Doctor Droid
Self-learning AI SRE agent that maps your stack into a live knowledge graph for faster root-cause analysis and automated remediation.
Doctor Droid stands out for its knowledge-graph approach, connecting alerts to code, configs, and runbooks in one live view. It's a strong fit for mid-to-large SRE teams (5–200+ engineers) already using tools like Datadog, PagerDuty, and GitHub, where context is scattered. The credit-based pricing means you pay per investigation, not per seat—fair for teams with predictable volumes, but small teams with a handful of alerts weekly may find ROI thin. Compared to Datadog Bits AI or incident.io, Doctor Droid's pre-built agents and self-hosting options give it an edge for teams wanting autonomous investigation without vendor lock-in.
Verified 1d ago · liveness 75/100 · cite: rightaichoice.com/tools/doctor-droid
- SRE teams managing complex multi-tool infrastructure where context is scattered
- Platform engineering teams looking to automate incident response and remediation
- DevOps teams wanting to cut MTTR without manual log digging
- On-call engineers who need faster root-cause and context during alerts
- Teams without any existing observability or monitoring tools
- Solo developers working on small side projects
- Organizations not ready to grant read-only access to cloud and code repositories
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Skip Doctor Droid if you don't have existing observability and cloud tools to connect, or if you're a solo developer on a side project who can't justify a $99/month investment for a handful of alerts.
If you exceed 99 investigations per month on the Startup plan, each additional credit costs $1, adding up fast if you have frequent alerts.
Doctor Droid's credit-based pricing fits teams with predictable alert volumes. At $99/mo for 99 credits (about 300 investigations), it's cost-effective for 5-10 engineer teams compared to hiring a full-time SRE. However, Business at $999+/mo is significantly more than incident.io's $99/mo for smaller teams, so it's only worth it if you need self-hosting or advanced automations.
In short
Doctor Droid — Self-learning AI SRE agent that maps your stack into a live knowledge graph for faster root-cause analysis and automated remediation. Best for SRE teams managing complex multi-tool infrastructure where context is scattered, Platform engineering teams looking to automate incident response and remediation, DevOps teams wanting to cut MTTR without manual log digging. Free to start; paid plans from $99/mo.
What's new in Doctor Droid
Checked yesterdayAcross the latest 4 updates: 1 feature update and 3 news mentions.
Context Engine: How DrDroid's AI Agent leverages the Continuously Improving Knowledge Graph
Announced the continuously improving knowledge graph that powers DrDroid's AI agent for production context.
How DrDroid AI SRE Agent is specialised for Production Incidents & On-call Investigations
Highlights DrDroid's specialization in incident response and on-call workflows.
Backtesting AI Agents: How SRE Teams Prove Reliability Before Production
Introduces a methodology for backtesting AI agents to ensure reliability before production deployment.
AI in Engineering: 6 Trends That Will Define 2026
Doctor Droid identifies six AI engineering trends expected to shape 2026.
What people actually say about Doctor Droid — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
25 mentions across 3 sources (YouTube, Product Hunt, Lemmy) · researched Aug 17, 2026.
- +Live knowledge graph mapping entities across your stack is genuinely useful.
- +Cross-tool correlation saves time by linking repos to services to pods.
- +Blast radius tracing on alerts helps prioritize incidents effectively.
- +Automated runbook execution reduces manual toil for common issues.
- +80+ integrations mean it fits into most existing workflows easily.
- −Community feedback is thin—no long-term reliability reviews yet.
- −AI predictions may be untrustworthy for rare or complex outages.
- −Pricing for heavy usage could escalate due to credit-based models.
- −Setup may exceed 30 minutes for large, heterogeneous environments.
- −Documentation and support channels are not well-documented.
- • Credit-based usage might lead to unexpected overages on Pro plans.
- • Enterprise pricing is custom; small teams might find it pricey.
Viability Score
How well maintained and how widely used is Doctor Droid? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Live knowledge graph of stack entities
- Cross-tool correlation (repo-to-service-to-pod)
- Blast radius tracing on alert
- Continuous learning from alerts and deploys
- Root-cause analysis with suggestions
- Automated runbook execution
- AI Memory for patterns and context
- Pattern detection for recurring issues
- Smart model switching to optimize credits
- Ingests runbooks, wikis, ADRs, READMEs
- Slack automations and investigations from chat
- Self-hosted deployment via Helm or Docker Compose
- Mac app for on-call visibility
- Open Source PlayBooks and droidctx
- Integrations with 80+ tools (cloud, code, telemetry, response)
About Doctor Droid
Doctor Droid (DrDroid) is an AI SRE agent that connects to your cloud, code, CI/CD, and observability tools via read-only OAuth—no agents or code changes needed—and builds a live knowledge graph of your entire stack. It crawls metrics, logs, traces, alerts, deploy events, and documentation, mapping every repo to its service, dashboard, Kubernetes pods, and cloud resources. When an alert fires, the agent traces the blast radius across connected entities in seconds, provides root-cause analysis with suggestions, and can execute automated runbooks. The agent continuously learns from every alert and deploy, building an AI Memory that replays successful investigation paths and skips dead ends, so MTTR drops on its own. It also ingests runbooks, wikis, ADRs, READMEs, and other institutional knowledge to ground its responses. Doctor Droid includes Slack automations, team collaboration with shared investigations, and a Mac app for on-call visibility. It's designed for SRE and platform engineering teams who want to cut MTTR without manual log digging. Pricing is credit-based (not per seat): one credit covers roughly three investigations, and the Startup plan includes 99 credits per month with top-ups at $1 each. Business and Enterprise tiers add self-hosting, advanced automations, SSO/SCIM, and custom outcome SLAs.
Behind the Verdict
Doctor Droid is a purpose-built AI SRE agent that addresses a real pain point: scattered context during incident response. Its core differentiator is the live knowledge graph that automatically maps repos, services, dashboards, Kubernetes pods, and cloud resources, so when an alert fires, the agent can trace the blast radius across connected entities in seconds. This is not just a chat interface over your logs; it's a persistent, learning system that gets faster with every incident. The pre-built agents—Investigation, Auto-remediation, Observability, Reporting, Infrastructure Migration, Retrospective—cover the full incident lifecycle, from detection to post-incident learning. You can trigger them from Slack, PagerDuty, webhooks, or an API, and they return results to the platform you're already working in. The credit-based pricing is a double-edged sword: it's fair for teams with predictable investigation volumes, but unpredictable spikes could lead to surprise top-up costs. The Startup plan at $99/mo for 99 credits (about 300 investigations) is reasonable for a 5-10 engineer team, but if you need self-hosting or advanced automations, you're looking at Business ($999+/mo) or Enterprise. Doctor Droid is not a fit for teams without existing observability tooling, as it depends on crawling your stack. For teams with complex, multi-tool infrastructure, it's a compelling alternative to manual log digging or building your own automation. The recent blog posts on backtesting AI agents and the continuously improving knowledge graph suggest a company focused on reliability and trust, which is crucial for an agent that takes autonomous actions in production.
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Real-world workflow fit
Concrete scenarios for the personas Doctor Droid actually fits — and what changes day-one when you adopt it.
You receive a PagerDuty alert for high latency in payments-api. You open Slack and ask DrDroid to investigate. The agent traces the blast radius, identifies a recent deploy as the cause, and provides a suggested runbook to roll back.
Outcome: You approve the rollback via Slack, and the incident is resolved in under 10 minutes, reducing MTTR significantly.
You want to automate alert triage for your 50-engineer team. You set up DrDroid with Datadog, Kubernetes, and GitHub. The agent learns recurring patterns and automatically files tech-debt tickets for noisy alerts, freeing your team to focus on high-severity issues.
Outcome: Your team sees a 30% reduction in alert noise within the first month, and the agent's AI Memory improves response times on repeat incidents.
You're managing a canary deployment. You use DrDroid's Deployment Tracking Agent to monitor post-deployment metrics and flag regressions. The agent detects an anomaly in error rates and automatically triggers a rollback, preventing customer impact.
Outcome: You catch a regression before it affects users, and the agent learns from the incident to improve future canary monitoring.
Use Cases
- Investigate a PagerDuty alert by running automated root-cause analysis across logs, metrics, and recent deploys.
- Auto-create and execute runbooks when a Kubernetes pod enters CrashLoopBackOff.
- Proactively suggest tightening retry budgets after detecting repeated timeouts in a microservice.
- Trace the blast radius from a single alert to impacted services, dashboards, and AWS resources.
- Ingest runbooks and wikis to ground AI responses in institutional knowledge.
- Automate alert triage and route to the right team using learned patterns.
- Backtest AI agent performance on historical incidents before production deployment.
- Monitor post-deployment workflows for regression detection.
Models Under the Hood
as of 2026-09-02
Limitations
- Pricing is credit-based: each investigation consumes roughly one-third credit on average, with the Startup plan including 99 credits per month and top-ups at $1 per credit.
- Smart model switching routes simple investigations to cheaper models, which may affect answer quality on complex cases.
- Self-hosting and bring-your-own-LLM options are available only on the Business plan and above, requiring a sales conversation.
- The Startup plan includes a 14-day trial and is designed for teams of 5-10 engineers.
- Advanced automations for Teams, Google Chat, PagerDuty, and other tools are only available on Business and above.
as of 2026-09-01
Verification history
We have re-verified Doctor Droid 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Doctor Droid tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Startup
$99/mo
Ideal for
5-10 engineer teams with moderate alert volumes who want a cloud-only, one-click setup with 99 investigation credits per month.
What this tier adds
Starting tier with 99 credits, Slack automations, and team collaboration; ideal for getting started with AI SRE.
Business
from $999/mo
Ideal for
20-200 engineer teams needing self-hosting, advanced automations, SSO/SCIM, and bring-your-own-LLM options.
What this tier adds
Adds self-hosting, volume credits, advanced automations for Teams, Google Chat, PagerDuty, Opsgenie, Zenduty, and SSO/SCIM with audit log.
Enterprise
Custom
Ideal for
200+ engineer organizations requiring custom outcome SLAs, ServiceNow/JSM automations, IP allowlist, and compliance certifications.
What this tier adds
Adds custom volume pricing, custom outcome SLA, ServiceNow and JSM automations, IP allowlist, data residency, SOC 2 Type II and ISO 27001, and 24/7 priority support with 15-min response.
Where the pricing makes sense
The company stage and team size where Doctor Droid's pricing actually pencils out — and where peers do it cheaper.
Doctor Droid's credit-based pricing fits teams with predictable alert volumes. At $99/mo for 99 credits (about 300 investigations), it's cost-effective for 5-10 engineer teams compared to hiring a full-time SRE. However, Business at $999+/mo is significantly more than incident.io's $99/mo for smaller teams, so it's only worth it if you need self-hosting or advanced automations.
Setup time & first value
How long it actually takes to get something useful out of Doctor Droid — broken out by persona, not the marketing-page minute.
For a 5-10 engineer team: OAuth connect to your cloud and observability tools via one-click setup, expect to be live in 30 minutes. For a larger team (20-200 engineers) on Business: allow a few hours to configure self-hosting and advanced automations, with DrDroid's support team assisting. For Enterprise: onboarding is dedicated and can take a week depending on data residency and custom
Switching to or from Doctor Droid
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual incident response (e.g., spreadsheets, wiki): Connect DrDroid via OAuth, and it will ingest your runbooks and wikis to build context automatically.
- →From Datadog Bits AI: Doctor Droid offers deeper integrations with your stack and a knowledge graph, so you can import existing dashboards and alert history.
- →From incident.io: You can migrate your incident data and runbooks, and Doctor Droid will learn from past incidents to improve future investigations.
- ↗To a traditional monitoring tool (e.g., Datadog): Export your investigation history and runbooks from Doctor Droid, though you'll lose the knowledge graph context.
- ↗To a custom in-house tool: Doctor Droid's API and webhook allow you to export findings and integrate them into your own workflows.
Integrations
Resources & Guides
- Resourcedrdroid.io
Blog · Doctor Droid
Helpful link from drdroid.io
- Resourcedrdroid.io
Mac App · Doctor Droid
Helpful link from drdroid.io
- Resourcedrdroid.io
Status Page Aggregator · Doctor Droid
Helpful link from drdroid.io
- Resourcedrdroid.io
Kenobi · Doctor Droid
Helpful link from drdroid.io
- Resourcedrdroid.io
Blog · Doctor Droid
Helpful link from drdroid.io
Tutorials & Learning
Official links
Tools that pair well with Doctor Droid
Common stack mates teams adopt alongside Doctor Droid, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Doctor Droid vs Spider Cloud
If you run a multi‑tool infrastructure and need to slash MTTR, Doctor Droid’s self‑learning knowledge graph and automated runbooks are purpose‑built for SRE teams. If you need real‑time web data to power AI agents or RAG pipelines, Spider Cloud’s high‑throughput, low‑cost scraping with Browser AI commands is the better fit. They tackle entirely different problems — choose based on your pain point.
Doctor Droid vs Temporal Ai
Choose Doctor Droid if your priority is slashing MTTR and automating incident response using a knowledge graph that connects your tools. Choose Temporal AI if you need a durable execution engine to build reliable multi-step workflows, especially AI agents, that survive failures. They solve different problems: incident analysis vs. workflow orchestration.
Doctor Droid vs Presto Voice
These tools serve completely different domains. Doctor Droid is an AI SRE agent for engineering teams needing faster incident response and root-cause analysis via a knowledge graph. Presto Voice is a drive-thru voice AI platform for QSR chains aiming to boost revenue and efficiency. Choose based on your industry: infrastructure ops vs. restaurant automation.
Alternatives to Doctor Droid
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