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Datadog vs Helicone

Live comparisonView as JSON
Datadog
cloudFree tier (5 hosts)
Helicone
hybridFree (100k requests)
Llm Tracing
Cost Tracking
Evaluation
Prompt Management
Real Time Monitoring
Pricing
Free tier (5 hosts)$15/host/mo Infrastructure$31/host/mo APMCustom Enterprise
Free (100k requests)$20/mo GrowthCustom Enterprise
Open Source
Self-Hosted
SDK Languages
pythonjavascriptgojavarubycsharpphp
pythonjavascripttypescript
Frameworks
langchainopenai-agents
langchainllamaindexvercel-aiopenai-agents
Compliance
soc2hipaagdprpci-dssiso27001
soc2gdpr
Best For
Full-stack observability at scale — infrastructure, APM, logs, and LLM tracing in one platform
Lightweight LLM proxy with cost tracking, caching, and rate limiting — minimal integration effort
Limitations
Expensive at scale; LLM observability is newer and less mature than dedicated tools like Langfuse; vendor lock-in on proprietary data format
Proxy-based architecture adds a network hop; less deep tracing than Langfuse; evaluation features are basic

Supported Not supported Unverified

Datadog LLM Observability and Helicone both capture LLM request data. They occupy different positions in the agent observability stack. Datadog provides deep agent chain tracing with dedicated span types and auto-instrumentation. Helicone provides proxy-based request logging with cost tracking and session grouping. Datadog wins on trace depth and agent chain visualization. Helicone wins on zero-code proxy integration and cost analytics.

Where Datadog wins

  • Deep agent chain tracing with dedicated span types. Datadog defines distinct span types for LLM, Agent, Workflow, Tool, Retrieval, Embedding, and Task operations. Each captures type-specific metadata. An Agent span nests workflows, tool calls, and LLM calls—reconstructing the full reasoning chain. Auto-instrumentation for OpenAI, LangChain, Bedrock, and Anthropic captures these hierarchies without manual annotation. Helicone's Sessions group related requests into hierarchical paths. The hierarchy is defined by the developer via Helicone-Session-Path headers—a manual grouping mechanism, not an auto-instrumented agent chain trace. Helicone does not auto-detect tool calls, retrieval steps, or agent decision boundaries.

  • Anomaly detection and security scanning on agent traces. Datadog provides automated anomaly detection across span names, workflow types, and input/output topics. It surfaces latency spikes, error rate increases, and cost deviations without manual threshold configuration. Prompt injection detection and sensitive data scanning are built-in. Helicone provides alerting on the Pro plan. It does not document automated anomaly detection, prompt injection scanning, or sensitive data redaction across agent traces.

  • Full APM integration for agent infrastructure monitoring. Datadog LLM Observability integrates with the full Datadog stack: infrastructure metrics, distributed tracing, log management, and alerting. Agent traces appear alongside the infrastructure they run on. Helicone is an AI-specific observability layer with no infrastructure monitoring capability—CPU, memory, network, and deployment health require separate tools.

Where Helicone wins

  • Zero-code proxy integration with 100+ model support. Helicone integrates by changing the base URL in the OpenAI SDK—no new libraries, no decorators, no callbacks. It supports 100+ models across OpenAI, Anthropic, Google, Groq, and Vertex AI through a unified gateway. Automatic logging captures every request without code changes. Datadog requires installing the ddtrace Python SDK and configuring auto-instrumentation or manual span creation—more setup, and Python-only. Helicone's proxy model works with any language or runtime that can make HTTP requests.

  • Cost tracking with per-request attribution and provider-level analytics. Helicone tracks per-request cost across all supported providers, with dashboard analytics showing cost by model, user, and time period. The proxy model captures cost data automatically since all requests route through Helicone. Datadog provides cost dashboards within LLM Observability, but cost tracking is limited to models supported by the auto-instrumentation integrations (OpenAI, LangChain, Bedrock, Anthropic).

The agentic difference

Datadog provides agent chain observability—the ability to trace a multi-step agent reasoning chain across orchestrator decisions, tool calls, retrieval steps, and LLM calls with auto-instrumented span types. This is what agent developers need to debug "why did the agent produce this output" in production.

Helicone provides LLM request observability—logging every model call with cost, latency, and session grouping. This answers "what LLM calls did the agent make and what did they cost" but does not reconstruct the agent reasoning chain or auto-detect tool calls and retrieval steps. For agents with simple architectures (single LLM call per request), Helicone's proxy model is faster to integrate. For multi-step agent systems with tool calls and sub-agents, Datadog's span model provides deeper visibility.

When to pick which

  • Pick Datadog when the agent system involves multi-step reasoning chains with tool calls, retrieval, and sub-agent delegation, and the team needs auto-instrumented trace hierarchies — especially if the infrastructure already runs on Datadog.

  • Pick Helicone when the priority is zero-code LLM request logging with cost tracking across multiple providers, the agent architecture is simple enough that session-level grouping suffices, and the team wants proxy-based integration without SDK dependencies.

Last verified: 2026-05-09