Datadog LLM Observability Pricing & Plans 2026

Name: Datadog LLM Observability
Brand: Datadog LLM Observability
Price: 2.5 USD
Availability: InStock

Complete pricing guide for Datadog LLM Observability. Compare all plans, analyze costs, and find the perfect tier for your needs.

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💎2 Paid Plans

⚡No Setup Fees

Choose Your Plan

LLM Observability (Trace + Evaluations)

$2.50 per 1M indexed LLM spans for tracing; $1.50 per 1K evaluations executed. Requires a Datadog APM or Infrastructure subscription (from $15/host/month).

✓End-to-end traces for LLM and agent workflows
✓Built-in and custom evaluations
✓Cost and token tracking by model and tag
✓Integration with APM, Logs, and Infrastructure

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Datadog Platform Bundle

Custom enterprise contract; typical committed-use deals start around $18–$23/host/month for APM + Infrastructure, with LLM Observability span and evaluation charges bundled at volume-discounted rates (often 20–40% below on-demand list prices).

✓LLM Observability bundled with APM, Infrastructure, Logs, RUM
✓Cloud SIEM and Sensitive Data Scanner integration
✓Volume discounts and committed-use pricing
✓Enterprise SSO, audit logging, and dedicated support

Start Free Trial →

Pricing sourced from Datadog LLM Observability · Last verified March 2026

Feature Comparison

Features	LLM Observability (Trace + Evaluations)	Datadog Platform Bundle
End-to-end traces for LLM and agent workflows	✓	✓
Built-in and custom evaluations	✓	✓
Cost and token tracking by model and tag	✓	✓
Integration with APM, Logs, and Infrastructure	✓	✓
LLM Observability bundled with APM, Infrastructure, Logs, RUM	—	✓
Cloud SIEM and Sensitive Data Scanner integration	—	✓
Volume discounts and committed-use pricing	—	✓
Enterprise SSO, audit logging, and dedicated support	—	✓

Is Datadog LLM Observability Worth It?

✅ Why Choose Datadog LLM Observability

• Unifies LLM traces with APM, infrastructure, and log telemetry so a single distributed trace covers the full request path including model calls, tool use, and downstream services
• Built-in evaluations cover quality, faithfulness, toxicity, and topic relevance without requiring teams to wire up a separate evaluation framework
• Security detection for prompt injection and sensitive data leakage reuses Datadog's existing detection rules engine, which is unusual among LLM-specific observability vendors
• Cost and token tracking can be sliced by model, environment, user, or arbitrary custom tags and alerted on through the standard monitor system
• Enterprise foundations are already in place: SOC 2, HIPAA, FedRAMP, granular RBAC, audit logs, and SSO are inherited from the core platform
• Native support for multi-agent and agentic workflow tracing, including frameworks like LangChain, LlamaIndex, OpenAI Assistants, and custom orchestration

⚠️ Consider This

• Pricing is opaque and usage-based, with separate charges for ingested spans and evaluations that can become expensive for high-volume LLM applications
• The product is most valuable when paired with the rest of Datadog; teams not already on the platform inherit a heavy onboarding and contract footprint
• Open-source LLM observability tools like Langfuse and Arize Phoenix offer self-hosting options that Datadog does not, which can be a blocker for regulated or air-gapped environments
• The interface assumes familiarity with Datadog conventions (facets, tags, monitors), which has a steeper learning curve than purpose-built LLM-only tools
• Custom evaluators and prompt experimentation features are less mature than dedicated LLM platforms like LangSmith, with fewer prompt management and dataset workflows

What Users Say About Datadog LLM Observability

👍 What Users Love

✓Unifies LLM traces with APM, infrastructure, and log telemetry so a single distributed trace covers the full request path including model calls, tool use, and downstream services
✓Built-in evaluations cover quality, faithfulness, toxicity, and topic relevance without requiring teams to wire up a separate evaluation framework
✓Security detection for prompt injection and sensitive data leakage reuses Datadog's existing detection rules engine, which is unusual among LLM-specific observability vendors
✓Cost and token tracking can be sliced by model, environment, user, or arbitrary custom tags and alerted on through the standard monitor system
✓Enterprise foundations are already in place: SOC 2, HIPAA, FedRAMP, granular RBAC, audit logs, and SSO are inherited from the core platform
✓Native support for multi-agent and agentic workflow tracing, including frameworks like LangChain, LlamaIndex, OpenAI Assistants, and custom orchestration

👎 Common Concerns

⚠Pricing is opaque and usage-based, with separate charges for ingested spans and evaluations that can become expensive for high-volume LLM applications
⚠The product is most valuable when paired with the rest of Datadog; teams not already on the platform inherit a heavy onboarding and contract footprint
⚠Open-source LLM observability tools like Langfuse and Arize Phoenix offer self-hosting options that Datadog does not, which can be a blocker for regulated or air-gapped environments
⚠The interface assumes familiarity with Datadog conventions (facets, tags, monitors), which has a steeper learning curve than purpose-built LLM-only tools
⚠Custom evaluators and prompt experimentation features are less mature than dedicated LLM platforms like LangSmith, with fewer prompt management and dataset workflows

Pricing FAQ

How does Datadog LLM Observability differ from LangSmith or Langfuse?

LangSmith and Langfuse are purpose-built LLM platforms focused on prompt engineering, dataset management, and developer-centric evaluation workflows. Datadog LLM Observability is built for production operations: it stitches LLM spans into the same distributed traces as your infrastructure, APM, and logs, and reuses Datadog's monitor, alerting, RBAC, and security detection systems. It is stronger for SRE and platform teams running AI in production, weaker for prompt iteration during development.

Which LLM providers and frameworks does it support?

Datadog supports OpenAI, Anthropic, Amazon Bedrock, Azure OpenAI, Google Vertex AI, and other major providers, plus orchestration frameworks including LangChain, LlamaIndex, and OpenAI Assistants. Custom instrumentation is available through Datadog's SDKs for Python, Node.js, and other supported runtimes.

Can I self-host Datadog LLM Observability?

No. Datadog is a SaaS product and does not offer a self-hosted or on-prem version of LLM Observability. Teams with strict data residency requirements can choose between US, EU, and other regional Datadog sites, and sensitive data scrubbing can be applied client-side before telemetry is shipped.

How are evaluations performed?

Datadog offers built-in LLM-as-judge evaluations for quality, faithfulness, topic relevance, and toxicity, plus custom rule-based and code-based evaluators. Evaluations can run on sampled production traffic or on curated datasets, and results are stored alongside the trace so regressions are visible in the same UI as latency or cost spikes.

Does it detect prompt injection and PII leaks?

Yes. LLM Observability integrates with Datadog's Sensitive Data Scanner and detection rules engine to flag prompt injection attempts, jailbreaks, and PII or secrets that appear in prompts or responses. Findings can route to Datadog Cloud SIEM workflows for security teams to triage.

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More about Datadog LLM Observability

Review Alternatives Free vs Paid Pros & Cons Worth It?Tutorial

Compare Datadog LLM Observability Pricing with Alternatives

Langfuse Pricing

Langfuse is an open-source LLM observability and engineering platform providing tracing, prompt management, evaluations, and dataset management for production AI applications.

Compare Pricing →

Helicone Pricing

Open-source LLM observability and AI gateway — logs every prompt, response, cost, and latency across 20+ providers with a one-line proxy or async SDK, plus caching, retries, and prompt experiments.

Compare Pricing →

Arize Phoenix Pricing

Phoenix is Arize's open-source LLM observability project, and it has quietly become the default way tens of thousands of teams see what their agents are actually doing in production. The pitch is simple: `pip install arize-phoenix`, instrument with OpenInference (or any OpenTelemetry-compatible library), and every LLM call, tool invocation, retrieval, and embedding shows up as a spanned timeline you can filter, search, and replay. No vendor account required, no proprietary SDK lock-in. The Open

Compare Pricing →

LangSmith Pricing

LangSmith is LangChain's commercial observability, evaluation and prompt management platform for LLM apps and agents in production.

Compare Pricing →

Choose Your Plan

LLM Observability (Trace + Evaluations)

$2.50 per 1M indexed LLM spans for tracing; $1.50 per 1K evaluations executed. Requires a Datadog APM or Infrastructure subscription (from $15/host/month).

✓End-to-end traces for LLM and agent workflows
✓Built-in and custom evaluations
✓Cost and token tracking by model and tag
✓Integration with APM, Logs, and Infrastructure

Start Free Trial →

Datadog Platform Bundle

✓LLM Observability bundled with APM, Infrastructure, Logs, RUM
✓Cloud SIEM and Sensitive Data Scanner integration
✓Volume discounts and committed-use pricing
✓Enterprise SSO, audit logging, and dedicated support

Start Free Trial →

Pricing sourced from Datadog LLM Observability · Last verified March 2026

Feature Comparison

Features	LLM Observability (Trace + Evaluations)	Datadog Platform Bundle
End-to-end traces for LLM and agent workflows	✓	✓
Built-in and custom evaluations	✓	✓
Cost and token tracking by model and tag	✓	✓
Integration with APM, Logs, and Infrastructure	✓	✓
LLM Observability bundled with APM, Infrastructure, Logs, RUM	—	✓
Cloud SIEM and Sensitive Data Scanner integration	—	✓
Volume discounts and committed-use pricing	—	✓
Enterprise SSO, audit logging, and dedicated support	—	✓

Is Datadog LLM Observability Worth It?

✅ Why Choose Datadog LLM Observability

• Unifies LLM traces with APM, infrastructure, and log telemetry so a single distributed trace covers the full request path including model calls, tool use, and downstream services
• Built-in evaluations cover quality, faithfulness, toxicity, and topic relevance without requiring teams to wire up a separate evaluation framework
• Security detection for prompt injection and sensitive data leakage reuses Datadog's existing detection rules engine, which is unusual among LLM-specific observability vendors
• Cost and token tracking can be sliced by model, environment, user, or arbitrary custom tags and alerted on through the standard monitor system
• Enterprise foundations are already in place: SOC 2, HIPAA, FedRAMP, granular RBAC, audit logs, and SSO are inherited from the core platform
• Native support for multi-agent and agentic workflow tracing, including frameworks like LangChain, LlamaIndex, OpenAI Assistants, and custom orchestration

⚠️ Consider This

• Pricing is opaque and usage-based, with separate charges for ingested spans and evaluations that can become expensive for high-volume LLM applications
• The product is most valuable when paired with the rest of Datadog; teams not already on the platform inherit a heavy onboarding and contract footprint
• Open-source LLM observability tools like Langfuse and Arize Phoenix offer self-hosting options that Datadog does not, which can be a blocker for regulated or air-gapped environments
• The interface assumes familiarity with Datadog conventions (facets, tags, monitors), which has a steeper learning curve than purpose-built LLM-only tools
• Custom evaluators and prompt experimentation features are less mature than dedicated LLM platforms like LangSmith, with fewer prompt management and dataset workflows

What Users Say About Datadog LLM Observability

👍 What Users Love

✓Unifies LLM traces with APM, infrastructure, and log telemetry so a single distributed trace covers the full request path including model calls, tool use, and downstream services
✓Built-in evaluations cover quality, faithfulness, toxicity, and topic relevance without requiring teams to wire up a separate evaluation framework
✓Security detection for prompt injection and sensitive data leakage reuses Datadog's existing detection rules engine, which is unusual among LLM-specific observability vendors
✓Cost and token tracking can be sliced by model, environment, user, or arbitrary custom tags and alerted on through the standard monitor system
✓Enterprise foundations are already in place: SOC 2, HIPAA, FedRAMP, granular RBAC, audit logs, and SSO are inherited from the core platform
✓Native support for multi-agent and agentic workflow tracing, including frameworks like LangChain, LlamaIndex, OpenAI Assistants, and custom orchestration

👎 Common Concerns

⚠Pricing is opaque and usage-based, with separate charges for ingested spans and evaluations that can become expensive for high-volume LLM applications
⚠The product is most valuable when paired with the rest of Datadog; teams not already on the platform inherit a heavy onboarding and contract footprint
⚠Open-source LLM observability tools like Langfuse and Arize Phoenix offer self-hosting options that Datadog does not, which can be a blocker for regulated or air-gapped environments
⚠The interface assumes familiarity with Datadog conventions (facets, tags, monitors), which has a steeper learning curve than purpose-built LLM-only tools
⚠Custom evaluators and prompt experimentation features are less mature than dedicated LLM platforms like LangSmith, with fewer prompt management and dataset workflows