×

What does it do?

  • Incident Resolution
  • Production Incident Management
  • Downtime Reduction
  • Developer Productivity
  • Observability

How is it used?

  • Use web app and Slack
  • get debugging insights and fixes.
  • 1. Access web app
  • 2. Engage w/ AI-Moose
  • 3. Collaborate in Slack
See more

Who is it good for?

  • Software Developers
  • DevOps Engineers
  • IT Operations Professionals
  • Site Reliability Engineers
  • Technical Support Specialists

Details & Features

  • Made By

    Wild Moose
  • Released On

    2022-08-27

Wild Moose is an AI-powered platform that autonomously handles production debugging and incident management by engaging with logs, metrics, and code. It leverages generative artificial intelligence to provide actionable insights and fix suggestions, allowing developers to focus on innovation rather than troubleshooting.

Features:
- Autonomous debugging: AI-Moose analyzes logs, metrics, and code to resolve issues before they escalate
- Actionable insights: Surfaces insights and provides fix suggestions to quickly address and resolve production issues
- Auto-generated playbooks: Generates custom investigative tools and playbooks tailored to specific incidents (higher-tier plans)
- Real-time collaboration: Developers can collaborate seamlessly within Slack, sharing insights and progress
- Incident management: Supports incident kickoff, status updates, and incident summaries
- End-to-end encryption: Ensures user data is encrypted at all times
- Data Processor Agreements (DPA): Established with LLM providers to ensure data is not used or stored for training purposes
- On-premise option: Available for Business and Enterprise customers, ensuring data is accessed only within the user's network
- Integrations: Supports Slack and Datadog (Starter plan), with additional integrations for Business and Enterprise plans

How It Works:
Users interact with Wild Moose primarily through a web app and Slack integration. AI-Moose autonomously kicks off investigations by engaging with logs, metrics, and code to surface insights and provide fix suggestions. Developers can collaborate within Slack to address issues using the insights and suggestions provided by the AI.

Integrations:
Wild Moose supports integrations with Slack for real-time collaboration and incident management, Datadog for pulling relevant metrics and logs, and additional platforms for Business and Enterprise plans, including observability data, codebase, CI/CD, and ticket management platforms.

Generative AI and Foundation Models:
Wild Moose leverages generative AI to autonomously debug and manage incidents. The specific AI foundation models or LLMs used are not detailed, but the platform ensures that data processed by these models is not used for training purposes through established Data Processor Agreements.

Availability and Launch:
Wild Moose is available as a web app and integrates with Slack. The company was founded and the product was launched in 2024.

Target Users:
Wild Moose is suitable for individual developers looking to enhance their production debugging skills, development teams aiming to improve product quality and incident response efficiency, and large organizations with custom needs at an enterprise scale, requiring tailored integrations and 24/7 premium support. It offers various pricing plans to cater to different user needs, from free plans for individual developers to enterprise plans for large organizations. Wild Moose is not open source.

  • Supported ecosystems
    Datadog, Sentry, Elastic, Slack, Datadog, Slack, Meta
  • What does it do?
    Incident Resolution, Production Incident Management, Downtime Reduction, Developer Productivity, Observability
  • Who is it good for?
    Software Developers, DevOps Engineers, IT Operations Professionals, Site Reliability Engineers, Technical Support Specialists

Alternatives

BlackBox AI is an AI-powered coding assistant that helps developers write code faster using autocomplete, generation, and search features.
LanceDB is an open-source vector database designed for AI applications, offering efficient storage, management, and retrieval of multi-modal data embeddings.
Langfuse provides tools for teams to build, debug, and improve large language model applications.
Buster is an AI platform that converts natural language queries into SQL commands for databases.
Unify.ai provides a single API to access and combine multiple large language models, optimizing performance based on user-defined criteria.
Superpowered.ai is an AI platform that integrates LLMs with user data to generate accurate, cited responses for various domains.
Lantern is a high-performance, cost-efficient vector database for developers to build AI apps easily.
Helicone is an open-source observability platform for developers working with Large Language Models.
UpTrain is an open-source LLMOps platform that streamlines evaluation, experimentation, and regression testing for developers working with large language models.
SciPhi simplifies the development, deployment, and scaling of Retrieval-Augmented Generation (RAG) systems.