8 Datadog alternatives (Including more cost effective options for DevOps teams)

If you're here, you're likely feeling the sting of a growing Datadog bill—or maybe you're seeking more granular control over your telemetry data. Datadog has long been a go-to in observability, but its pricing model, cardinality challenges, and rigid telemetry management often leave DevOps and SRE teams searching for alternatives. In this guide, we break down eight powerful Datadog alternatives, including cheaper platforms, open-source tools, and next-gen observability solutions.

But switching isn’t the only option. If you want to stick with Datadog, there's still a powerful way to improve your observability ROI: smart telemetry management. Platforms like Sawmills sit upstream of your observability tools, giving you control over what data gets ingested—and how much you pay.

Why DevOps Teams Look for Datadog Alternatives

• High and unpredictable cost: Datadog's usage-based pricing can lead to surprise bills.
• Cardinality issues: High-cardinality metrics can quickly balloon both storage and cost.
• Lack of telemetry control: Limited pre-processing and enrichment before ingestion.
• Vendor lock-in: Proprietary agents and dashboards restrict flexibility.
• Emergence of smarter, AI-powered tools: Teams want tools that do more with less.

Top Datadog Alternatives to Know About

1. Grafana Cloud

Grafana Cloud is a fully managed observability platform built on open-source tools like Grafana, Loki, Tempo, and Mimir. It’s an excellent fit for teams who value open standards and powerful visualization capabilities. DevOps teams frequently use it to unify logs, metrics, and traces in a single place while maintaining strong integration with Prometheus-based monitoring.

Grafana Cloud excels in environments where you already have Prometheus exporters and dashboards, allowing for an easy transition without losing past investment. Its tiered pricing makes it appealing to startups and SMBs who want to scale gradually. The native support for OpenTelemetry and long-term roadmap centered around open source give it a modern edge over proprietary alternatives.

Pros:

• Built on open-source stack
• Transparent pricing tiers
• Strong visualization and alerting
• Native support for OTEL

Cons:

• Steeper learning curve for setup
• Log volume pricing can still scale quickly

Cheaper than Datadog? Usually, especially in smaller deployments.

2. Prometheus + Grafana (Self-Hosted)

For teams that want full control over their monitoring infrastructure, the Prometheus and Grafana combo is a go-to solution. This stack is particularly attractive to engineering teams with Kubernetes-heavy workloads and in-house expertise. Prometheus handles metrics collection with a pull-based model and integrates seamlessly with Grafana for dashboards.

Self-hosting this duo gives organizations complete control over retention, security, and performance tuning. However, the trade-off is operational overhead—you’ll need to provision and manage the infrastructure, upgrade software, and tune configurations. Still, for teams that prioritize cost control and customization, this is a mature and battle-tested choice.

Pros:

• No licensing costs
• Massive community support
• Customizable to the core

Cons:

• No built-in logging or tracing
• Requires manual maintenance and scaling

Cheaper than Datadog? Yes, if you can manage the operational overhead.

3. New Relic

New Relic has undergone a reinvention in recent years, moving toward a more generous pricing model with a usage-based structure and a strong free tier. Known for its comprehensive APM capabilities, it now covers metrics, events, logs, and traces (MELT) under a unified experience.

What sets New Relic apart is its NRQL query language and the unified entity-centric view of your systems. Many DevOps teams use it to correlate application-level insights with infrastructure-level telemetry, accelerating root cause analysis. The platform offers an intuitive setup experience and rich pre-built dashboards, making it easier to onboard cross-functional teams.

Pros:

• Generous free tier
• Query everything with NRQL
• Strong APM and tracing support

Cons:

• Can become expensive at scale
• Interface has a learning curve

Cheaper than Datadog? In low-to-moderate usage tiers.

4. Splunk Observability

Splunk Observability Cloud extends the company’s log analytics expertise into full MELT observability. It provides real-time streaming analytics and automatic metric correlation, making it valuable for enterprises with high performance and compliance demands.

Splunk is especially strong in environments where log analysis is central to the team’s workflows. Many organizations already using Splunk Enterprise for security or compliance reporting expand into observability with minimal disruption. However, the learning curve and cost make it better suited for well-staffed engineering teams.

Pros:

• Advanced analytics engine
• Rich ecosystem of integrations
• Long-standing enterprise credibility

Cons:

• Expensive at scale
• Can be complex to administer

Cheaper than Datadog? Not typically.

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5. Dynatrace

Dynatrace differentiates itself with deep automation and AI-driven observability. Its Davis AI engine continuously evaluates telemetry to surface anomalies and recommend remediation. This platform is ideal for large enterprises with complex microservices environments and high SLAs.

DevOps teams value Dynatrace for its auto-discovery of dependencies and infrastructure, reducing the need for manual configuration. It offers a full-stack perspective, integrating user experience, network, infrastructure, and application-level insights into a cohesive view. While powerful, Dynatrace's licensing model and setup complexity require dedicated team resources.

Pros:

• Davis AI for proactive issue detection
• Auto-discovery of services and dependencies
• High data granularity

Cons:

• Steep learning curve
• Less customizable UI

Cheaper than Datadog? Depends on usage, often similar pricing.

6. Honeycomb

Honeycomb stands out for its unique approach to observability, especially for debugging high-cardinality, distributed systems. Instead of aggregating logs and metrics in traditional ways, Honeycomb emphasizes event-driven observability, enabling you to ask exploratory questions about your system's behavior.

It shines in environments where tracing and understanding user behavior across services is critical. For instance, DevOps teams at consumer-facing SaaS platforms often use Honeycomb to investigate customer-impacting latency or pinpoint which services introduce delays. It’s also a favorite among teams practicing observability-driven development.

Pros:

• Extremely granular observability
• Unique event-based approach
• Great for debugging distributed systems

Cons:

• Pricing based on events, not volume
• Can get pricey in trace-heavy environments

Cheaper than Datadog? Sometimes, depending on telemetry shape.

7. Elastic Observability (ELK Stack)

Elastic Observability is an extension of the widely used ELK (Elasticsearch, Logstash, Kibana) stack. It enables unified analysis of logs, metrics, and APM traces under a single platform. It's ideal for teams already using Elasticsearch for log search and looking to consolidate monitoring.

Elastic’s strength lies in its flexibility and full-stack control. DevOps teams can build tailored dashboards, apply fine-grained access controls, and integrate data from diverse sources. However, tuning the stack for large-scale performance and maintaining upgrades can become a time investment. For regulated industries or teams focused on cost control, the self-managed model offers significant appeal.

Pros:

• Full stack control
• Powerful search and dashboards
• Rich plugin ecosystem

Cons:

• Requires tuning for performance
• Upgrades can be painful

Cheaper than Datadog? Yes, especially for in-house deployments.

8. Lightstep by ServiceNow

Lightstep focuses on change intelligence and tracing at scale, making it especially useful in environments where deployments happen frequently and service-level accountability is key. The platform enables teams to correlate deploys, configuration changes, and user impact in real time.

Because it’s natively built on OpenTelemetry, Lightstep integrates well into modern cloud-native workflows. DevOps engineers use it to reduce MTTR by quickly identifying what changed, when, and where. While not a full replacement for all observability tooling, it's an ideal choice for teams that need deep tracing and change impact analysis across microservices.

Pros:

• Change intelligence for fast incident response
• OpenTelemetry native
• Strong tracing visibility

Cons:

• More niche, not a full replacement for all observability types

Cheaper than Datadog? Potentially, for trace-focused use cases.

How to Choose the Right Datadog Alternative

• Understand your telemetry shape: High cardinality or volume? Prioritize platforms that manage volume early like Sawmills.
• Cost predictability: Look for transparent or flat-rate pricing models.
• AI vs. DIY: Decide if your team benefits more from AI-recommendations or fully manual setups.
• Integration requirements: Consider what tools need to be integrated and how easily.

Conclusion

Choosing a Datadog alternative isn't just about saving money, it's about gaining control. From open-source frameworks to AI-powered platforms, there's an observability tool that fits your team’s scale, cost sensitivity, and telemetry needs.

But switching may not be necessary. Sawmills empowers teams to stay on Datadog while dramatically improving cost-efficiency, signal-to-noise ratio, and compliance. By managing telemetry at the source, Sawmills gives DevOps teams the power to enforce policies, eliminate low-value data, and optimize their entire observability pipeline.

Ready to take control of your telemetry? Schedule a Sawmills demo today and unlock smarter observability workflows without needing to rip and replace.