Datadog Code Security

Name: Datadog Code Security
Brand: Datadog
Rating: 4.5 (18 reviews)

Leader

Datadog provides cloud-scale monitoring and security for metrics, traces and logs in one unified platform.

Est 2010|Valuation $91.4B|Raised $795.9M

LinkedIn 546K|Website|Web traffic 4.6M|FTEs 8,100|HQ New York, New York, US

MarketsApplication Security Testing (AST)Cloud-Native Application Protection Platform (CNAPP)Code Security

01Competitive Landscape

Narrative competitive analysis

Where Datadog Code Security is expected to be competitive: - Organizations that already use Datadog telemetry can prioritize vulnerabilities based on production reality rather than static severity alone. - Teams that prefer a single operational console for code risk, runtime context, ownership, and ticketing may value consolidation. Where competitors may be preferred: - Best-of-breed AppSec vendors may offer deeper language/framework coverage, more mature policy engines, and longer product maturity in AppSec-specific areas. - Vendors with built-in economics for code scanning (for example, GitHub Advanced Security where organizations already have enterprise GitHub licensing) can be cost-effective for code scanning and secret scanning. - Tools with strong custom-rule ecosystems (Semgrep) or broad artifact coverage (Snyk) may be preferred where observability context is less critical.

	Datadog Code Security	Snyk	Semgrep
Representative starting price	Published per-committer and per-host pricing available from the vendor; separate SAST/SCA, bundle, and runtime pricing tiers are published	Public plans; enterprise pricing often custom; pricing models commonly based on developers/tests or similar units	Team pricing varies; contributor-based pricing commonly referenced in vendor materials and secondary explainers
Typical fit	Organizations already invested in Datadog wanting combined runtime and code scanning workflows	Teams prioritizing developer-first scanning across SCA/SAST/IaC/containers and broad ecosystem integrations	Teams prioritizing customizable SAST rules, CLI/CI workflows, and developer-centric scanning
Key strengths	Runtime-informed prioritization and platform consolidation of telemetry and security findings	Broad AppSec feature breadth and mature integrations	Large rule ecosystem and focus on custom rules and developer workflows
Key weaknesses	Potential for high cost depending on platform adoption and preview/regional availability considerations	Cost and pricing clarity at scale can be an issue for some buyers	Total cost can increase with scale; packaging can be complex across offerings

01Competitive Landscape

Narrative competitive analysis

Where Datadog Code Security is expected to be competitive:

Organizations that already use Datadog telemetry can prioritize vulnerabilities based on production reality rather than static severity alone.
Teams that prefer a single operational console for code risk, runtime context, ownership, and ticketing may value consolidation.

Where competitors may be preferred:

Best-of-breed AppSec vendors may offer deeper language/framework coverage, more mature policy engines, and longer product maturity in AppSec-specific areas.
Vendors with built-in economics for code scanning (for example, GitHub Advanced Security where organizations already have enterprise GitHub licensing) can be cost-effective for code scanning and secret scanning.
Tools with strong custom-rule ecosystems (Semgrep) or broad artifact coverage (Snyk) may be preferred where observability context is less critical.

	Datadog Code Security	Snyk	Semgrep
Representative starting price	Published per-committer and per-host pricing available from the vendor; separate SAST/SCA, bundle, and runtime pricing tiers are published	Public plans; enterprise pricing often custom; pricing models commonly based on developers/tests or similar units	Team pricing varies; contributor-based pricing commonly referenced in vendor materials and secondary explainers
Typical fit	Organizations already invested in Datadog wanting combined runtime and code scanning workflows	Teams prioritizing developer-first scanning across SCA/SAST/IaC/containers and broad ecosystem integrations	Teams prioritizing customizable SAST rules, CLI/CI workflows, and developer-centric scanning
Key strengths	Runtime-informed prioritization and platform consolidation of telemetry and security findings	Broad AppSec feature breadth and mature integrations	Large rule ecosystem and focus on custom rules and developer workflows
Key weaknesses	Potential for high cost depending on platform adoption and preview/regional availability considerations	Cost and pricing clarity at scale can be an issue for some buyers	Total cost can increase with scale; packaging can be complex across offerings