Datadog vs Grafana

Mahir Khan • Jan 26, 2025

Datadog and Grafana, two leading solutions in the observability space, take two different product approaches:

Datadog offers a fully managed SaaS platform with logs, metrics, traces, and security all integrated into one solution
Grafana offers an open-source visualization tool that connects with third party software like Prometheus (metrics), as well as its own tools such as Loki (logs) and Tempo (tracing), giving users the ability to create their own monitoring stack

But which one is the right choice? Is Datadog’s all-in-one platform worth the high price tag, or does Grafana’s flexible, open-source nature provide a better long-term investment? In this article, we’ll analyze the core features, pricing models, and trade-offs of the two platforms.

Different Approaches to Observability

From a high level, the key differences between Datadog and Grafana stem from how they center observability in their product.

What is Datadog’s Approach?

Datadog’s platform works as an all-in-one, fully managed tool that provides logs, metrics, traces, and security monitoring without the need to connect third-party data sources. Because Datadog is SaaS-only, all infrastructure management, scaling, and updates are handled by Datadog itself. However, pricing scales with data ingestion and monitored host count, making it quite expensive as teams grow.

What is Grafana’s Approach?

Grafana’s platform is an open-source observability platform created by Grafana Labs that focuses on visualization rather than data collection. Unlike Datadog, Grafana does not natively collect logs, metrics, or traces. Instead, it relies on complementary tools to do its monitoring for them. This modular approach gives teams more control over their monitoring stack, helping avoid vendor lock-in.

Convenience vs. Control

The choice often boils down to convenience vs control. Datadog is a suite of tools that make it a one-stop-shop for observability. Grafana enables developers to craft more bespoke observability set-ups, preserving more control over costs.

Core Features

Infrastructure Monitoring

How does Datadog handle Infrastructure Monitoring?

Datadog relies on lightweight agent processes that run on virtual machines (EC2, GCP VMs, Azure VMs), containers, and cloud services.

How Datadog works:

Datadog agents run as daemons on each host
Agents automatically detect running services and start collecting CPU, memory, disk I/O, network usage, and other infrastructure metrics
Native Cloud integrations also automatically detect cloud resources such as EC2 instances, Kubernetes clusters, and databases
Pre-configured dashboard screens display all of these metrics
AI-powered anomaly detection flags odd resource usages (such as a CPU spike)

What is an example of a Datadog deployment?

Datadog agent is deployed as a DaemonSet inside the Kubernetes cluster
Agent automatically detects and scrapes CPU, memory, and disk usage for each pod and node
Metrics are visualized in pre-built Kubernetes dashboards
If a pod spikes in CPU usage (or any other metric), Datadog automatically detects the anomaly and triggers an alert

How does Grafana handle Infrastructure Monitoring?

Grafana at its core is a visualization tool. To achieve the same level of monitoring as Datadog, Grafana queries external time-series data sources instead of using agents.

How Grafana Works:

Prometheus (opens in a new tab) (or another time-series database) collects infrastructure metrics
Grafana dashboards receive the metrics using PromQL (opens in a new tab) (Prometheus’s query language)
Grafana displays the data in visual dashboards, but does not auto-discover new services or cloud resources (these must be queried manually)

What is an example of a Grafana deployment?

Prometheus is installed inside a Kubernetes cluster
Prometheus scrapes metrics from Kubernetes nodes and pods at predefined intervals
Grafana queries Prometheus using PromQL to display CPU, memory, and disk usage
If a pod spikes in CPU usage, Alertmanager can be set up to trigger alerts (with manually set-up thresholds)

Which Platform is Better for Infrastructure Monitoring?

Datadog’s ready-to-go agent ecosystem and integrations make it an easy solution with minimal configuration required. Grafana is more flexible and cost-efficient, especially if you’re comfortable monitoring applications with Prometheus, but the platform also requires more engineering effort.

Log Management

How does log management work in Datadog?

Datadog provides fully indexed log storage, allowing for instant full-text search across all your logs. This means that you can search with any keyword, request ID, or error message.

How Datadog Works:

Datadog agent collects logs from from cloud services, applications, and infrastructure
All logs are indexed and stored in the cloud
Logs are automatically correlated with traces and metrics
Log rehydration (to re-index archived logs) is available, but comes at a high cost

How would Datadog handle an Increase in HTTP 400 Error Codes?

Datadog’s AI-driven anomaly detection flags the spike and triggers an alert
Engineer would click on the alert, which would bring up logs from the affected time period
A correlated trace could then be tracked to the exact service that caused this issue

How does log management work in Grafana?

Grafana uses Loki (opens in a new tab), an open-source log aggregation system that does not index log content but instead indexes metadata (labels). This significantly reduces the storage costs but requires structured querying (via LogQL) to retrieve the logs (rather than being able to search with any keyword). Regex-based searching is supported, but since Loki lacks full-text indexing, regex must parse the raw log content line by line, which can get really slow for large datasets.

How Grafana Loki Works:

Logs are stored in object storage (S3, GCS, MinIO) instead of indexed databases
Metadata-based indexing helps filter and speed up queries
LogQL (opens in a new tab) (Loki’s query language) is used to search for logs
Regex filtering can help, but full-text search is not supported

How would Grafana Loki Handle an increase in HTTP 400 Error Codes?

A basic threshold-based Grafana alert detects the spike and triggers an alert
Engineer clicks on the alert to open Loki logs from the affected time period
Since full text search is not available, the engineer would filter logs using metadata labels (app=backend, service=api-gateway, etc.)
LogQL query is used to refine search (can also use regex)
Engineer manually extracts the Trace ID from log entry and uses Grafana Tempo to track the root cause

Which tool offers better log management features?

Datadog’s fully indexed log databases gives teams the ability for quick, global search with any keyword and get one-click correlation between logs, traces, and other Datadog data. On the other hand, Grafana Loki’s metadata based indexing approach makes it significantly cheaper than Datadog, especially in large scale environments.

APM & Tracing

How Does Datadog Handle APM & Tracing?

Just like its logs, Datadog pre-indexes trace data for instant search and retrieval using any field (or keyword). This also allows for detailed end to end visibility across microservices, which can be shown in its generated service maps.

How Datadog Works:

Datadog agents automatically collect traces for every request
Traces are indexed (enabling an easy search by endpoint, user ID, or error type)
Service maps help visualize how services interact and how requests move through system
The trace waterfall view highlights error patterns in application performance, such as slow database queries
Engineers can click on a trace to get more details with logs and other metrics

How would Datadog debug a slow API response?

Datadog traces the request from frontend → API → database
The trace waterfall view reveals the API request and the SQL query causing the delay
Clicking on the trace shows the exact SQL query, confirming the bottleneck

How Does Grafana Handle APM & Tracing?

Grafana uses Tempo (opens in a new tab), a lightweight distributed tracing backend that relies on trace ID lookups or attributes instead of full-text search. Just like with Loki, this makes it much cheaper than Datadog’s indexed traces.

How Grafana Tempo Works:

Applications generate data using OpenTelemetry, Jaeger, or Zipkin
Traces are stored efficiently without indexing (can handle millions of spans per second)
Engineers can locate traces using trace IDs (found in log entries) or by query field searches on attributes (such as service name or http status code) via TraceQL (Tempo’s query language)
Once trace is found, the trace waterfall view shows the error

How would Grafana Tempo debug a slow API response?

Engineer checks logs in Loki to find an error log that includes a trace ID or relevant metadata
The trace ID is copied and queried in Tempo, which returns the full request flow (or can use TraceQL to search by attribute)
The trace waterfall view shows the API request that is causing the delay and confirms the slow response

How do Datadog and Grafana compare in terms of APM functionality and Tracing?

Datadog APM is fast with auto-indexed traces and detailed service maps. Grafana tempo is more cost-effective and scales more efficiently, but lacks the instant search that Datadog offers.

Security & Compliance

How does Datadog handle security and compliance?

Datadog provides native security monitoring tools that integrate directly with its infrastructure to detect security threats. Its list of tools include:

Security Information & Event Management (SIEM): Correlates logs, traces, and security events for real-time threat detection
Cloud Security Posture Management (CSPM): Scans AWS, GCP, and Azure configurations for compliance violations
Workload Security: Monitors container activity to detect file system changes, privilege escalations, and other suspicious behavior
Runtime Security Monitoring: Uses rules-based and anomaly-based detection to flag unauthorized access

How does Grafana handle security and compliance?

Because Grafana does not provide built-in security monitoring, teams must connect third-party security tools to meet compliance standards. Some integrations used are:

Falco: Detects runtime threats in Kubernetes such as unexpected file access or privilege escalations
OpenTelemetry Security signals: Captures security-related traces/logs
Grafana Loki + Prometheus Alertmanager: Basic security alerts, but lacks advanced anomaly detection

How do the two tools compare in security and compliance?

Datadog’s built in security monitoring (SIEM, CSPM, etc.) is great for having an all-in-one package for automated threat detection and compliance checks. Grafana’s self-hosted platform offers teams the chance to integrate open-source tools such as Falco and OpenTelemetry, which can be much cheaper, but also require more effort to set up.

Costs and Pricing Models

What is Datadog’s pricing?

Datadog uses a usage-based pricing model with costs scaling with the number of hosts, data ingestion, and retention. Datadog's pricing, in summary:

Charges per host (Infrastructure monitoring, APM): $15-$34 per host per month for infrastructure monitoring, $31-$45 per host per month for APM
Charges per GB of logs ingested and retained: $0.10 per GB for Datadog data volume + $2.50 per million log events with 30d retention
Additional fees for other advanced features such as machine learning anomaly detection and real-time data processing

What is Grafana’s pricing?

Grafana is on paper a cheaper alternative, with self-hosting being free, but requires effort to set up and maintain. Here are some typical costs with integrating other tools:

Log storage (via Loki): $0.50 per GB stored (30 day retention so no need to rehydrate)
Prometheus for Metrics: $0.08 per active series
APM (via Tempo) requires self-setup
Grafana Cloud: $19 per month for pro plan (which can include Grafana Cloud Adaptive Metrics)
Grafana Enterprise adds advanced support and features at extra cost

How do Datadog and Grafana compare in pricing?

Datadog’s fully managed observability platform can become costly due to its per host/data ingested model. Grafana can be much cheaper with its non-indexed log storage and self set-up. The tradeoff here is the effort to create and manage a monitoring environment to rival Datadog’s capabilities.

So which platform should you choose?

Ultimately, choosing between Datadog and Grafana comes down to your team’s needs. Choose Datadog if you need an all-in-one minimal setup observability platform, lack time to set up third party tools, and are okay with higher costs for ease of use. Choose Grafana if you want full control over your monitoring stack, are comfortable with managing Prometheus, Loki, and Tempo, and want to reduce observability costs.

We offer a third solution: HyperDX (opens in a new tab). HyperDX combines the bundling and detailing of Datadog with the open-source benefits of Grafana. HyperDX is also far more suitable for developers that care about logs with high-cardinality, capturing events at an intricate level.

Key Takeaways

Datadog: Great for Fully Managed Observability

Pros
- All-in-one observability suite with logs, metrics, tracing, and security monitoring that gathers all your data from each host via the Datadog agent
- AI-driven anomaly detection to detect spikes and error patterns across services
- Fast, full-text search on indexed logs with correlation to traces and metrics which can be received with Datadog queries
Cons
- High costs at scale, especially with its per-host pricing model that drives up usage fees as infrastructure grows
- Vendor lock-in since monitoring solution is all under one umbrella rather than having to connect external data sources

Grafana: Great for Cost-Effective, Customizable Monitoring

Pros
- Open source and self hosted with full control over your data and monitoring stack
- Allows integrations with many data sources (even including a Datadog data source plugin)
- Cheaper log storage (Loki) and scalable metric ingestion (Prometheus) help reduce overall usage costs
- Customizable dashboards and query editor supporting PromQL, LogQL, and Tempo for visualization
Cons
- High amount of engineering effort due to Prometheus, Loki, and Tempo having to be manually configured and maintained
- No built-in AI-driven alerts or full-text log search