Logwise 
Logwise is an AI log-analysis or developer tool for interpreting software events and troubleshooting systems.
Starting price Free + paid plans
Tool Information
Logwise is an AI log-analysis or developer tool for interpreting software events and troubleshooting systems.
Users define a focused goal, provide only authorized and minimal inputs, generate a draft, verify facts and source material, correct quality and rights issues, test the result, and keep a human responsible for publication or action.
Free or limited access is available with optional paid features; stable numeric pricing was not verified.
Logs can contain credentials and personal data, while AI can misdiagnose incidents. Redaction, least privilege, retention, source correlation, security review, and human incident response matter.
F.A.Q (3)
Pros and Cons
Pros
- Logwise now presents its incident-response product under the Resolvd name
- Resolvd builds a knowledge base from logs; applications; and other sources
- Semantic search offers a simpler way to query operational data
- Automated anomaly detection can surface unusual system behavior
- Events are correlated across connected data sources
- Logs; metrics; documentation; and communication context are combined
- Alert-noise reduction helps responders focus on critical incidents
- AI-generated insights can shorten manual log review
- Slack integration delivers findings within a team workflow
- Jira integration can connect diagnosis with issue tracking
- PagerDuty integration aligns insights with on-call alerts
- Centralized context reduces switching among monitoring systems
- Proactive alerting can flag problems before manual investigation
- The platform targets both incident diagnosis and resolution
- A demo path lets teams assess fit with their own stack
- In-workflow delivery can speed collaboration during outages
Cons
- Logwise branding has changed to Resolvd and old references may cause confusion
- Public pricing is not shown on the landing page
- The site claims autonomous resolution without detailing approval boundaries
- Incorrect anomaly detection can create false alarms
- Missed anomalies can give teams a false sense of safety
- AI-generated root-cause explanations may confuse correlation with causation
- Connecting logs; Slack; Jira; and PagerDuty centralizes highly sensitive data
- Logs commonly contain credentials or personal data that must be redacted
- Automated remediation can worsen an outage if actions are unsafe
- Semantic search may miss exact identifiers or rare error signatures
- Incident responders still need raw evidence and reproducible queries
- The advertised response-time and manual-review reductions are vendor claims
- Data-volume limits; retention; and indexing delay require contract review
- Integration permissions should follow least-privilege principles
- A knowledge base becomes stale when systems and runbooks change
- Teams need rollback; audit logs; human escalation; and disaster-recovery testing
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Overview
Before you subscribe
- Check the currency, billing period, automatic renewal, usage limits and seats at checkout — prices and promotions change.
- App-store pricing can differ from website pricing by country, device and tax.
- Share only authorised data, and read the provider's encryption, retention and model-training terms first.
- AI output can be incomplete or wrong. Verify facts, citations, calculations and code before you publish or ship.
- Commercial usage rights vary by plan, provider terms and jurisdiction.
- None of these tools replaces a qualified professional for medical, legal or financial decisions.
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