Datadog vs. Splunk: Which Fits Your Observability Stack in 2026?

Engineering teams rarely choose between Datadog and Splunk. Most end up running both: one for infrastructure and application monitoring, the other for logs and security analytics.

That split feels normal until an incident hits. Your APM dashboard flags a latency spike in Datadog. Your logs live in Splunk. Now someone is manually stitching together two timelines from two tools, while the meter runs on both invoices.

The real question for 2026 isn't which platform wins outright. It's whether keeping both is still worth the duplicated data, duplicated licenses, and slower root cause work every time something breaks.

Datadog vs. Splunk at a glance

Both platforms are strong at what they were built for. The friction shows up when a team needs both at once.

Core use case differences

Datadog started as an infrastructure and application monitoring tool and built outward from there. Its strengths sit in:

• Cloud-native infrastructure monitoring across containers, Kubernetes, and serverless workloads
• Application performance monitoring (APM) with distributed tracing
• Real user monitoring (RUM) for front-end performance

Splunk started on the opposite end. Its strengths are:

• Searching massive volumes of structured and unstructured log data
• Security information and event management (SIEM), including compliance and threat detection
• On-premises deployments where data residency or legacy systems matter

Splunk does sell infrastructure monitoring and APM through Splunk Observability Cloud, but that's a separate product with separate pricing from the Splunk Platform most teams run for logs and security. In practice, neither vendor's core product covers the other's core job well. That gap, not vendor preference, is why so many teams run both platforms side by side.

In practice, this usually maps to team structure. A platform engineering or SRE team tends to reach for Datadog first for its custom dashboards and design centered on infrastructure and application health. A security or IT operations team, especially one handling compliance requirements, tends to standardize on Splunk regardless of what the rest of engineering uses for monitoring. 

Both decisions are reasonable in isolation. The friction shows up once someone asks why the company is paying for two full platforms that only partially overlap.

Log management and search capabilities

Splunk's Search Processing Language (SPL) is built for deep, ad hoc investigation across large log volumes. Security and compliance teams rely on it for correlation rules, threat hunting, and retention requirements that log tools built for infrastructure monitoring weren't designed to handle.

Datadog's log search and pipelines are built to plug directly into the same platform as its infrastructure and APM data. That makes it easier to pivot from a log line to the trace or host that produced it without leaving the tool. What it doesn't offer is Splunk's depth of search language or its security-specific tooling.

A security team investigating a suspicious login benefits from Splunk's ability to correlate authentication logs, network events, and historical baselines in one search. An engineer debugging a slow API endpoint benefits more from Datadog's ease of use, surfacing the exact trace and host tied to a spike in log volume without the learning curve of a separate query language.

If your primary log use case is security investigation, Splunk still leads. If your main goal is correlating logs with the rest of your observability data during an incident, Datadog's integration is the bigger advantage. Running both means paying for depth you don't always need on one side and giving up integration on the other.

Real-time monitoring and alerting

Datadog's alerting and automation are built around the same data it already collects: infrastructure metrics, APM traces, and RUM sessions. Alerts can trigger off anomaly detection, forecasts, or static thresholds, and they route directly into on-call workflows tied to that telemetry.

Splunk's alerting works differently. It's built on top of log and event data, which suits security use cases like flagging unusual login patterns or policy violations. For infrastructure-level incidents, alerts in the core Splunk Platform often depend on data piped in from somewhere else, which adds a step compared to a tool built around that data natively.

Consider a checkout service that starts throwing intermittent 500 errors. In Datadog, an anomaly alert fires against the error-rate metric, and an engineer can jump straight from that alert to the trace that caused it. In Splunk, the same failure might only surface once someone thinks to search the application logs for that error pattern since the alert wasn't built around infrastructure telemetry in the first place. Neither approach is wrong. They're just optimized for different starting points.

That means teams running both tools end up building parallel alerting logic: one set of rules in Datadog for performance and availability, another in Splunk for security and log-based anomalies. When an incident touches both categories, someone must manually connect the two alert streams.

Pricing models and total cost

Splunk offers pricing tied to data ingestion volume, search activity, or workload for its core platform. Splunk's pricing page lists activity-based, ingest-based, and workload-based options for the Splunk Cloud Platform, plus entity-based Splunk Observability Cloud plans starting at $15 per host per month for infrastructure monitoring when billed annually. 

Datadog charges per host for infrastructure monitoring and APM, with logs priced separately by volume. Datadog's pricing page shows infrastructure monitoring starts at $15 per host per month, while its pricing list shows APM starts at $31 per host per month, both when billed annually. Log ingestion starts at $0.10 per ingested GB per month, plus indexing and retention costs on top.

A realistic growth scenario

Say your team runs 50 hosts today and generates 500 GB of logs a day. A year from now, after a product launch and a couple of new services, that grows to 150 hosts and 1.5 TB of logs a day. That's not an aggressive growth curve. It's a normal one.

On Datadog's published list pricing, infrastructure and APM alone would roughly triple with host count, before log ingestion and indexing costs, which scale separately with data volume.

On Splunk, ingest-based or workload-based pricing ties cost directly to data volume. Tripling your daily log volume can triple your ingest bill on its own, independent of how many engineers are actually using the platform.

Run both, and both curves apply at the same time, on two separate invoices, for data that increasingly overlaps.

None of this makes either one overpriced for what it does well. It does mean the cost of running two full observability platforms compounds faster than most budgets plan for.

The tool sprawl problem

The math above is only part of the cost. The higher cost shows up during incidents.

New Relic's 2026 Observability Forecast found the average organization ran about four observability tools in 2025 and is trending back up to five this year. Separate research from BlueCat and Enterprise Management Associates (EMA) found that 87% of network operations teams rely on multiple tools, typically without meaningful integration between them.

That fragmentation shows up as delay. When metrics, traces, and logs live in separate platforms, someone must manually reconstruct a timeline across tools before root cause work can even start. 

New Relic's 2025 Observability Forecast, cited in the same Network World reporting, found that 41% of leaders still learn about service interruptions through customer complaints, incident tickets, or manual checks, rather than an alert firing where it should.

Running Datadog and Splunk together is a smaller version of this same problem. Every incident that touches both infrastructure and logs means switching context, re-authenticating into a second platform, and manually correlating timestamps across systems that don't share a data model.

If licensing costs are the main frustration, it's worth looking at how teams evaluate Splunk alternatives or Datadog alternatives on their own, rather than deciding by default to keep running both.

A unified alternative

New Relic starts from a different premise. Instead of building separate products for infrastructure, APM, and logs and leaving teams to stitch them together, it puts application performance monitoring, infrastructure monitoring, and log management on one data model, with one query language and one bill.

That doesn't make New Relic a replacement for Splunk's SIEM and compliance tooling. What it does is remove the reason to run two full platforms just to cover infrastructure, APM, and logs. New Relic's application performance monitoring platform surfaces infrastructure metrics, error impact, and log patterns in the same view as a trace, so correlating them during an incident doesn't require switching tools or reconciling timestamps by hand.

Pricing follows the same logic. New Relic uses usage-based pricing tied to data ingestion and user seats, not per-host counts stacked across multiple products. The platform includes 100 GB of free data ingest every month and one free full platform user, which is enough for most teams to run a real evaluation before committing budget. Current rates are on New Relic's pricing page.

Datadog and Splunk both do their core job well. The problem isn't either platform alone. It's the cost and complexity of running two full stacks to cover monitoring, APM, and logs that increasingly overlap. If your team is weighing whether to renew both licenses or consolidate onto one platform, it's worth testing using your own data. Start a free New Relic account, no credit card required.

FAQs about Datadog vs. Splunk 

Can you use Datadog and Splunk together?

Yes, and many teams do today, usually because Datadog covers infrastructure and APM while Splunk was already in place for logs and security. The trade-off is cost and correlation. You're paying for two platforms and manually connecting data that would otherwise live in one place.

Which is more expensive, Datadog or Splunk?

It depends on your workload. Datadog's per-host pricing can climb quickly as infrastructure and scalability needs grow, while Splunk's ingest-based pricing can climb just as fast alongside log volume. Since neither pricing model accounts for what the other tool already covers, teams running both usually see costs compound rather than offset each other.

Does New Relic replace both Datadog and Splunk?

New Relic covers the overlap between the two: infrastructure monitoring, APM, and log management on one platform. It doesn't replace Splunk's SIEM and compliance tooling, so security teams that rely on it will likely keep it. For teams whose Datadog and Splunk use cases mostly overlap with monitoring and logs, consolidating onto New Relic removes the duplicate licensing and lets one data model handle both jobs.

How long does it take to migrate off Splunk or Datadog?

Migration timelines vary based on the custom tooling, dashboards, and alerting logic the existing platform uses. Teams typically start by running New Relic alongside the existing tool for a few weeks to validate coverage, then shift alerting and dashboards over in phases rather than all at once.