Prilog
Summary
The incident fires at 2 AM, the on-call engineer spends an hour grepping logs, opens a Jira ticket, assigns it to a backlog, and the same error recurs next Thursday — that loop is what Prilog is built to break.
Prilog detects production incidents, maps the failure back to the responsible code, generates a candidate fix, and routes that fix into your existing PR and task workflow — without a human manually triaging each step. Teams using Datadog, SigNoz, or AWS get the observability data ingested directly; teams on GitHub, GitLab, Jira, or Linear get the output delivered where they already work. The autonomous loop covers detection through remediation, which means recurring incidents that previously consumed hours of on-call time become queued PRs. The ceiling appears at complex, cross-service failures where root cause spans multiple repositories — the fix quality drops and engineers end up reviewing suggestions that require significant rework before merging.
Bottom line: Prilog earns its place for teams drowning in repeated single-service incidents and wanting a bot-to-PR pipeline — but teams dealing with distributed, multi-repo failures will find themselves editing generated fixes more than shipping them.
Pricing Plans
Usage-BasedLast verified 2 days ago- Price
- $249+/mo
- Free Tier
- 7 days, 3 fixes, 1 project
FREE TRIAL
7-day trial with limited fixes and projects
- 7 days
- 3 fixes
- 1 project
- No credit card required
Starter
Entry-level plan for small projects
- 30 fixes per month
- Up to 3 projects
- $2.00 per AI Credit
- Email support
Professional
Mid-tier plan for growing teams
- 120 fixes per month
- Up to 15 projects
- $2.00 per AI Credit
- Priority queue
- Email & Slack support
Enterprise
Enterprise plan with unlimited usage and advanced features
- Unlimited fixes & projects
- SSO/SAML
- SLA
- Security review
- On-prem support
- Custom integrations
- Advanced compliance
View full pricing on prilog.ai →
Pricing may have changed since last verified. Check the official site for current plans.
Community Performance Report Card
No community ratings yet. Be the first to rate this tool!
Community Benchmarks Community
Sign in to submit a benchmarkNo community benchmarks yet. Be the first to share a real-world data point.
Pros
Sign in to edit- End-to-end incident-to-PR automation, so the gap between an alert firing and a remediation candidate appearing in your task tracker shrinks from hours of manual triage to an automated handoff.
- Native integration with Datadog, SigNoz, and AWS for ingestion, paired with GitHub, GitLab, Jira, and Linear for output, which means the tool drops into an existing stack without forcing a workflow change on either the observability or the engineering side.
- Historical incident learning that the vendor states improves fix suggestions over time, so recurring failures that previously required an engineer to re-diagnose from scratch get progressively better-prepped fix candidates.
- SOC 2 and GDPR compliance posture built in, which means security review for granting an agent read access to production logs and write access to repos does not become the bottleneck that kills the rollout.
- Freemium entry point that lets a team validate fix quality on real incidents before committing budget, so you find out whether the generated PRs are merge-ready or draft-quality before the contract is signed.
Cons
Sign in to edit- Cross-service, multi-repository incidents hit a quality wall: when root cause spans more than one service, the generated fix addresses the symptom visible in the logs rather than the upstream source, and engineers spend more time correcting the suggestion than they would have spent writing it — at that point the tool saves no time on your worst incidents, only your easiest ones.
- No self-hosted deployment option exists, which means teams under strict data-residency mandates or operating in air-gapped environments cannot use Prilog at all, and those teams move to a competitor or build internal tooling regardless of how well the fix quality performs in evaluation.
- Fix output is gated on credits tied to paid tiers, so teams running high incident volumes hit the usage ceiling and face a choice between throttling the automation or absorbing the cost increase — at scale, the per-fix economics need to be validated against actual merge rate before the bill grows.
Community Reviews
Sign in to write a reviewNo reviews yet. Be the first to share your experience.
About
- Platforms
- Web-based SaaS; works with cloud repositories (GitHub, GitLab) and observability platforms
- API Available
- No
- Self-Hosted
- No
- Last Updated
- 2026-06-04T08:35:45.540Z
Best For
Who it's for
- Engineering teams managing high-volume production incidents
- Teams using Datadog, SigNoz, or AWS for observability
- Organizations seeking to automate bug-to-PR workflows
- Teams already using GitHub, GitLab, Jira, or Linear
- Companies prioritizing security and regulatory compliance
What it does well
- Reducing MTTR for recurring production incidents
- Automating root-cause analysis from observability data
- Generating code fixes directly from production logs
- Routing remediation work into existing CI/CD and task-management workflows
- Learning from historical incidents to improve future fix quality
Integrations
Discussion Community
Sign in to commentNo discussion yet. Sign in to start the conversation.
Compare Prilog
Spotted incorrect or missing data? Join our community of contributors.
Sign Up to ContributeCommunity Notes & Tips Community
Sign in to contributeBe the first to contribute. General notes, observations, gotchas, and tips from people who use this tool day-to-day.
Frequently Asked Questions
- Is Prilog free?
- Prilog is a paid tool ($249+/mo). A 7-day free trial is available.
- Is Prilog open source?
- No — Prilog is a closed-source tool. Source code is not publicly available.
- What platforms does Prilog support?
- Prilog is available on: Web-based SaaS; works with cloud repositories (GitHub, GitLab) and observability platforms.
Hours Saved & ROI Stories Community
Sign in to contributeBe the first to contribute. Concrete time/cost savings, with context. e.g. "Cut my code review backlog from 4h to 45m per week."
Curated lists that include this category
Prilog watches your observability stack, identifies a production incident, traces it to the offending code, generates a fix, and opens a PR or task — all without a human touching the keyboard between alert and remediation candidate. The core workflow ingests signals from Datadog, SigNoz, or AWS, runs root-cause analysis against the codebase, produces a code-level change, and delivers it into GitHub, GitLab, Jira, or Linear. Each completed fix feeds back into the model’s context, so the vendor states fix quality improves as Prilog accumulates incident history from your specific codebase.
The differentiating claim is the closed loop: most observability tools stop at surfacing the problem; Prilog extends that to generating and routing the fix. That distinction matters for teams where MTTR is tracked and the gap between ‘we know what broke’ and ‘the PR is open’ is measured in hours of engineer time. The vendor also highlights SOC 2 and GDPR posture as first-class features, which matters for organizations that would otherwise block an agent with write access to production repos from reaching sensitive log data.
Prilog fits tightly scoped, high-recurrence incidents in a single service — the scenario where the pattern is recognizable and the fix is localized. It starts to strain when incidents span multiple services, require cross-repo changes, or involve failure modes the system has not seen before. In those cases, the generated fix arrives as a starting point rather than a merge-ready patch, and engineers absorb the review cost. There is no self-hosted option, so teams with hard data-residency requirements or air-gapped environments cannot deploy it regardless of compliance certifications.