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bitdrift.ai Review

bitdrift.ai is an agentic mobile observability platform that provides real-time insights into user interactions and operational performance across devices.

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bitdrift.ai — product screenshot

Why it matters

1Launched bitdrift AI on August 19, 2026, as the world's first agentic mobile observability platform.
2Secured $15 million in Series A funding in December 2023 from investors including Amplify Partners and 01 Advisors.
3Offers a pricing model based on Monthly Active Applications (MAA) with no log limits or overage fees.
4Provides full-fidelity, unsampled data directly from user devices, enabling autonomous AI agents.

About bitdrift.ai

Business Model
Subscription SaaS
Usage Pricing
Contact for pricing per stream
Platforms
Web, API
Target Audience
Developers and product teams looking to enhance mobile observability.

Pricing Plans

Standard
Contact for pricing / monthly
  • • Concurrent streams
  • • Installs
  • • Performance monitoring
Enterprise
Contact for pricing / monthly
  • • All features of Standard
  • • Dedicated support
  • • Custom SLAs

Cost Examples

  • • Contact for specific use cases

Leadership

Pete MorelliCEOLinkedIn
Iain FinlaysonCTO
Valerii KuznietsovCo-founder
API DocsGitHub

Specs

API Docs

View Documentation →

API Available

Yes, public API

overview

What is bitdrift.ai?

bitdrift.ai is an agentic mobile observability platform developed by bitdrift that enables developers and engineering teams to gain real-time, unsampled insights into mobile application performance and user behavior. It empowers AI agents to autonomously investigate and resolve mobile issues, providing infinite visibility into applications.

Launched on August 19, 2026, bitdrift AI differentiates itself by offering a full-fidelity system that allows AI agents to query mobile user behavior and act on it autonomously. The platform captures on-device telemetry, including logs, traces, and session context, to facilitate real-time anomaly detection and performance monitoring. It supports models such as Claude Code, Cursor, Codex, and Copilot, with multimodality for text and vision data.

features

Key Features of bitdrift.ai

bitdrift.ai provides a robust set of features designed for comprehensive mobile observability, leveraging AI to automate issue detection and resolution. The platform's core capabilities focus on real-time data capture and analysis from mobile devices.

  • Agentic Mobile Observability: Enables AI agents to autonomously investigate and resolve mobile issues.
  • Real-time Anomaly Detection: Identifies performance issues and unusual user behavior as they occur.
  • Full-Fidelity, Unsampled Data: Captures 100% of on-device telemetry without sampling, eliminating blind spots.
  • Automatic Instrumentation: Utilizes AI-driven SDK skills to instrument mobile applications for observability.
  • Unlimited Data Visibility: Offers no log limits or overage fees based on telemetry volume, allowing extensive data capture.
  • 3D Session Replay: Provides visual, context-rich debugging with detailed session replays.
  • Open Standard Function Calling: Supports open_standard for function calling, enhancing interoperability.
  • Multimodality Support: Processes both text and vision data for comprehensive insights.
  • API Availability: Offers a public API (https://docs.bitdrift.dev/api/index.md) for programmatic access and integration.

use cases

Who Should Use bitdrift.ai?

bitdrift.ai is primarily designed for engineering teams and developers focused on mobile application development and maintenance. Its capabilities are tailored to enhance the efficiency and effectiveness of mobile observability and issue resolution.

  • Developers and Mobile Engineers: For real-time debugging, crash reporting, and performance monitoring of mobile applications.
  • Engineering Teams: To improve Mean Time To Resolution (MTTR) for mobile issues and proactively detect problems.
  • AI Agents: To autonomously query mobile user behavior and act on full-resolution, unsampled data.
  • Product Teams: For analyzing user journeys and understanding real user monitoring data to inform product decisions.
  • Research Teams: To leverage AI models like Claude Code, Cursor, Codex, and Copilot for advanced mobile insights.

how to use

How to Use bitdrift.ai

To begin using bitdrift.ai, engineering teams integrate the bitdrift SDK into their mobile applications to start capturing on-device telemetry. The platform then processes this data, making it accessible for real-time monitoring and AI-driven analysis.

  • 1Integrate the bitdrift SDK into your mobile application to enable data capture.
  • 2Configure AI agents to monitor specific metrics or user behaviors within the platform.
  • 3Utilize the bitdrift Public API and bd skills for programmatic access to observability data.
  • 4Access real-time dashboards and 3D session replays for debugging and performance analysis.
  • 5Define workflows for autonomous investigation and resolution of detected mobile issues.

pricing

bitdrift.ai Pricing & Plans

bitdrift.ai employs a subscription-based pricing model that is primarily based on Monthly Active Applications (MAA), rather than telemetry volume. This approach aims to provide predictable costs without log limits or overage fees. Specific pricing details for its tiers are available upon contact.

  • Standard: Contact for pricing (monthly)
  • Enterprise: Contact for pricing (monthly)

Pros

  • +Provides full-fidelity, unsampled data directly from user devices, eliminating blind spots.
  • +Enables autonomous AI agents to investigate and resolve mobile issues, improving MTTR by 10x for beta users.
  • +Offers a predictable pricing model based on Monthly Active Applications (MAA) with no log limits or overage fees.
  • +Purpose-built for mobile environments, addressing unique challenges like network conditions and UI issues.
  • +Includes advanced debugging features such as 3D session replay and context-rich logs.
  • +Supports integration with OpenTelemetry and offers a public API for extensibility.

Cons

  • −Specific pricing details for Standard and Enterprise tiers are not publicly disclosed, requiring direct contact.
  • −Specific numerical API rate limits (e.g., requests per minute) are not publicly detailed in documentation.
  • −The platform's advanced agentic AI capabilities may require a learning curve for teams new to AI-driven observability.
  • −While offering broad capabilities, its specialized focus on mobile may not be ideal for general-purpose observability needs across diverse platforms.

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