Firetiger
UnclaimedAI agents autonomously detect, investigate, and resolve production bugs before customers are impacted.
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TL;DR - Firetiger
- AI agents autonomously detect, investigate, and help fix production bugs.
- Offers hyperscale data ingestion with zero per-event charges and high-cardinality indexing.
- Continuously builds a knowledge graph and proposes runbooks for proactive system improvement.
Pricing: Paid only
Best for: Enterprises & pros
Pros & Cons
Pros
- Significantly reduces manual effort and time spent on bug detection and investigation.
- Eliminates per-event ingestion charges, allowing for comprehensive data collection without sampling.
- Proactively identifies issues before they impact customers, improving system reliability.
- Provides deep insights into system dependencies and proposes actionable runbooks.
- Offers flexible data querying and analysis through open data formats.
Cons
- Requires integration with existing tools, which may involve initial setup time.
- Pricing is based on agent operations, which may require monitoring usage to manage costs effectively.
Key Features
AI-powered autonomous agents for bug detection and investigationContinuous monitoring and root cause analysisAutomated hand-off of fixes and verification of deploymentHyperscale event, trace, and log ingestion with zero per-event chargesAutomated knowledge graph generation from telemetryHigh-cardinality indexing without additional costOpen query interoperability with platforms like Athena, Snowflake, and SparkIntegration with existing development tools like GitHub
Pricing Plans
Free TrialGrowth
$599.99/month
- 1 billion events
- 9,000 agent operations
- 5-day data retention
- Get 1,500 more agent operations for $100 / month.
Pro
$1199.99/month
- 2.5 billion events
- 25,000 agent operations
- 14-day data retention
- Get 1,500 more agent operations for $100 / month.
Enterprise
Contact us
- Unlimited events
- Unlimited agent operations
- 30-day data retention
About Firetiger
By Toolradar Team·
Firetiger provides an AI-powered observability solution that uses autonomous agents to proactively identify and fix production issues. Unlike traditional observability tools that require manual investigation, Firetiger's agents continuously monitor systems, detect regressions, elevated response times, and other anomalies, and then investigate root causes. The platform automates the entire incident response lifecycle, from detection and investigation to handing off fixes and verifying their deployment, operating 24/7.
This solution is designed for engineering teams struggling with the chaos of traditional observability in the AI era, where customer complaints often initiate reactive fire drills. Firetiger integrates with existing tools and immediately begins learning from telemetry data, building a knowledge graph of services, customers, and dependencies. It offers hyperscale data ingestion with zero per-event charges, allowing teams to send all events, traces, and logs without sampling, and indexes on high-cardinality dimensions without cost explosion. The underlying data layer is built on open Iceberg tables, enabling interoperability with various analytics platforms.
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Firetiger FAQ
How do Firetiger's AI agents differ from traditional alert systems in detecting issues?
Firetiger's AI agents go beyond simple threshold-based alerts by continuously learning from your telemetry data to detect subtle regressions, elevated response times, and deviations from normal behavior. They don't just alert; they investigate root causes, propose fixes, and verify their deployment autonomously, providing a complete incident response cycle rather than just notification.
What does 'zero ingestion charges' mean for data management, and how does it compare to other observability platforms?
Zero ingestion charges mean that you can send every event, trace, and log, regardless of shape or dimensionality, without incurring per-event fees. This eliminates the need to sample or drop data to manage costs, a common practice with other platforms that charge based on data volume, ensuring you have a complete dataset for analysis without financial penalty.
How does Firetiger's agentic data layer, built on Apache Iceberg, enhance its capabilities?
The agentic data layer, built on object storage and Apache Iceberg, is directly managed by the AI agents. This allows agents to manipulate schema and indexing dynamically to optimize for the specific system they are improving. This architecture also provides open query interoperability, meaning your data is accessible by external tools like Athena, Snowflake, or Spark for business reports and audits.
Can Firetiger track issues down to individual user IDs or session tokens without incurring prohibitive costs?
Yes, Firetiger supports high-cardinality indexing at no extra cost. This means you can index on dimensions like user IDs, session tokens, and request paths without the cost explosion typically associated with such detailed tracking on other platforms. Every field is queryable, allowing for granular tracking of individual customers and their experiences.
What is the 'automated knowledge generation' feature, and how does it benefit engineering teams?
Automated knowledge generation involves Firetiger continuously building a knowledge graph from your telemetry. This graph maps services, customers, and their dependencies. Based on this knowledge, agents can propose relevant runbooks and define quality metrics, which they then continuously monitor. This proactive approach helps teams understand system behavior better and anticipate potential issues.
Source: firetiger.com