Datadog Cloud Cost Management
Product · SaaS / cloud software · Finance & accounting by Datadog
Unified observability for cloud, SaaS, and AI spend
Datadog Cloud Cost Management is a SaaS observability platform that unifies cloud, SaaS, and AI spend with technical performance data. It provides deep visibility into costs across major cloud providers and AI platforms, correlating cost spikes with infrastructure changes. The platform offers automated cost recommendations, granular cost allocation for containers and GPUs, and AI-driven root cause analysis.
- Price
- Pricing on request
Category: Observability & Monitoring
- Deployment
- Cloud
- API
- Available
- Unified observability for cloud, SaaS, and AI spend
- Automated cost recommendations for AWS, Azure, GCP, OpenAI, and Anthropic
- Granular container cost allocation including GPU and network costs
- AI-powered root cause analysis for cost spikes via Bits Chat
- Standardized reporting using the FinOps FOCUS format
Overview
Datadog Cloud Cost Management is a SaaS-based observability platform that unifies cloud, SaaS, and AI spend with technical performance data. Designed for engineering and FinOps teams, it provides deep visibility into costs across AWS, Azure, Google Cloud, and OCI, as well as AI providers like OpenAI and Anthropic. The platform solves the problem of disconnected cost and performance data by allowing users to correlate cost spikes directly with infrastructure changes or application metrics. It features automated cost recommendations, granular container and GPU cost allocation, and AI-driven root cause analysis through Bits Chat. By integrating with the broader Datadog ecosystem, it enables teams to optimize workloads and reduce waste through a single, unified interface.
Cost Observability and AI Spend
- Granular AI Attribution: The platform automatically attributes AI costs down to the specific user, project, or model.
Optimization and Remediation
- Automated Recommendations: Datadog provides cost recommendations to optimize spend for OpenAI, Anthropic, AWS, Azure, and Google Cloud.
- Cost Optimization Automations: The platform continuously optimizes cloud spend through Cost Optimization Automations.
- Commitment Management: Teams can manage commitment coverage and utilization to maximize savings and reduce waste.
Allocation and Attribution
- Custom Allocation Rules: Tag Pipelines and Custom Allocation Rules ensure accurate attribution of all costs across shared resources.
Collaboration and Governance
- Anomaly Detection and Alerting: Teams can alert on cost anomalies with monitors, detection, and root cause analysis.
- Reporting and Exporting: Any cost query can be transformed into a CSV or captured in a Notebook for quick collaboration.
Highlights
- Unified observability for cloud, SaaS, and AI spend
- Automated cost recommendations for AWS, Azure, GCP, OpenAI, and Anthropic
- Granular container cost allocation including GPU and network costs
- AI-powered root cause analysis for cost spikes via Bits Chat
- Standardized reporting using the FinOps FOCUS format
Use cases
- FinOps teams use the platform to allocate shared container and GPU costs across different business units.
- Engineers investigate sudden cloud cost spikes by correlating them with real-time infrastructure changes and application traces.
- Organizations monitor and optimize their AI ROI by tracking spend per model and project across multiple AI providers.
- Finance teams use Notebooks to collaborate with engineering on long-term cost optimization and budget planning.
How it works
- Connect Data Sources: Connect your cloud accounts (AWS, Azure, Google Cloud, or OCI) and AI providers using Datadog's built-in integrations.
- Correlate Cost and Performance: Datadog automatically ingests and correlates cost data with infrastructure performance and application telemetry.
- Configure Allocation Rules: Use Tag Pipelines and Custom Allocation Rules to attribute spend to specific teams, services, or projects.
- Optimize and Remediate: Review automated recommendations and use Bits AI to investigate anomalies or generate remediation code.
Specifications
- Deployment: Cloud-based SaaS platform
- Platforms: Web, API, Mobile App (iOS, Android)
- Integrations: AWS, Azure, Google Cloud, OCI, OpenAI, Anthropic, Snowflake, Databricks
- APIs and SDKs: HTTP REST API with official client libraries for Java, Python, Ruby, Go, and Node.js
- Sign-in and administration: Role-based access control and Governance Console for organization-wide policies
- Supported Clouds: AWS, Azure, Google Cloud, OCI
- Supported AI Providers: OpenAI, Anthropic
- Data Standard: FinOps FOCUS format
- Metric Resolution: Daily trends and real-time anomalies
- Allocation Granularity: User, Project, Model, Service, or Team level
- Container Metrics: GPU, Data Transfer, Network costs
- Automation Tools: Cost Optimization Automations, Bits Code fix generation
- Access Methods: Web UI, REST API, Mobile App
FAQ
Which cloud providers are supported?
Datadog Cloud Cost Management supports AWS, Azure, Google Cloud, and Oracle Cloud Infrastructure (OCI).
Can I monitor AI-specific costs?
Yes, it provides visibility into spend for AI providers including OpenAI and Anthropic, with the ability to attribute costs down to the user, project, or model.
How does it help with cost optimization?
The platform provides automated cost recommendations and can even generate the necessary code for fixes using Bits Code.
Does it support container-level cost attribution?
It supports granular container cost allocation, including specific tracking for GPU, data transfer, and network costs.
Added by AntSpace from public sources (datadoghq.com).