Datadog Pricing & Plans 2026

Name: Datadog
Brand: Datadog
Availability: InStock

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

Not sure if free is enough? See our Free vs Paid comparison →
Still deciding? Read our full verdict on whether Datadog is worth it →

🆓Free Tier Available

💎5 Paid Plans

⚡No Setup Fees

Choose Your Plan

Free

Start Free Trial →

Pro (Infrastructure)

From $15/host/month (annual)

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Enterprise (Infrastructure)

From $23/host/month (annual)

Start Free Trial →

APM and Add-on Products

Modular, e.g. APM from ~$31/host/month, Logs from $0.10/GB ingested

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Custom / Volume

Contact sales

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Pricing sourced from Datadog · Last verified March 2026

Feature Comparison

Detailed feature comparison coming soon. Visit Datadog's website for complete plan details.

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Is Datadog Worth It?

✅ Why Choose Datadog

• Unified platform spanning infrastructure, APM, logs, RUM, synthetics, network, security, and LLM observability—reducing the need for multiple vendors and enabling cross-signal correlation in a single UI.
• Massive integration catalog (800+) with first-class support for AWS, Azure, GCP, Kubernetes, and AI providers like OpenAI, Anthropic, and Bedrock, making onboarding fast for typical cloud stacks.
• Strong APM and distributed tracing with flame graphs, trace search, and code-level visibility, including continuous profiler that pinpoints CPU and memory hotspots in production.
• First-class LLM Observability product that captures prompts, completions, token cost, latency, and quality signals for AI agents and RAG pipelines—rare among legacy observability vendors.
• Mature alerting, anomaly detection, and SLO tooling, plus Bits AI for natural-language querying, incident summaries, and root cause suggestions across telemetry.
• Enterprise-grade compliance (SOC 2, ISO 27001, HIPAA, PCI, FedRAMP) and regional data residency options suitable for regulated industries.

⚠️ Consider This

• Pricing is notoriously expensive and complex—each module is billed separately by host, ingested GB, indexed events, or sessions, and costs can scale unpredictably with traffic spikes or high-cardinality tags.
• The breadth of products creates a steep learning curve; new users often struggle to navigate dashboards, monitors, log indexes, and the differences between metrics, traces, and logs pricing.
• Custom metrics and high-cardinality tagging can drive surprise overage bills, requiring active cost governance and tag policy management.
• Some advanced features (Cloud SIEM, ASM, Database Monitoring, LLM Observability) are gated to higher tiers or sold as separate SKUs, leading to bundle bloat for teams that need many capabilities.
• Outbound data egress and long-term log retention are limited compared to dedicated log warehouses; teams with heavy compliance retention often pair Datadog with cheaper archive storage.

What Users Say About Datadog

👍 What Users Love

✓Unified platform spanning infrastructure, APM, logs, RUM, synthetics, network, security, and LLM observability—reducing the need for multiple vendors and enabling cross-signal correlation in a single UI.
✓Massive integration catalog (800+) with first-class support for AWS, Azure, GCP, Kubernetes, and AI providers like OpenAI, Anthropic, and Bedrock, making onboarding fast for typical cloud stacks.
✓Strong APM and distributed tracing with flame graphs, trace search, and code-level visibility, including continuous profiler that pinpoints CPU and memory hotspots in production.
✓First-class LLM Observability product that captures prompts, completions, token cost, latency, and quality signals for AI agents and RAG pipelines—rare among legacy observability vendors.
✓Mature alerting, anomaly detection, and SLO tooling, plus Bits AI for natural-language querying, incident summaries, and root cause suggestions across telemetry.
✓Enterprise-grade compliance (SOC 2, ISO 27001, HIPAA, PCI, FedRAMP) and regional data residency options suitable for regulated industries.

👎 Common Concerns

⚠Pricing is notoriously expensive and complex—each module is billed separately by host, ingested GB, indexed events, or sessions, and costs can scale unpredictably with traffic spikes or high-cardinality tags.
⚠The breadth of products creates a steep learning curve; new users often struggle to navigate dashboards, monitors, log indexes, and the differences between metrics, traces, and logs pricing.
⚠Custom metrics and high-cardinality tagging can drive surprise overage bills, requiring active cost governance and tag policy management.
⚠Some advanced features (Cloud SIEM, ASM, Database Monitoring, LLM Observability) are gated to higher tiers or sold as separate SKUs, leading to bundle bloat for teams that need many capabilities.
⚠Outbound data egress and long-term log retention are limited compared to dedicated log warehouses; teams with heavy compliance retention often pair Datadog with cheaper archive storage.

Pricing FAQ

What does Datadog actually monitor?

Datadog monitors infrastructure (servers, containers, Kubernetes, cloud services), applications (via APM and distributed tracing), logs, real user sessions, synthetic tests, network flows, databases, security posture and threats, and AI/LLM workloads. All signals live in one platform and can be correlated together.

How is Datadog priced?

Datadog uses modular pricing: each product (Infrastructure, APM, Logs, RUM, Synthetics, Security, LLM Observability, etc.) is billed separately. Common units include per-host per-month, per ingested or indexed GB of logs, per million APM spans, and per session. Volume discounts and annual commitments are available, but many teams find costs grow quickly without active governance.

Does Datadog support monitoring AI and LLM applications?

Yes. Datadog LLM Observability traces prompts, completions, tool calls, token usage, latency, and cost across LLM and agent pipelines, and integrates with providers like OpenAI, Anthropic, AWS Bedrock, and frameworks such as LangChain and LlamaIndex. It also offers evaluations for quality, safety, and hallucinations.

How does Datadog compare to open-source observability stacks?

Open-source stacks (Prometheus, Grafana, Loki, OpenTelemetry, Jaeger) can match many of Datadog's features but require self-hosting, scaling, and integration work. Datadog trades higher cost for a fully managed, integrated experience with cross-signal correlation, enterprise security, and turnkey integrations. Datadog also natively ingests OpenTelemetry data.

Is Datadog suitable for small teams or startups?

Datadog has a free tier for basic infrastructure monitoring of up to five hosts, and startups can use the platform productively. However, pricing scales aggressively with hosts, log volume, and custom metrics, so small teams should monitor usage carefully or consider lighter-weight alternatives until scale justifies the cost.

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Datadog Pricing & Plans 2026

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

🆓Free Tier Available

💎5 Paid Plans

⚡No Setup Fees

Choose Your Plan

Free

Start Free Trial →

Pro (Infrastructure)

From $15/host/month (annual)

Start Free Trial →

Enterprise (Infrastructure)

From $23/host/month (annual)

Start Free Trial →

APM and Add-on Products

Modular, e.g. APM from ~$31/host/month, Logs from $0.10/GB ingested

Start Free Trial →

Custom / Volume

Contact sales

Start Free Trial →

Pricing sourced from Datadog · Last verified March 2026

Is Datadog Worth It?

✅ Why Choose Datadog

• Unified platform spanning infrastructure, APM, logs, RUM, synthetics, network, security, and LLM observability—reducing the need for multiple vendors and enabling cross-signal correlation in a single UI.
• Massive integration catalog (800+) with first-class support for AWS, Azure, GCP, Kubernetes, and AI providers like OpenAI, Anthropic, and Bedrock, making onboarding fast for typical cloud stacks.
• Strong APM and distributed tracing with flame graphs, trace search, and code-level visibility, including continuous profiler that pinpoints CPU and memory hotspots in production.
• First-class LLM Observability product that captures prompts, completions, token cost, latency, and quality signals for AI agents and RAG pipelines—rare among legacy observability vendors.
• Mature alerting, anomaly detection, and SLO tooling, plus Bits AI for natural-language querying, incident summaries, and root cause suggestions across telemetry.
• Enterprise-grade compliance (SOC 2, ISO 27001, HIPAA, PCI, FedRAMP) and regional data residency options suitable for regulated industries.

⚠️ Consider This

• Pricing is notoriously expensive and complex—each module is billed separately by host, ingested GB, indexed events, or sessions, and costs can scale unpredictably with traffic spikes or high-cardinality tags.
• The breadth of products creates a steep learning curve; new users often struggle to navigate dashboards, monitors, log indexes, and the differences between metrics, traces, and logs pricing.
• Custom metrics and high-cardinality tagging can drive surprise overage bills, requiring active cost governance and tag policy management.
• Some advanced features (Cloud SIEM, ASM, Database Monitoring, LLM Observability) are gated to higher tiers or sold as separate SKUs, leading to bundle bloat for teams that need many capabilities.
• Outbound data egress and long-term log retention are limited compared to dedicated log warehouses; teams with heavy compliance retention often pair Datadog with cheaper archive storage.

What Users Say About Datadog

👍 What Users Love

✓Unified platform spanning infrastructure, APM, logs, RUM, synthetics, network, security, and LLM observability—reducing the need for multiple vendors and enabling cross-signal correlation in a single UI.
✓Massive integration catalog (800+) with first-class support for AWS, Azure, GCP, Kubernetes, and AI providers like OpenAI, Anthropic, and Bedrock, making onboarding fast for typical cloud stacks.
✓Strong APM and distributed tracing with flame graphs, trace search, and code-level visibility, including continuous profiler that pinpoints CPU and memory hotspots in production.
✓First-class LLM Observability product that captures prompts, completions, token cost, latency, and quality signals for AI agents and RAG pipelines—rare among legacy observability vendors.
✓Mature alerting, anomaly detection, and SLO tooling, plus Bits AI for natural-language querying, incident summaries, and root cause suggestions across telemetry.
✓Enterprise-grade compliance (SOC 2, ISO 27001, HIPAA, PCI, FedRAMP) and regional data residency options suitable for regulated industries.

👎 Common Concerns

⚠Pricing is notoriously expensive and complex—each module is billed separately by host, ingested GB, indexed events, or sessions, and costs can scale unpredictably with traffic spikes or high-cardinality tags.
⚠The breadth of products creates a steep learning curve; new users often struggle to navigate dashboards, monitors, log indexes, and the differences between metrics, traces, and logs pricing.
⚠Custom metrics and high-cardinality tagging can drive surprise overage bills, requiring active cost governance and tag policy management.
⚠Some advanced features (Cloud SIEM, ASM, Database Monitoring, LLM Observability) are gated to higher tiers or sold as separate SKUs, leading to bundle bloat for teams that need many capabilities.
⚠Outbound data egress and long-term log retention are limited compared to dedicated log warehouses; teams with heavy compliance retention often pair Datadog with cheaper archive storage.