superlog is live · free · last verified 8 Nov 2026 · viability healthy
superlog
Superlog automates on-call alert triage, investigating production errors and opening fix pull requests in your repo.
Most AI SRE tools stop at a summary in chat. Superlog pushes through to a pull request and then keeps working it — rebasing, chasing flaky tests, watching the rollout. That's the real differentiator for teams whose on-call rotation already lives in Slack and Sentry. Skip it if nobody on your team will review agent-authored PRs, because that review step is the whole control surface.
Verdict generatedVerified 5d ago · liveness 81/100 · cite: rightaichoice.com/tools/superlog
- SRE and on-call teams whose incidents already land in Sentry and Slack and want the diagnosis-and-fix loop automated
- Platform teams with instrumentation gaps — missing logs, traces or non-standard OTel tags — who never get to the cleanup
- Mixed-stack shops running Sentry plus Datadog plus Grafana that want one agent reading all of it
- Engineering orgs that want to pick their own model provider rather than be locked to one vendor's
- Teams that won't let an agent open pull requests against production repositories
- Shops with no branch protection, CI gates or runbooks to judge agent output against
- Solo developers with only a handful of alerts per month
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Skip Superlog if nobody on your team will review an agent-authored pull request, or if you lack branch protection and CI checks to gate what an agent commits.
superlog's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
In short
superlog — Superlog automates on-call alert triage, investigating production errors and opening fix pull requests in your repo. Best for SRE and on-call teams whose incidents already land in Sentry and Slack and want the diagnosis-and-fix loop automated, Platform teams with instrumentation gaps — missing logs, traces or non-standard OTel tags — who never get to the cleanup, Mixed-stack shops running Sentry plus Datadog plus Grafana that want one agent reading all of it. Free to use.
What is superlog?
superlog — Superlog automates on-call alert triage, investigating production errors and opening fix pull requests in your repo. Best for SRE and on-call teams whose incidents already land in Sentry and Slack and want the diagnosis-and-fix loop automated, Platform teams with instrumentation gaps — missing logs, traces or non-standard OTel tags — who never get to the cleanup, Mixed-stack shops running Sentry plus Datadog plus Grafana that want one agent reading all of it. Free to use.
Superlog is an automation layer for SRE, DevOps and platform teams that want production incidents investigated without a human driving every step. You connect the repos and telemetry an agent can read, hand it a runbook, and pick a trigger — a new Sentry error, a Slack message, a webhook, or a schedule. The agent traces the failure, checks recent deploys, identifies affected customers, then replies in the alert thread with the failing trace and a pull request for review. The product also works proactively. Overview checks flag missing logs, missing traces, and tags that skip OpenTelemetry conventions, and the MCP gives your agent a suggested prompt to fix each gap. When an agent finds a new failure mode, Superlog prompts it to add an alert or synthetic test so the next occurrence gets caught. A browser SDK captures frontend exceptions with source maps pointing at the line that failed. Context comes from the tools you already run: Sentry, Datadog, GitHub, Slack, AWS, OpenTelemetry, plus Grafana, Google Cloud, PostHog, Dash0 and ClickStack. You can also store telemetry directly, with 5M logs included monthly and 30 days of retention. Model choice stays yours — bring your own model and harness, or your own keys and subscriptions, and switch providers without rebuilding automations. Enterprise adds SSO, SAML, audit logs, scoped repo access and isolated investigation sandboxes. It's a layer on top of your observability stack, not a replacement for Datadog, Sentry or Grafana.
Key features
- Trigger automations on new Sentry issues, Slack messages, webhooks or schedules
- Agent replies in the alert thread with the failing trace and an open pull request
- Checks past reports so repeat alerts generate less noise
- Overview flags missing logs, missing traces and non-standard OpenTelemetry tags
- MCP gives your agent a suggested prompt to fix each instrumentation gap
- Prompts agent to add an alert or synthetic test when a new failure mode appears
- Browser SDK captures frontend exceptions with source maps to the failing line
- Store logs, metrics and traces via OpenTelemetry with 30-day retention
- Bring your own model and harness, or your own keys and subscriptions
- Scope which repositories and connected accounts each agent can read
- Run investigations in isolated sandboxes with supplied context and instructions
- Enterprise SSO, SAML and audit logs
Behind the verdict
We'd reach for Superlog when the problem isn't detection but response — you already have Datadog or Sentry firing alerts, and a human is the slow part between the page and a diagnosis. The runbook-plus-trigger model is the right shape for that: pick repos and telemetry an agent can read, set the trigger (Sentry error, Slack, webhook, schedule), and let it reply in the alert thread with a trace and a PR. The proactive side is where the economics get interesting. Most SRE teams know their instrumentation has holes; nobody has time to close them. Overview flagging missing logs, missing traces and non-conventional tags, then handing the agent a fix prompt, turns that backlog into something that actually ships. Where it bites: you need CI gates, branch protection and a review culture. If your team rubber-stamps PRs, an agent opening them just adds volume. The agent also needs scoped access to be useful — Enterprise covers SSO, SAML, audit logs and isolated investigation sandboxes, which matters if security reviews your tooling. Closest alternatives are Sentry Seer and Datadog Bits AI, both of which investigate inside their own platform. Superlog's pitch is the opposite: it connects your existing tools together with your codebase and doesn't ask you to migrate. That's a better fit for mixed stacks — Sentry for errors, Datadog for latency, Grafana for dashboards — where a single-vendor agent only sees one slice. Pricing is free to start with a pay-as-you-go usage tier. Telemetry storage runs 5M logs included monthly with 30 days retention, then $0.50 per additional million on Pro and Team. Bring-your-own-model means your inference bill is whatever your provider charges, which cuts both ways: predictable if you already have keys, an extra line item if you don't. Pass if
Models under the hood
as of 2026-11-08
Limitations
- Setup depends on connecting your own stack — Sentry, Datadog, GitHub, Slack and AWS connectors, plus OpenTelemetry log/trace data and the browser SDK, must be wired up before triage and fix PRs are useful.
- Superlog does not appear to supply inference itself: the vendor materials describe bringing your own model, harness, keys and subscriptions.
- Included storage is 5M logs per month with 30 days of retention, and on Pro and Team plans each additional million logs costs $0.50.
- Capability claims come from the vendor's own homepage and product-update posts; independent verification of fix quality and root-cause accuracy was not available in this research pass.
as of 2026-10-20
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Is superlog still active?
superlog is live · free · last verified 8 Nov 2026 · viability healthy.
Viability score
How well maintained and how widely used is superlog? 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: November 2026
How we score →What's new in superlog
Checked todayAcross the latest 1 update: 1 feature update.
What do users say about superlog?
What people actually say about superlog — 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.
Researched46 mentions across 5 sources (Hacker News, YouTube, Product Hunt, GitHub, Lemmy) · researched Aug 29, 2026.
Average across the 5 sources that answered — each source counts once, not each post.
- +Auto-generates fix PRs with regression tests, dramatically reducing MTTR.
- +Incident grouping with fingerprinting cuts alert noise from dozens to one.
- +Confidence Gate pulls engineers only when the AI is unsure, avoiding blind fixes.
- +OpenTelemetry-native ingestion (logs, traces, metrics) makes integration straightforward.
- +MCP server and Slack @-mentions let you query telemetry without switching tools.
- −Mandatory Slack onboarding blocks non-Slack teams from trying the tool.
- −AI-generated PRs raise trust issues; 'read causes change' side-effect concerns persist.
- −Auto-instrumentation can cause telemetry cost spikes due to high cardinality.
- −Self-hosting option is not yet available, limiting data control.
- −Single US-West data center raises latency and compliance concerns for EU users.
- • Telemetry storage costs can spike with high-cardinality attributes; sampling is needed to control costs.
- • Investigations may be priced per incident; high volumes could add up.
- • Enterprise tier likely requires annual commitment; no public pricing.
How we verified this
Every listed fact about superlog — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 8 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified superlog 17 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.
- — changed pricing page, limitations, summary, who should skip it, integrations, features, description, who it suits, pricing tiers, our verdict
- — changed our verdict, who should skip it, pricing tiers, summary, features, who it suits, description
- — changed limitations, pricing page
- — changed switching out, who it suits, features, who should skip it, setup time, hidden costs, description, use cases, limitations, summary, our verdict, switching in
- — changed SSL certificate
- — changed who should skip it, integrations, features, who it suits, summary, pricing tiers, our verdict, description
Showing the 6 most recent of 17 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does superlog cost in 2026?
superlog: Free to use. Pricing last verified 8 Nov 2026.
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.
Where the pricing makes sense
The company stage and team size where superlog's pricing actually pencils out — and where peers do it cheaper.
superlog's pricing fits teams whose volume aligns with the published tiers. Compare against the alternatives listed below for stage-specific value.
Has superlog's pricing changed?
No plan price for superlog has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick superlog?
Pick superlog if you are SRE and on-call teams whose incidents already land in Sentry and Slack and want the diagnosis-and-fix loop automated, Platform teams with instrumentation gaps — missing logs, traces or non-standard OTel tags — who never get to the cleanup, Mixed-stack shops running Sentry plus Datadog plus Grafana that want one agent reading all of it, Engineering orgs that want to pick their own model provider rather than be locked to one vendor's.
Look elsewhere if you are Teams that won't let an agent open pull requests against production repositories, Shops with no branch protection, CI gates or runbooks to judge agent output against, Solo developers with only a handful of alerts per month, Buyers looking to replace Datadog or Sentry — Superlog is a layer on top, not a swap.
Skip it if Skip Superlog if nobody on your team will review an agent-authored pull request, or if you lack branch protection and CI checks to gate what an agent commits.
GeneratedReal-world workflow fit
Concrete scenarios for the personas superlog actually fits — and what changes day-one when you adopt it.
You wire Sentry, Datadog, GitHub and Slack via one-click connectors, then turn on the Sentry new-issue triage template and the hourly reliability check. New critical issues get rated, written up in plain words in Slack, and turned into a fix PR while false alarms stay quiet; the hourly check compares error rates and latency to their usual range.
Outcome: Your on-call rotation stops hand-triaging every new issue and only sees the ones that actually matter, with a PR already attached to the critical ones.
You schedule the weekly performance review and the weekly observability review. Each Monday at 09:00 Superlog traces the slowest Datadog endpoints and queries back to code, opens PRs for safe fixes, and opens a PR adding the missing logs, tags and spans behind recent errors.
Outcome: Performance and debuggability work that never got prioritized turns into a steady stream of reviewable PRs instead of a stale backlog.
You point the Answer support questions template at your Slack support channel and the Answer community questions template at a Discord /automate command. Superlog reads your code and docs and replies in-thread with an answer and links.
Outcome: Routine "is this a bug?" and "can we do this?" questions get answered without pulling an engineer off their work.
Use cases
- Auto-triage new Sentry issues with severity, suspected commit and owner posted to Slack
- Run an hourly reliability check comparing Sentry error rates and Datadog latency against normal range
- Open a weekly PR adding missing logs, tags and spans to hard-to-debug code paths
- Find the slowest endpoints and queries in Datadog and open PRs for safe fixes
- Answer internal support questions by reading your code and docs in Slack threads
- Answer community questions via a /automate slash command in Discord
- Proactively scan AWS CloudWatch, Datadog and Sentry for issues you have no alerts for
- Keep agent PRs moving through rebasing, flaky tests and merge queues
Setup time & first value
How long it actually takes to get something useful out of superlog — broken out by persona, not the marketing-page minute.
Expect a session, not a signup: connect Sentry, Datadog, GitHub and Slack via the one-click connectors, pick the templates you want, and set your model or tokens. Teams with branch protection and CI already in place get to a useful first run fastest; teams without them need to fix that first.
Switching to or from superlog
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual on-call triage: replace the hand-written Sentry issue write-ups with the Sentry new-issue triage template so each issue gets rated, traced and turned into a fix PR
- →From a scripted alerting bot: move the hourly Sentry/Datadog comparison into the Reliability check template and keep the Slack alert channel
- ↗To manual on-call triage: disable the scheduled automations and keep using Sentry and Datadog directly
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “superlog”, and we withheld 6: 6 could not be judged, because “superlog” 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 superlog.
Official links
What are the best superlog alternatives?
The closest alternatives to superlog we list are Resolve AI, Relvy AI and Dash0. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Resolve AI | Contact Sales | Custom pricing | — |
| Relvy AI | Contact Sales | Custom pricing | — |
| Dash0 | Paid | Pricing not published | — |
Alternatives to superlog
View all
Resolve AI
Resolve AI runs autonomous AI SRE agents on-call to triage alerts, investigate incidents and cut MTTR.
Tools that pair well with superlog
Common stack mates teams adopt alongside superlog, with the specific reason each pairing earns its keep.
Resolve AI
Resolve AI runs autonomous AI SRE agents on-call to triage alerts, investigate incidents and cut MTTR.
Relvy AI
Relvy AI automates on-call alert investigation with auditable notebooks your team can review
Dash0
Dash0: OpenTelemetry-native observability with Agent0 AI root cause analysis and fix pull requests.
superlog vs its closest rival
superlog's closest head-to-head is with Chili Piper — These aren't competitors — don't run a bake-off. If you sell B2B software and inbound demo requests are sitting unanswered, Chili Piper is the buy: it turns form fills and chat into booked meetings and pushes routing through Salesforce, and the new $12/user/mo standalone ChiliCal tier (200-seat minimum) is the cheap way in if scheduling links are all you need. If your problem is 3am PagerDuty noise and errors nobody has time to trace, Superlog is the buy: it watches Sentry and Datadog, posts severity plus suspected commit and owner into Slack, and opens PRs for missing logs or slow queries, with you supplying the model tokens. Buying either to solve the other's problem is a wasted budget. Verdict generated
Head-to-head comparisons
Superlog vs Chili Piper
These aren't competitors — don't run a bake-off. If you sell B2B software and inbound demo requests are sitting unanswered, Chili Piper is the buy: it turns form fills and chat into booked meetings and pushes routing through Salesforce, and the new $12/user/mo standalone ChiliCal tier (200-seat minimum) is the cheap way in if scheduling links are all you need. If your problem is 3am PagerDuty noise and errors nobody has time to trace, Superlog is the buy: it watches Sentry and Datadog, posts severity plus suspected commit and owner into Slack, and opens PRs for missing logs or slow queries, with you supplying the model tokens. Buying either to solve the other's problem is a wasted budget.
GeneratedSuperlog vs Temporal Ai
These are not substitutes. Temporal AI is infrastructure you build on when the problem is losing the running state of a multi-step process — a crashed worker, an abandoned session, a workflow that spans days. Superlog is a layer you plug in when the problem is people: alert fatigue for on-call, repeated support questions that already have answers hiding in your code and docs. If your team owns both problems, you could genuinely run Superlog on top of work Temporal already orchestrates. But nobody opening their wallet is choosing one over the other.
GeneratedSuperlog vs Audioeye
These are not competitors and no sane buyer weighs them against each other. AudioEye sells managed accessibility compliance — scanning, expert-written fixes, overlay, VPAT/ACR paperwork, and legal defense posture — to organizations worried about ADA lawsuits and procurement reviews. Superlog sells an alert-triage and support-automation layer to DevOps and SRE teams already running Sentry, Datadog, GitHub, and Slack. If you're choosing between them, you've mis-scoped the problem: pick AudioEye for accessibility compliance and Superlog for on-call noise reduction.
GeneratedFrequently asked questions
For makers of superlog
- Get a Verified Listing for superlog ($39): a written decision within 1 working day.
- Free listing for another tool: Free reviews are decided in order, within 3 weeks of the review starting.
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