Deeptrace
AI SRE agent that investigates production alerts and posts evidence-backed root causes in Slack within minutes.
Deeptrace is worth a serious look if your on-call engineers are drowning in alerts and you already have telemetry flowing into Datadog, Grafana, or Sentry. The 2–3 minute evidence-backed root causes plus 2026 additions — Escalations to OpsGenie and PagerDuty, signed webhooks, natural-language GitHub Actions triggering, and Slack Channel Auto-Join — go further toward closing the loop than most AI incident tools. The catch is the ~70% accuracy Deeptrace itself publishes: budget verification time into the workflow. If you want a fully autonomous fixer, look at human-in-the-loop options instead; if you want fast first-responder triage on top of your existing stack, this fits.
Verified 5d ago · liveness 78/100 · cite: rightaichoice.com/tools/deeptrace
- On-call engineering teams drowning in alert volume who need triage before opening Slack
- Fast-growing startups scaling reliability from Series A to C without adding SRE headcount
- Platform and SRE teams trying to cut MTTR on microservices architectures
- Teams with scripted remediation runbooks who want GitHub Actions fixes triggered from investigation findings
- Teams with no observability tooling — Deeptrace reasons over existing logs, traces, and metrics
- Anyone expecting zero-config accuracy — published root-cause accuracy runs around 70% and needs verification
- Small teams whose only monitoring need is basic uptime pings
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Deeptrace if your team runs no observability stack — it needs logs, traces, metrics, and code to reason over — or if you want a tool whose ~70% root-cause findings don't need a human to verify.
Startup investigation allowance caps at 1,000 alerts and chats per month — a noisy production environment can burn through that well before the month ends.
Deeptrace's published entry tier is a 2-week Startup trial covering up to 1,000 alerts and chats per month with unlimited users, and the Enterprise tier is a 4-week trial with investigation capacity tailored to alert volume, flexible deployment (SaaS, hybrid, self-hosted), dedicated support and SLA, and custom integrations. That positions it alongside commercial AI-SRE and incident-response platforms rather than free open-source on-call tooling. Fits Series A–C and F1000 platform teams that
In short
Deeptrace — AI SRE agent that investigates production alerts and posts evidence-backed root causes in Slack within minutes. Best for On-call engineering teams drowning in alert volume who need triage before opening Slack, Fast-growing startups scaling reliability from Series A to C without adding SRE headcount, Platform and SRE teams trying to cut MTTR on microservices architectures. Free to start; it also has paid plans, priced in a currency we have not confirmed — see the pricing table for the vendor’s own figures.
What's new in Deeptrace
Checked 5 days agoAcross the latest 4 updates: 4 feature updates.
Webhooks feature
Investigation results now stream to external services as signed JSON payloads in real time, so downstream systems can react the moment a root cause is posted.
Escalations feature
Investigation findings are automatically routed to OpsGenie, PagerDuty, and Slack with retry logic and delivery tracking, removing manual copy-paste from the escalation path.
Actions feature
GitHub Actions workflows and custom scripts can be triggered by natural language from a finding to run deployments, revert PRs, or take remediation steps.
Channel Auto-Join feature
Deeptrace auto-joins Slack channels matching configured name patterns and immediately starts investigating incidents declared in those channels.
Viability Score
How well maintained and how widely used is Deeptrace? 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: October 2026
How we score →Key Features
- Automatic investigation triggered by every alert in your Slack channel
- Root cause analysis across logs, traces, metrics, and code with citations
- ~2–3 minute average time to root cause
- Reported ~70% investigation accuracy; ~50% MTTR reduction across teams
- Alert intelligence: automatic priority ranking by business impact
- Related alerts grouped into single issues to cut noise
- Natural-language chat in Slack and web app for follow-up questions
- Auto-generated pull requests for fixes, with runbook and documentation updates
- Linear ticket creation with full investigation context attached
- Living knowledge graph mapping infrastructure, services, and dependencies
- Compounds knowledge over time from feedback and past investigations
- Signed JSON webhooks streaming investigation results to external services in real time
- Escalations routing findings to OpsGenie, PagerDuty, and Slack with retry logic and delivery tracking
- GitHub Actions workflows and custom scripts triggered by natural language for deployments, revert PRs, or remediation
- Slack Channel Auto-Join on incident channels matching name patterns
About Deeptrace
Deeptrace is an AI SRE agent for production incident investigation. Instead of an engineer opening Slack at 3am to triage a wall of alerts, Deeptrace picks up the alert, reasons across your logs, traces, metrics, and code, and posts a root cause with citations — landing in 2–3 minutes on average. It's aimed at engineering and platform teams running on-call rotations on microservices, where alert volume has outgrown headcount. The product spans three layers. Alert intelligence groups related alerts into single issues and ranks them by business impact. Root cause analysis cross-references observability data and code and attaches evidence to every conclusion rather than a confident guess. Then Actions goes from diagnosis to repair: auto-generated pull requests, runbook and documentation updates, and Linear tickets created with full context. Recent 2026 releases push Deeptrace deeper into the incident workflow. Signed webhooks stream investigation results to external services in real time. A new Escalations feature routes findings to OpsGenie, PagerDuty, and Slack with retry logic and delivery tracking. GitHub Actions workflows and custom scripts can now be triggered by natural language for deployments, revert PRs, or remediation. Channel Auto-Join means Deeptrace drops into Slack incident channels matching name patterns and starts investigating immediately. Underneath sits a living knowledge graph of your infrastructure, services, and dependencies that updates as your architecture changes and compounds with each investigation. Deeptrace connects to 20+ tools including Datadog, Grafana, Sentry, New Relic, AWS CloudWatch, GitHub, PagerDuty, Notion, Snowflake, PostHog, Groundcover, Clickhouse, BigQuery, and Linear, with no code changes required. It sits on top of your existing observability stack rather than replacing it. Deeptrace reports ~70% investigation accuracy and cuts MTTR by roughly 50%, so treat it as a very fast first responder whose conclusions you verify rather than an autonomous SRE.
Behind the Verdict
Deeptrace's core bet is that the painful part of on-call isn't detection — Datadog and PagerDuty already do that — it's context gathering. An alert fires, then a human spends 20 minutes clicking between dashboards, log search, and git blame before they even know what broke. Deeptrace attacks that gap: it ingests your observability data and code, builds a living knowledge graph of services and dependencies, and when an alert lands in Slack it runs an investigation and posts a root cause with citations in 2–3 minutes. The published accuracy is ~70%, and Deeptrace's own docs frame findings as needing verification — that's honest framing and worth internalizing before you buy. Strengths: the investigation flow is wired into the tools engineers already live in (Slack, PagerDuty, OpsGenie, Linear, GitHub), and the 2026 releases meaningfully extend reach. Escalations with retry logic and delivery tracking means an investigation can fan out to a second system without a human copy-pasting. Signed webhooks let your own services react to findings in real time. Channel Auto-Join removes the “invite the bot” step. Actions can trigger GitHub Actions workflows and custom scripts by natural language to run deployments, open revert PRs, or take remediation steps — that's the difference between a diagnostic tool and part of the response plane. The living knowledge graph is the durable asset: it gets more useful the longer it runs, which raises switching cost. Weaknesses: Deeptrace is a layer, not a replacement. It needs logs, traces, metrics, and code to reason over — a team with thin observability will get thin results. The ~70% accuracy figure is public and means engineers still validate before acting, so the time savings are real but partial. And it asks for a real feedback loop (dedicated Slack channel, correction of bad investigations) to compound — teams that install it and ignore it won't see the curve. Where it fits: Series A–C platform teams scaling microservices without adding SRE headcount, and F1000 engineering orgs trying to trim MTTR on high-alert-volume services. Where it doesn't: shops without an observability stack, or teams looking for a fully autonomous system that fixes prod without a human in the loop.
Researching Deeptrace? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Deeptrace actually fits — and what changes day-one when you adopt it.
A PagerDuty alert fires at 3am; Deeptrace picks it up in Slack, correlates logs, traces, metrics, and code, and posts a root-cause summary with citations in ~2–3 minutes; the engineer verifies and ships the suggested fix.
Outcome: Triage time drops from ~20 minutes of manual context gathering to a few minutes of verification, and MTTR falls roughly 50% across the team.
Deeptrace is connected to Datadog, Grafana, Sentry, AWS CloudWatch, and GitHub; Channel Auto-Join drops it into incident channels by name pattern; Escalations route findings to OpsGenie and PagerDuty with retry logic, and signed webhooks push results to an internal service.
Outcome: Investigations fan out across the incident toolchain without an engineer copy-pasting findings, and the living knowledge graph gets richer with each incident.
An alert lands; Deeptrace's finding points to a known failure mode, so the engineer chats a follow-up question in Slack, then triggers the documented GitHub Actions workflow by natural language to run remediation or open a revert PR, and files a Linear ticket with the investigation context attached.
Outcome: The junior engineer handles the incident with AI-guided analysis and citations rather than escalating immediately, easing on-call load on seniors.
Use Cases
- Automatically investigate every PagerDuty or Datadog alert and get a cited root cause in Slack within minutes.
- Cut on-call context-switching between log search, dashboards, traces, and git blame.
- Trigger GitHub Actions workflows from a natural-language finding to run remediation or open a revert PR.
- Let junior engineers handle production incidents with AI-guided root cause analysis and citations.
- Group related alerts into single issues and rank by business impact so the first thing you see is the thing that matters.
- Chat with Deeptrace mid-incident to ask follow-up questions grounded in your actual logs, metrics, and code.
- Route investigation findings to OpsGenie, PagerDuty, or a downstream service via escalations and signed webhooks.
- Auto-join incident Slack channels by name pattern so investigation starts the moment an incident is declared.
Limitations
- Deeptrace requires existing observability data (logs, traces, metrics, code) to function — it integrates with tools like Datadog, Grafana, and GitHub to map your system, so thin telemetry produces thin results.
- It posts evidence-backed conclusions with citations, but the ~70% investigation accuracy it publishes means engineers should validate findings before acting.
- Deeptrace augments, rather than replaces, your existing observability stack.
- A dedicated Slack channel and feedback loop are needed for the knowledge graph to compound — teams that don't correct bad investigations won't see the accuracy curve improve.
- Enterprise pricing details are not public.
as of 2026-10-03
Verification history
We have re-verified Deeptrace 8 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-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-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
Showing the 6 most recent of 8 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 Deeptrace tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Startup
2-Week Trial
Ideal for
Small to mid-size engineering teams that want to trial Deeptrace on real production alerts before committing — covers up to 1,000 alerts and chats per month with unlimited users.
What this tier adds
Starting entry point: 2-week trial, single workspace, up to 1,000 alerts and chats per month, unlimited users, dedicated Slack support channel.
Enterprise
4-Week Trial
Ideal for
F1000 or high-volume platform teams that need investigation capacity sized to their alert volume, hybrid or self-hosted deployment, and a committed SLA.
What this tier adds
Adds investigation capacity tailored to your alert volume, flexible deployment (SaaS, hybrid, self-hosted), dedicated support and SLA, and custom integrations — entered via a 4-week trial.
Where the pricing makes sense
The company stage and team size where Deeptrace's pricing actually pencils out — and where peers do it cheaper.
Deeptrace's published entry tier is a 2-week Startup trial covering up to 1,000 alerts and chats per month with unlimited users, and the Enterprise tier is a 4-week trial with investigation capacity tailored to alert volume, flexible deployment (SaaS, hybrid, self-hosted), dedicated support and SLA, and custom integrations. That positions it alongside commercial AI-SRE and incident-response platforms rather than free open-source on-call tooling. Fits Series A–C and F1000 platform teams that
Setup time & first value
How long it actually takes to get something useful out of Deeptrace — broken out by persona, not the marketing-page minute.
For teams with Datadog, Grafana, Sentry, or CloudWatch already feeding telemetry: first value in minutes per the vendor's docs — create an account, connect your tools, and Deeptrace begins mapping infra, services, and dependencies before it investigates its first alert. Teams layering on GitHub, Linear, PagerDuty, or OpsGenie should add a short configuration pass for those connections and Slack
Switching to or from Deeptrace
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual Slack triage: point Deeptrace at your existing Slack alert channels and it starts investigating incoming alerts without changing your alerting rules.
- →From Datadog or Grafana native alerts: connect Deeptrace alongside your current dashboards — it reads the same telemetry and posts root causes rather than replacing the source of truth.
- →From PagerDuty/OpsGenie manual on-call: add Deeptrace as an investigation layer that escalates findings to the same tools, keeping your existing escalation policies.
- ↗To a manual Slack + dashboards workflow: disconnect Deeptrace from Slack, Datadog, Grafana, and GitHub; your existing observability and alerting stack continues on its own.
- ↗To a different AI incident tool: export the investigations and runbook notes Deeptrace generated in Linear and GitHub PRs so your knowledge graph equivalent starts with history.
Integrations
Resources & Guides
- Documentationdeeptrace.com
Docs · Deeptrace
Full product docs from deeptrace.com
- Documentationdeeptrace.com
Integrations · Deeptrace
Full product docs from deeptrace.com
- Documentationdeeptrace.com
Features · Deeptrace
Full product docs from deeptrace.com
- Documentationdeeptrace.com
Changelog · Deeptrace
Full product docs from deeptrace.com
- API Referencedeeptrace.com
Api · Deeptrace
Methods, params, types from deeptrace.com
Tutorials & Learning
YouTube returned 6 videos for “Deeptrace”, and we withheld 6: 6 could not be judged, because “Deeptrace” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Deeptrace.
Official links
Tools that pair well with Deeptrace
Common stack mates teams adopt alongside Deeptrace, with the specific reason each pairing earns its keep.
Corelayer
AI-native incident response that finds production root causes, cuts alert noise, and opens fix PRs — deployable on-prem or in your cloud.
Sazabi
Sazabi is AI-native observability: it replaces dashboards with chat debugging, autonomous alerts, and coding agents that open fix PRs.
Relvy AI
Autonomous AI on-call engineer that investigates alerts and produces auditable investigation notebooks.
Featured Head-to-Head Comparisons
Deeptrace vs Spider Cloud
For teams drowning in production alerts, Deeptrace is a game-changer—it automates investigation and root cause analysis across your stack. For AI pipelines that live on fresh web data, Spider Cloud delivers absurdly cheap, reliable scraping at scale. Pick the tool that matches your pain point: on-call burnout or data hunger.
Deeptrace vs Presto Voice
If you're an engineering team drowning in alerts, Deeptrace is your AI SRE — it automates root cause analysis and remediation across logs, traces, and code. If you run a QSR chain, Presto Voice delivers drive-thru voice AI with proven upselling and 95% automation. They solve totally different problems, so choose based on your industry.
Deeptrace vs Temporal Ai
Choose Deeptrace if your primary pain is alert fatigue and you want an AI agent that automatically investigates and even fixes production issues. Choose Temporal if you need a robust platform to build crash-proof AI agents and long-running workflows. They are complementary: Deeptrace for incident response, Temporal for workflow reliability.
Alternatives to Deeptrace
View all
Corelayer
AI-native incident response that finds production root causes, cuts alert noise, and opens fix PRs — deployable on-prem or in your cloud.
Frequently Asked Questions
Used Deeptrace? Help shape our editorial sentiment research.