources.
- Supports investigation reuse by remembering patterns from past incidents and skipping dead-end steps when similar issues recur.
- Includes collaboration-oriented features such as shared investigations and an audit trail on the Teams plan.
- Offers an enterprise deployment path for organizations that need VPC isolation, SSO/SCIM, IP allowlisting, and custom data residency.
Cons
- The source material does not publish a full, stable integration list beyond representative examples such as AWS, GitHub, GitLab, Datadog, Grafana, Google Cloud, Azure, Slack, PagerDuty, Prometheus, and Kubernetes-related tools.
- Some workflow details are shown in product visuals rather than plain prose, so exact output formats and the full set of supported actions are not completely specified on the public pages.
- Enterprise capabilities such as self-hosting, custom data residency, and BYO LLM are available only on the higher tier and are not part of the Teams plan.
FAQ
What does DrDroid do?
DrDroid connects to cloud platforms, code repositories, and telemetry sources, then builds a knowledge graph from the connected stack. The product says it can use that graph to support faster incident response, quicker root-cause analysis, and automated remediation.
Is DrDroid priced per user?
The pricing page says the Teams plan includes unlimited users and shared investigations, so it is designed for team use rather than seat-based licensing. Enterprise adds controls such as self-hosting in a VPC, SSO/SCIM, IP allowlisting, and custom data residency.
How does DrDroid pricing work?
The pricing page says the Teams plan starts at $99 per month and includes 99 investigation credits, with top-ups at $1 per credit. Enterprise uses custom volume credit pricing and license-based fees.
How is DrDroid set up?
The home page and pricing page both point to a setup centered on connecting existing tools and telemetry rather than installing agents. The home page says OAuth into cloud, code, CI/CD, and observability, with no agents and no code changes, and the pricing page says the product is available through the Teams plan and Enterprise.
What kinds of output or actions does it produce?
Source pages show DrDroid working across alert investigation, diagnosis, and runbook-driven response. The product can also ingest runbooks, wikis, ADRs, READMEs, and on-call docs, but the source material does not define every output format in detail.
Quick Facts
- Category
- AI SRE Agent
- Primary use
- Incident investigation, root-cause analysis, and remediation support
- Deployment
- Connects to existing cloud, code, CI/CD, and observability tools; Enterprise supports self-hosting in a VPC
- Pricing
- Teams starts at $99/month with 99 investigation credits; Enterprise is custom
- Team model
- Unlimited users on both Teams and Enterprise
- Source domain
- drdroid.io
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