Axiom
Stop guessing. Ship AI products with confidence and gain unified observability for modern applications.
Overview
Axiom is a comprehensive observability platform designed to help engineering teams gain deep insights into their applications and AI products. It unifies logs, traces, and (soon) metrics into a single system, eliminating the need for multiple tools. A key differentiator is its focus on AI engineering, providing specialized capabilities for evaluating prompts, tracing AI agent workflows, and monitoring the cost and latency associated with various AI providers. The platform emphasizes zero-sampling distributed tracing to ensure no data is lost, offering a complete picture of every request. This, combined with volumetric logging and application performance monitoring, allows teams to quickly identify and resolve issues, improve service quality, and make data-driven decisions. Axiom supports flexible data ingestion through OpenTelemetry, its Events API, and integrations with popular platforms like Vercel and Cloudflare, making it adaptable to diverse technical stacks.
Pricing
Pros & Cons
Pros
- Unified platform for logs, traces, and metrics simplifies observability.
- Zero-sampling distributed tracing provides complete visibility into requests.
- Specialized features for AI engineering, including prompt evaluation and workflow tracing.
- Supports long-term active retention for historical data analysis.
- Flexible data ingestion methods, including OpenTelemetry and custom APIs.
- Offers a playground to try the service with live data without an account.
Cons
- Metrics feature is listed as 'coming soon', indicating incomplete observability suite.
- Specific pricing details are not immediately visible on the main page, requiring navigation to a separate page.
- May have a learning curve for teams new to advanced observability or AI-specific tracing.
- Reliance on integrations for certain platforms might introduce dependencies.
- The breadth of features could be overwhelming for very small, simple projects.
Use Cases
Reviews & Ratings
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Best For
- Monitoring and debugging distributed microservices architectures.
- Evaluating and optimizing AI model performance and prompt engineering.
- Tracing complex AI agent workflows to understand decision-making.
- Tracking cost and latency of AI providers in real-time.
- Ensuring service quality and identifying performance bottlenecks in applications.
- Retaining and analyzing historical log and trace data for compliance or long-term trends.
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