#AI observability
3 tools curated for you
Move from alert to answer in seconds with Honeycomb's purpose-built columnar data store that executes complex queries at incredible speed across your entire distributed system. Debug LLM behavior and low latency microservices directly by tracing every request through Honeycomb's unified telemetry pipeline without switching contexts. Investigate anomalies before they impact customers using SLO-based monitoring that automatically detects and surfaces problems in your AI-driven software. Access observability data directly from your AI agent IDE via Honeycomb MCP to maintain a streamlined investigative process without leaving your workflow. Control telemetry costs while gaining deeper insights by defining custom strategies to collect, enrich, filter, and shape your data before it reaches storage. Uncover hidden issues through dynamic, explorable visualizations that let you interact with data in real-time and follow any investigative path without dead ends. Integrate observability seamlessly into your existing tech stack with native support for over 60 tools across the full software development lifecycle. Enrich every engineer with expert-level investigation guidance through Honeycomb Intelligence's AI-powered insights for instant root cause analysis. Process voluminous, complex telemetry from contemporary software systems at scale without compromising query performance or incurring extra charges for business context.
Gain complete visibility into every AI agent execution, LLM call, and tool usage with OpenTelemetry-based tracing and debugging that eliminates blind spots in production. Attribute token and LLM costs to specific agents and tasks with real-time cost attribution, enabling precise budget management and resource planning across all AI operations. Prevent sensitive data leakage before it reaches logs through automatic PII detection that flags and blocks exposure in agent responses, ensuring privacy regulation compliance. Stop policy violations and restricted model usage mid-execution with runtime policy enforcement that sends immediate notifications and triggers hard blocks on serious breaches. Demonstrate adherence to EU AI Act and HIPAA controls by exporting compliance evidence directly from the platform, simplifying regulatory audits and reducing legal risk. Eliminate vendor lock-in by operating across any AI model, agent framework, or cloud environment with a vendor-neutral SDK supporting LangChain, CrewAI, OpenAI Agents SDK, and Claude Code. Monitor health scores, policy violations, and operational parameters for all registered AI agents from a single dashboard with the unified agent registry. Improve AI agent performance continuously by comparing responses, testing prompts with datasets and scorers, and grading effectiveness through the prompt registry and evaluation feature.
Gain complete visibility into every layer of your AI application stack, from model calls to database queries, with OpenTelemetry-based distributed tracing that captures each agent step and API request. Pinpoint bottlenecks and failures across complex distributed AI systems by tracing the full execution path of every request through all services and infrastructure layers. Reduce operational costs by identifying resource-intensive API requests, database queries, and model calls through detailed usage tracking and cost tracking features. Debug AI applications faster using a SQL query interface that runs Postgres-flavored queries over every span, letting you explore metrics and telemetry data with precision. Continuously improve LLM performance with built-in evaluation methods including living evals inside traces, programmatic checks, LLM judges, and human review workflows. Handle deeply nested trace and attribute data efficiently with FusionFire, a columnar engine built on Apache DataFusion designed for wide, complex AI system telemetry. Monitor APIs and LLM calls across Python, JavaScript/TypeScript, Rust, Go, and Java using native SDKs and OpenTelemetry compatibility for unified observability. Track and log every interaction within your AI application stack, from model execution times to API response metrics, enabling data-driven optimization decisions.