Axiom

Profile

Axiom is a cloud-native observability and log management platform purpose-built for timestamped event data at scale. The platform addresses the cost crisis and operational complexity of traditional logging solutions through its proprietary EventDB architecture, achieving extreme compression ratios and sub-second query latency on petabyte-scale datasets. Serving over 30,000 teams from startups to enterprises, Axiom enables organizations to ingest, store, and analyze complete event data without sampling or retention compromises. The platform combines fully managed cloud deployment with transparent usage-based pricing, eliminating infrastructure overhead while maintaining enterprise-grade security certifications including SOC 2 Type II, ISO 27001, GDPR, and HIPAA compliance.

Focus

Axiom solves the fundamental mismatch between exponential event data growth and prohibitive observability costs that force organizations to sample data or limit retention. The platform enables complete data retention and querying at a fraction of traditional costs through specialized columnar storage and compression techniques. Platform engineers, SREs, and DevOps teams gain comprehensive system visibility across distributed architectures without operational burden of managing logging infrastructure. The architecture supports diverse use cases including log analysis, distributed tracing, security monitoring, AI model observability, and product analytics. Organizations benefit from linear scalability, real-time streaming capabilities, and flexible deployment options while maintaining predictable costs aligned with actual usage rather than arbitrary tier boundaries.

Background

Axiom was founded to fundamentally reimagine observability infrastructure for cloud-native architectures, recognizing that traditional logging platforms built for monolithic applications cannot efficiently handle modern distributed systems. Led by cofounder and CEO Neil Jagdish Patel, the company built its platform from scratch using cloud-native primitives rather than retrofitting legacy architectures. The platform serves notable production deployments including Cal.com for cost-effective long-term log retention, Plex for streaming service visibility, and Hapn for petabyte-scale AWS Lambda event analysis. Axiom operates as a fully managed cloud service with active development introducing capabilities like MetricsDB for high-cardinality time-series data and specialized AI engineering observability features.

Main features

Purpose-built columnar storage with extreme compression

EventDB employs a proprietary columnar storage architecture specifically optimized for timestamped event data, achieving compression ratios of 25-50x during real-time ingestion with additional compression through background compaction processes. The storage layer decomposes events into columns using specialized encodings—dictionary encoding for strings, optimized compression for numerics, and bitmap compression for booleans—organized into immutable blocks containing compressed column data, metadata, statistics, and time indexes. Serverless query execution spawns ephemeral workers that operate directly on compressed data with intelligent caching and predicate pushdown, maintaining sub-second latency even on petabyte-scale datasets while eliminating traditional tradeoffs between storage efficiency and query performance.

Axiom Processing Language for event data analysis

APL provides a pipe-based query language specifically designed for event data exploration, using Unix-style operator chaining to progressively filter, transform, and summarize data. The language supports extensive operator categories including tabular operators for filtering and aggregation, scalar functions for value transformation, regex operations for pattern matching, and array functions for complex data structures. Specialized GenAI functions simplify working with AI conversation data by extracting prompts, calculating token costs, analyzing conversation structure, and detecting tool calls without manual JSON parsing. Queries can be constructed through an intuitive visual builder or written directly in APL syntax, with both approaches producing powerful visualizations and enabling real-time data exploration without predefined schemas.

Native OpenTelemetry integration with flexible ingestion

The platform provides comprehensive OpenTelemetry Protocol support with SDKs for Python, Node.js, .NET, Go, Java, and Ruby, enabling standardized instrumentation across diverse technology stacks. Ingestion architecture supports direct API calls via HTTP with NDJSON format, integration with observability tools including Vector and Fluent Bit, and cloud platform integrations for AWS CloudWatch, Lambda, Heroku, and Vercel. Reference architectures accommodate both agent patterns deploying lightweight collectors on every host and aggregator patterns maintaining centralized collection layers for routing and enrichment. The distributed edge layer handles protocol translation, authentication, and data validation while intelligent routing provides high availability through real-time health monitoring and automatic failover.