Agentifact assessment — independently scored, not sponsored. Last verified Mar 6, 2026.

MonitoringN/A

Datadog LLM Observability

Mature enterprise observability platform with strong tracing/integrations and company trust, but limited direct API evidence lowers agent readiness.

Visit Datadog LLM ObservabilityVerified · March 6, 2026

✓ Our Verdict

Viable option — review the tradeoffs

Use Case

You need end-to-end visibility into LLM chains and agent workflows to debug non-deterministic behavior, track costs, and ensure reliability in production.

SolutionDatadog LLM Observability provides tracing of prompts, tool calls, intermediate steps, latency, token usage, and quality metrics, integrated with enterprise APM.

SetupInstrument agents with Datadog SDK calls in your code; leverages existing Datadog account for visualization.

Rich traces and dashboards reveal bottlenecks and errors quickly; strong for agentic apps like Bedrock/Strands but requires SDK integration—not fully automatic.

tracing

Use Case

You want to evaluate and compare LLM model performance across providers without rebuilding pipelines.

SolutionBuilt-in Experiments and Clusters views enable semantic/functional scoring, A/B testing, and topic-based performance analysis.

SetupAdd SDK to evaluation scripts; use unified traces for multi-provider comparison via AI proxy.

Scalable offline evals with reproducible metrics; excels at correlating model issues from infra, though custom evals need engineering.

evals

Limitation — major

Limited Direct API Access

Relies on SDK instrumentation rather than automatic tracing; lowers agent readiness for fully autonomous setups without code changes.

Datadog LLM Observability vs New Relic

Datadog leads in dedicated LLM/agent tracing; New Relic offers broader full-stack but less AI-specific depth.

Choose Datadog LLM Observability

Pick Datadog for agentic workflows, tool-level token tracking, and Bedrock/ADK integrations.

Choose New Relic

Pick New Relic for predictable data-ingest pricing and general APM without heavy LLM focus.

Caution

Enterprise Pricing Scale

Costs rise with trace volume from verbose agent runs; monitor token/ingest usage early to avoid surprises in high-scale deployments.

Trust Breakdown

74

Trust scoreSolid

AGENT

Autonomous workflow delegation

TRUST

Transparency & verification

INTEROP

Protocol compatibility breadth

SECURITY

Security controls & audit trail

DOCS

Documentation completeness

How these scores are calculated →

What It Actually Does

In Plain English

Monitors AI application performance by tracking language model calls, latency, and errors across your infrastructure. Lets you debug model responses and catch cost overruns before they spike.

Mature enterprise observability platform with strong tracing/integrations and company trust, but limited direct API evidence lowers agent readiness.

Fit Assessment

Best for

✓observability
✓monitoring
✓llm-tracing

Not ideal for

✗automatic premium activation without user confirmation

Connection Patterns

Blueprints that include this tool:

Datadog LLM Observability + APM integration

datadog-llm-observability

→

Known Failure Modes

automatic premium activation without user confirmation

74

Datadog LLM Observability

Solid · 74/100

Visit Datadog LLM Observability

Score Breakdown

AGENT

Autonomous workflow delegation

TRUST

Transparency & verification

INTEROP

Protocol compatibility breadth

SECURITY

Security controls & audit trail

DOCS

Documentation completeness

Protocol Support

MCP—

A2A—

A2H—

REST API✓

Agent-callable—

Capabilities

Transaction capable—

ACP support—

Audit trace✓

Governance

audit-log
pii-masking
prompt-injection-detection

Pricing

Paid

$120/day automatic premium for LLM observability

Workflow Fit

observabilitymonitoringllm-tracing

Related Concepts

Browse full Lexicon →

Related Categories

Ready to evaluate Datadog LLM Observability in your stack?

N/A

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