Datadog LLM Observability Doesn't Have a Free Plan — Here's What It Costs

⚡ Quick Verdict

No free plan. The cheapest way in is LLM Observability (Trace + Evaluations) at $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).. Consider free alternatives in the analytics & monitoring category if budget is tight.

See Pricing →See Plans ↓

Who Should Pay for This

👤

Best For

✓Established business
✓Budget for premium tools
✓Need analytics & monitoring features
✓Professional use case
✓Want official support

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

🆓 Free Alternatives to Datadog LLM Observability

→ Langfuse

Free plan includes: full feature parity with cloud version, unlimited traces, users, and data retention, complete control over data and infrastructure

Free PlanCompare →

→ Helicone

Free plan includes: 10,000 requests per month, full dashboard access, cost analytics & request logging

Free PlanCompare →

→ Arize Phoenix

Free plan includes: basic features

Free PlanCompare →

Frequently Asked Questions

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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See Datadog LLM Observability plans and find the right tier for your needs.

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More about Datadog LLM Observability

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📖 Datadog LLM Observability Overview 💰 Datadog LLM Observability Pricing & Plans ⚖️ Is Datadog LLM Observability Worth It?🔄 Compare Datadog LLM Observability Alternatives

Last verified March 2026

Datadog LLM Observability Doesn't Have a Free Plan — Here's What It Costs

⚡ Quick Verdict

See Pricing →See Plans ↓

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