superlog
Superlog: AI agents that fix production bugs with auto-generated PRs
Superlog is a bold bet on autonomous fix PRs, and the 90% acceptance rate suggests it works in the right environments. If you have mature runbooks and CI/CD, this could cut MTTR dramatically. But if you're not comfortable with AI editing code, you're better off with passive tools like Datadog or Sentry.
Verified 1d ago · liveness 82/100 · cite: rightaichoice.com/tools/superlog
- DevOps engineers automating runbooks with AI
- SREs reducing MTTR via autonomous PR creation
- Platform teams needing self-hosted observability with open-source flexibility
- Startups wanting proactive monitoring without high telemetry costs
- Non-technical users needing simple dashboards without agent control
- Teams unwilling to allow AI to modify production code or infrastructure
- Small projects with minimal incidents where manual alert handling suffices
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Skip Superlog if you are unwilling to let AI modify production code, lack mature runbooks and CI/CD pipelines, or need simple dashboards without agent control.
Going past 50 included investigations per month costs $1.50 each, which can add up quickly if you have frequent incidents.
Superlog's pricing is friendly for small teams and startups: free tier with 50 investigations/month and generous telemetry, pay-as-you-go with no base fee. Compared to Datadog or Sentry which charge per host or per event, Superlog's metered investigations and telemetry can be cheaper for low-volume users, but heavy users might find enterprise costs higher.
In short
superlog — Superlog: AI agents that fix production bugs with auto-generated PRs. Best for DevOps engineers automating runbooks with AI, SREs reducing MTTR via autonomous PR creation, Platform teams needing self-hosted observability with open-source flexibility. Free to start; paid plans from $150/mo.
What's new in superlog
Checked yesterdayAcross the latest 4 updates: 4 feature updates.
Smarter custom MCP setup
Custom MCP server setup now auto-detects OAuth and keeps manual credentials as fallback.
Incident page, redesigned
Rebuilt incident page with unified activity feed and inline questions.
Vercel, Railway, and Render connectors
One-click connectors for Vercel, Railway, Render added.
Alert episodes
Contiguous alert firings grouped into episodes.
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.
44 mentions across 5 sources (Hacker News, Product Hunt, Bluesky, GitHub, Lemmy) · researched Jul 26, 2026.
- +Automatically creates PRs to fix production bugs, reducing MTTR.
- +Groups noisy alerts into single incidents, cutting alert fatigue.
- +Open-source Apache 2.0 license allows full customization.
- +Integrates seamlessly with Slack, PagerDuty, and cloud providers.
- +Auto-instrumentation detects services and sets up monitoring automatically.
- −Slack onboarding is mandatory, excluding teams on other platforms.
- −AI fix PRs may introduce errors if confidence gate is low.
- −No dedicated API documented for third-party integrations.
- −Trace cardinality can balloon costs without proper sampling.
- −Self-hosted version not yet available despite open-source code.
- • Trace storage costs can exceed free tier if cardinality spikes
- • Cloud-only routing may incur egress fees for self-hosted telemetry
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: August 2026
How we score →Key Features
- AI agents auto-generate resolution PRs with regression tests
- Incident grouping with fingerprinting and severity scoring (SEV1-3)
- Impact assessment for each incident
- Confidence Gate – posts findings when not sure, pulls in engineers
- Slack integration – @-mention bot to ask about code and telemetry
- MCP server with OTel-based logs, traces, metrics, and dashboards
- One-click connectors for Vercel, Railway, Render
- OpenTelemetry-native ingestion for logs, traces, metrics
- Custom context – Notion, Linear, custom MCPs, AGENTS.md, CLAUDE.md
- Memory system – learns from PR reviews to improve future fixes
- Alert episodes – contiguous alert firings grouped into single episodes
- Redesigned incident page with unified activity feed
- Dashboard template variables for multi-service environments
- Open-source core with self-hosting option
- Role-based access control
About superlog
Superlog is an open-source observability platform that uses AI agents to automatically diagnose and fix production issues. It ingests logs, traces, and metrics via OpenTelemetry, then turns incidents into actionable pull requests, complete with regression tests, and delivers them to Slack for review. Backed by Y Combinator, it's built for DevOps engineers, SREs, and platform teams who want to cut MTTR without drowning in alert noise. Unlike traditional observability tools that only alert you, Superlog's agents go a step further. When an error occurs, the platform groups similar events into a single incident, assigns a severity score (SEV1-3), and assesses business impact. It then prepares a resolution PR, passing it through CI checks and even addressing human comments in follow-ups. A key differentiator is the "Confidence Gate": if the agent isn't sure about a fix, it posts findings and pulls in the right engineers for context. The platform is deeply agent-friendly. It pulls context from your Notion knowledge base, Linear tickets, and custom MCPs, and it maintains a memory system that improves PR quality from every review. You can also ask questions directly in Slack via @-mentions, and the MCP server exposes logs, traces, metrics, and dashboards, so you can build dashboards on demand without switching tools. Recent updates include one-click connectors for Vercel, Railway, and Render, a redesigned incident page with a unified activity feed, and alert episodes that group contiguous firings. Superlog prices telemetry and investigations separately. The free tier gives you 50 investigations per month and generous telemetry limits, with a pay-as-you-go option that has no base fee. This makes it a low-risk starting point for smaller teams, while larger organizations may find the custom enterprise tier more suitable. It's a niche play—not for teams unwilling to let AI touch production code, but for those ready to automate runbooks, it's a step toward self-healing infrastructure.
Behind the Verdict
Superlog is a distinctive observability platform that goes beyond alerting to actually fix bugs. Its AI agents analyze errors, group them into incidents, and generate pull requests with regression tests. The platform is open-source and integrates deeply with Slack, Notion, Linear, and GitHub, making it a natural fit for teams that already rely on these tools. Strengths: The autonomous PR generation is the standout feature, saving significant time for teams dealing with repetitive bugs. The Confidence Gate is a thoughtful touch, preventing unsafe automatic fixes. The memory system improves over time, and the MCP server exposes full telemetry for on-demand dashboards. The pricing is transparent and flexible, with a free tier and pay-as-you-go options. Weaknesses: The concept requires trust in AI to modify code, which may not suit all teams. The free tier's 50 investigations/month might be limiting for high-volume environments. The reliance on open-source self-hosting means you need to handle your own infrastructure if you don't want to use the cloud service. Where it fits: Ideal for DevOps engineers and SREs in startups or mid-sized companies that want to automate runbooks and reduce MTTR without massive investment. Teams with defined runbooks and CI/CD will get the most value. Where it doesn't: Not for non-technical users or teams that need simple dashboards without AI control. It's also not for organizations that lack mature runbooks or are hesitant about AI modifying production code. Overall, Superlog is a niche but powerful tool for forward-thinking engineering teams.
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Real-world workflow fit
Concrete scenarios for the personas superlog actually fits — and what changes day-one when you adopt it.
You connect your app via one-click Vercel connector and within minutes start receiving PRs for bugs, which you review in Slack and merge.
Outcome: Reduced MTTR from hours to minutes, with most PRs accepted.
You set up Slack @-mentions to ask Superlog about production issues, and it provides answers and even generates dashboards on demand.
Outcome: Your team spends less time investigating and more time on strategic work.
You self-host Superlog and configure custom MCPs and Notion context to get fixes tailored to your system.
Outcome: You achieve self-healing infrastructure with fewer manual interventions.
Use Cases
- Automatically roll back a failed deployment when error rates spike
- Restart a crashed service and notify the on-call engineer via Slack
- Scale up Kubernetes pods during traffic surges using predictive AI
- Run security incident playbooks after detecting anomalous login patterns
- Investigate and fix a Stripe credential error by creating a validation PR
- Use Slack @-mentions to ask Superlog about production issues
Models Under the Hood
as of 2026-08-11
Limitations
- Pricing page shows a free plan with 50 investigations/month and telemetry limits, with usage beyond included investigations costing $1.50 each.
- Pay-as-you-go includes a one-time grant of 100 promotional investigations plus 50 included monthly.
- Telemetry overages are $0.50 per million spans/logs and $0.15 per million metric points.
as of 2026-08-14
Verification history
We have re-verified superlog 9 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-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-checked, vendor evidence unchanged
- — 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
Showing the 6 most recent of 9 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 superlog tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Side projects and first installs with up to 50 incidents investigated per month, perfect for trying out Superlog.
What this tier adds
Starter tier with 1M spans, 5M logs, 10M metric points, 30-day retention.
Pay as you go
Usage-based
Ideal for
Teams that want flexibility with no base fee and only pay for what they use.
What this tier adds
Includes 50 investigations per month plus a one-time 100 promotional investigations; telemetry overages apply.
Pro
$150/mo
Ideal for
Growing teams needing higher volume and advanced features beyond the free tier.
What this tier adds
Priced at $150/mo with expanded limits (specifics on site).
Max
$300/mo
Ideal for
High-volume teams requiring maximum capacity and premium support.
What this tier adds
Priced at $300/mo with even higher limits and priority features.
Enterprise
Custom
Ideal for
Large organizations with custom requirements, dedicated support, and advanced security.
What this tier adds
Custom pricing with tailored SLAs, SSO, and compliance features.
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 is friendly for small teams and startups: free tier with 50 investigations/month and generous telemetry, pay-as-you-go with no base fee. Compared to Datadog or Sentry which charge per host or per event, Superlog's metered investigations and telemetry can be cheaper for low-volume users, but heavy users might find enterprise costs higher.
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.
Most teams can connect Superlog to their app in under 10 minutes using one-click connectors or OTLP. You'll see your first PR within a few hours of enabling monitoring.
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 Datadog or Sentry: Import errors via the built-in integrations and start using Superlog's PR generation.
- ↗To Datadog or Sentry: Export your telemetry using OpenTelemetry and redirect your agents accordingly.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with superlog
Common stack mates teams adopt alongside superlog, with the specific reason each pairing earns its keep.
Dash0
OpenTelemetry-native observability with autonomous AI SRE Agent0, plus AI Coding Insights to monitor coding agents in production.
Resolve AI
Autonomous AI agents for on-call, incident response, and production ops—cut MTTR and let engineers get back to building.
Interfere
AI-driven production monitoring that auto-triage bugs and suggests fixes.
Featured Head-to-Head Comparisons
Superlog vs Chili Piper
Chili Piper and superlog serve entirely different buyers. Chili Piper is a sales conversion platform for B2B teams wanting to turn web traffic into meetings instantly—ideal if you live in Salesforce/HubSpot. superlog is an incident management observability tool for DevOps/SREs who want AI to fix production issues autonomously. Choose based on your domain: revenue ops or infrastructure reliability.
Superlog vs Temporal Ai
Choose Temporal AI if you need to build resilient, stateful AI agents or long-running workflows that survive failures—its durable execution is unmatched. Pick Superlog if your primary pain is production incident response and you want AI to auto-remediate issues in your infrastructure. They serve different verticals; your choice depends on whether you're orchestrating code or reacting to incidents.
Superlog vs Audioeye
Choose AudioEye if you need enterprise-grade web accessibility compliance with legal support and VPAT documentation. Choose superlog if you're a DevOps team wanting open-source, AI-driven automated incident remediation. They solve completely different problems — no direct competition.
Alternatives to superlog
View allDash0
OpenTelemetry-native observability with autonomous AI SRE Agent0, plus AI Coding Insights to monitor coding agents in production.
Resolve AI
Autonomous AI agents for on-call, incident response, and production ops—cut MTTR and let engineers get back to building.
Frequently Asked Questions
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