# 9 Best Datadog Alternatives and Competitors in 2026

> Compare 9 Datadog alternatives by use case, pricing model, and migration path: Last9, Grafana Cloud, SigNoz, New Relic, Dynatrace, Splunk, and more.

Source: https://last9.io/blog/8-datadog-alternatives-for-2024/

_Last updated: August 25, 2026._

If you're reading this, there's a decent chance you already know why you're here: a Datadog bill that grew faster than your infrastructure did. Custom metrics, log indexing, APM hosts, and high-cardinality tags each show up as their own line item, and the total is hard to predict from month to month even when your traffic hasn't changed much. Search "Datadog pricing" on Reddit or Hacker News and you'll find the same complaint repeated by teams at very different scales: the platform is genuinely good, the bill is genuinely the problem.

This isn't a "Datadog is bad" piece. It's a practical comparison of nine real alternatives, what each one is actually built for, what it costs at a realistic scale, and where it falls short, so you can figure out which one (if any) fits your situation instead of taking a vendor's word for it.

## Why teams look for Datadog alternatives

Three friction points come up again and again in why teams start evaluating alternatives, not because Datadog doesn't work, but because of how the bill behaves as usage grows:

- **Unpredictable pricing.** Per-host infrastructure and APM charges, plus per-GB log ingestion and per-event indexing, mean the bill is a function of several independent variables at once. Add a few high-cardinality metric labels and the impact shows up in the invoice before anyone notices it in a dashboard.
- **Platform complexity for smaller teams.** Datadog's breadth (APM, RUM, Synthetics, Security, CI Visibility, and more) is a genuine strength for large orgs standardizing on one vendor, and genuine overkill for a team that mainly needs metrics, logs, and traces working well together.
- **Difficulty forecasting bills.** Because so many pricing dimensions move independently, teams often can't answer "what will next month cost if we add these ten services" with any confidence, which is a real operational problem when the answer feeds into a budget. For a full breakdown, see [our guide to Datadog pricing](https://last9.io/blog/datadog-pricing-all-your-questions-answered/).

Here's how the main alternatives stack up at a glance:

| Tool                           | Type                              | Pricing model                                                    | Best for                                                                                                             | Watch out for                                                                     |
| ------------------------------ | --------------------------------- | ---------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- |
| **Last9**                      | Glassbox (BYOC)                   | Usage-based, no per-host charge                                  | High-cardinality metrics, logs, and traces, plus APM, RUM, Synthetics, and infra, without the per-label cost penalty | Requires OpenTelemetry or Prometheus-compatible instrumentation to get full value |
| **Grafana Cloud / Prometheus** | SaaS (managed) or self-hosted OSS | Per-active-series (metrics) + per-GB (logs/traces) + per-user    | PromQL and Grafana dashboards without operating the backend yourself                                                 | Multi-signal usage can add up across several independently priced dimensions      |
| **SigNoz**                     | SaaS or self-hosted OSS           | Per-GB (logs/traces) + per-sample (metrics), or free self-hosted | OpenTelemetry-native, one UI for all three signals                                                                   | Self-hosting means owning the operational burden                                  |
| **New Relic**                  | SaaS                              | Per-GB ingest (all signals combined) + per-user                  | Broad APM coverage, generous free ingest tier                                                                        | Per-user Pro-tier pricing gets expensive past 5 full-platform seats               |
| **Dynatrace**                  | SaaS / Managed                    | Per GiB ingested                                                 | Full-stack coverage without wiring up every signal manually                                                          | Enterprise pricing and complexity                                                 |
| **Splunk Observability**       | SaaS / On-prem                    | Per GB ingested                                                  | Heavy log volume and analytics needs                                                                                 | Ingest-based cost escalates with telemetry volume                                 |
| **Elastic Observability**      | SaaS / Self-managed               | Ingest/consumption-based (Elastic Cloud)                         | Teams already invested in Elasticsearch                                                                              | Requires real operational Elasticsearch expertise                                 |

## The 9 best Datadog alternatives

### 1\. Last9

**Best for:** high-cardinality metrics without treating every useful label as a cost risk.

Last9 runs as a glassbox deployment, also called BYOC (bring your own cloud): the data plane sits inside your own cloud account while Last9 operates the control plane, rather than the shared multi-tenant SaaS model most tools on this list use. It's OTLP-native and compatible with Prometheus and OpenTelemetry, so instrumentation you already have doesn't need to change to switch backends. Beyond metrics, logs, and traces, the platform now covers APM, RUM, and Synthetic monitoring alongside infrastructure, with 100+ pre-built integrations for common sources. The core positioning is still a telemetry control plane: keep the labels that make an incident debuggable (user tier, region, deployment version) without those same labels multiplying your bill the way they do on per-host or per-metric pricing models, priced on usage-based terms rather than per host.

Key features: glassbox (BYOC) deployment, OTLP-native ingestion, Prometheus and OpenTelemetry compatibility, cardinality-aware cost controls (see [OpenTelemetry Explained](https://last9.io/blog/distributed-tracing-with-opentelemetry/) for how the underlying Last9 Cardinality Score framework works), APM, RUM, and Synthetic monitoring alongside infra, 100+ pre-built integrations, usage-based pricing with no per-host charge.

Pros: no per-host tax as you scale infrastructure, works with existing OTel/Prometheus instrumentation with no re-instrumentation, cost stays tied to what you actually query and retain rather than raw host count, telemetry stays in your own cloud under the glassbox model.

Cons: newer platform than the established incumbents on this list, so less brand-name recognition; best value assumes you're already on or moving toward OpenTelemetry or Prometheus-compatible instrumentation.

Best for: engineering-led teams already on or moving toward OpenTelemetry who've been burned by cardinality-driven cost spikes on a per-host platform.

_If cardinality is the reason you're reading this, [book a demo](https://last9.io/schedule-demo/) and bring your current Datadog bill. It's usually the fastest way to see the actual difference for your specific usage._

### 2\. Grafana Cloud and Prometheus

**Best for:** teams that want PromQL and Grafana dashboards without operating the storage backend themselves.

Grafana Cloud is the managed version of the open-source Grafana, Prometheus, Loki, and Tempo stack. The appeal is standing on fully open standards (nothing proprietary to migrate off of later) with a generous always-free tier: 10,000 active metric series and 50GB each of logs and traces per month, no credit card required. Past the free tier, Pro pricing starts at a $19/month platform fee, then usage charges apply per active series for metrics and per GB for logs and traces. For the deeper feature-by-feature breakdown, see our [Datadog vs Grafana](https://last9.io/blog/datadog-vs-grafana/) and [Prometheus vs Datadog](https://last9.io/blog/prometheus-vs-datadog/) comparisons.

Key features: native PromQL and Grafana dashboards, open exporter ecosystem, self-hosted option available if you want to avoid usage-based cloud pricing entirely.

Pros: open standards throughout, strong free tier, visualization is genuinely best-in-class since it's the same Grafana most teams already use.

Cons: multi-signal usage (metrics plus logs plus traces at real scale) can add up across several independently priced dimensions, similar to the complaint teams have about Datadog, just with different unit prices.

Best for: teams standardized on Prometheus and Grafana already who want a managed version rather than running their own storage.

### 3\. SigNoz

**Best for:** teams standardized on OpenTelemetry who are comfortable self-hosting, or want a lower-cost managed option.

SigNoz puts metrics, logs, and traces in one application built OTel-native from the start, with an integrated APM and trace view. The Community Edition is genuinely free and self-hosted with full control over your own infrastructure. Cloud pricing starts at $49/month for the Teams plan (usage-based beyond that at $0.30/GB for logs and traces, $0.10 per million metric samples), with eligible startups getting 50 percent off.

Key features: unified metrics/logs/traces UI, OpenTelemetry-native, free self-hosted Community Edition.

Pros: lowest-cost entry point on this list if you're willing to self-host, transparent per-GB cloud pricing if you're not.

Cons: self-hosting means you own the operational burden of running and scaling the backend yourself, smaller company and support org than the enterprise names on this list.

Best for: teams already OpenTelemetry-native who want either a free self-hosted option or simple, transparent usage-based cloud pricing.

### 4\. New Relic

**Best for:** medium to large teams needing broad APM coverage across many languages, with a genuinely useful free tier.

New Relic bundles all telemetry types (metrics, logs, traces) into a single ingest pool rather than pricing infrastructure and APM separately the way Datadog does. The free tier includes 100GB of data ingest per month and one full-platform user at no cost, which is real headroom for a small team. Past that, ingest is $0.40/GB (or $0.60/GB on the Data Plus tier for extended retention), and full-platform user seats range from $10 for a first Standard user up to $349/user/month on the Pro tier for larger teams needing more than five full users. See our full [New Relic vs Datadog](https://last9.io/blog/new-relic-vs-datadog/) comparison for more detail.

Key features: mature APM with distributed tracing, broad language support, unified ingest pricing (no separate per-host APM charge).

Pros: strong free tier, ingest-based pricing is simpler to reason about than Datadog's multiple independent dimensions.

Cons: cost still tracks ingest volume and user count, and Pro-tier per-user pricing gets expensive fast for teams needing more than a handful of full-platform seats.

Best for: teams who want one APM platform with straightforward ingest-based pricing and can keep their full-platform user count small, or are ready to pay for Pro-tier seats if not.

### 5\. Dynatrace

**Best for:** large enterprises wanting full-stack coverage without manually wiring up every signal.

Dynatrace's OneAgent auto-instruments across the stack, and its Davis AI feature handles dependency mapping and root-cause suggestions automatically. Pricing is per GiB ingested rather than per host. See our [Datadog vs Dynatrace](https://last9.io/blog/datadog-vs-dynatrace/) comparison for the full breakdown.

Key features: OneAgent auto-instrumentation, Davis AI for automated root-cause analysis, full-stack coverage out of the box.

Pros: minimal manual instrumentation work, strong for large, complex environments that need automated correlation across many services.

Cons: enterprise pricing and complexity, often more platform than a small or mid-sized team needs.

Best for: large enterprises that want automation to do the correlation work that a smaller team would otherwise do manually.

### 6\. Splunk Observability

**Best for:** enterprises with heavy log volume and analytics needs.

Splunk's observability suite leans on the company's long history in log search and analytics at scale, with real-time streaming and a mature ecosystem. Pricing is per GB ingested. See our [Splunk vs Datadog](https://last9.io/blog/splunk-vs-datadog-a-side-by-side-comparison/) comparison for a side-by-side look.

Key features: log search at genuinely large scale, real-time streaming analytics, deep ecosystem of integrations built over many years.

Pros: best-in-class for teams whose primary need is searching and analyzing huge log volumes.

Cons: ingest-based cost escalates with telemetry volume the same way Datadog's does, and the platform's strength (log analytics depth) is overkill if that's not your primary need.

Best for: enterprises where log search and analytics, not tracing or APM, is the actual bottleneck.

### 7\. Elastic Observability

**Best for:** teams already invested in Elasticsearch who want to extend that investment into observability.

Elastic Observability reuses the Elasticsearch and Kibana skills a team may already have, with flexible self-managed or SaaS deployment.

Key features: powerful log search built on Elasticsearch, flexible deployment options, reuses existing Elastic expertise.

Pros: genuinely strong choice if your team already runs Elasticsearch for other purposes and doesn't want a second search paradigm to learn.

Cons: requires real operational Elasticsearch expertise if self-managed, which is a meaningful ask if that expertise doesn't already exist on the team.

Best for: teams with existing Elasticsearch operational knowledge who want to extend it rather than adopt a new platform.

### 8\. AppDynamics

**Best for:** enterprises prioritizing business-transaction monitoring over open-standard metrics.

AppDynamics (Cisco-owned) focuses on transaction tracing and topology mapping with business-impact reporting, useful when the question is "how is this outage affecting revenue" rather than purely technical metrics.

Key features: transaction-level tracing, topology mapping, business-impact reporting tied to technical incidents.

Pros: strong fit when stakeholders need business-impact framing, not just technical dashboards.

Cons: APM-focused with less emphasis on open-standard metrics compared to OpenTelemetry-native options on this list.

Best for: enterprises where translating incidents into business impact for non-technical stakeholders is a core requirement.

### 9\. LogicMonitor

**Best for:** mid to large teams managing hybrid or multi-cloud infrastructure.

LogicMonitor emphasizes automated discovery and AIOps across hybrid and multi-cloud environments, with a large template library for common infrastructure types.

Key features: automated infrastructure discovery, AIOps-driven alerting, extensive pre-built templates.

Pros: strong infrastructure-monitoring automation for complex, hybrid environments.

Cons: infrastructure-first focus means less application-level tracing depth than APM-focused options on this list.

Best for: teams whose primary pain is hybrid/multi-cloud infrastructure visibility rather than application-level tracing.

### Also worth a look

A few more names that come up in this space depending on your specific need: **OpenObserve** (OpenTelemetry-native, open-source, positions itself as a lighter-weight alternative for smaller deployments), **Middleware** (AI-driven, unified observability with a focus on cost efficiency), **Coralogix** (streaming-first architecture that analyzes data before deciding what to store, aimed at controlling log costs), **Instana** (IBM-owned, strong automatic and continuous instrumentation for microservices), and **AWS CloudWatch** (the default if you're all-in on AWS and want one less vendor relationship to manage, though generally less capable for cross-cloud or hybrid setups).

## What this actually costs: a real example

Pricing tiers in isolation don't tell you much. Here's what five of these tools would cost at a specific, realistic scale: **50 hosts, 500GB of logs per month, a 10-person engineering team**, using each vendor's own current public list pricing.

| Tool              | Estimated monthly cost           | What's included in this estimate                                                                                                                                                                                                                                                                                                                                                                                          |
| ----------------- | -------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Datadog**       | ~$2,775                          | Infrastructure Monitoring Pro (50 hosts x $15) + APM (50 hosts x $31) + log ingest (500GB x $0.10) + indexing (assuming 50% of logs indexed, a realistic rather than worst-case rate, at $1.70/million events)                                                                                                                                                                                                            |
| **New Relic**     | ~$3,650                          | 10 Pro-tier full-platform users (10 x $349, since the 5-user cap on Standard doesn't cover a 10-person team) + ingest beyond the 100GB free tier (400GB x $0.40)                                                                                                                                                                                                                                                          |
| **Grafana Cloud** | ~$323 (logs and platform only)   | Pro platform fee ($19) + log ingest beyond the 50GB free tier (450GB at a combined ~$0.55/GB process/write/retain rate) + 7 paid users beyond the 3 free (7 x $8). Excludes metrics cost, which scales with active series count and isn't comparable at a flat per-host rate.                                                                                                                                             |
| **SigNoz**        | ~$150 to $199 (logs only, cloud) | 500GB of logs at the published $0.30/GB usage rate, above the $49/month Teams plan base. Excludes metrics and traces volume, and doesn't reflect the free self-hosted option, which would be $0 in infrastructure cost paid to SigNoz.                                                                                                                                                                                    |
| **Last9**         | Usage-based, quoted per workload | Not a published list price since it's usage-based and quoted per workload, but here's a real number rather than a shrug: Probo's public case study documented a 90 percent reduction in monitoring spend after switching from Datadog. Applied to this scenario's ~$2,775 Datadog estimate, that works out to roughly $278/month, an illustration of the scale of the difference, not a quote for your specific workload. |

A few honest caveats on this table: these are list prices as published today, not negotiated enterprise rates, which can differ meaningfully at scale. The Grafana Cloud and SigNoz figures exclude metrics costs specifically because those scale with active series or sample count in a way that isn't fairly comparable to a flat "50 hosts" assumption. And Datadog's indexing assumption (50% of ingested logs) is a realistic middle ground, not a guarantee. If your team indexes closer to 100% of what it ingests, that line moves up substantially. And the Last9 figure is a percentage-based estimate applied to this scenario, drawn from one real customer's documented outcome, not a workload-specific quote.

_Want an actual number for your own usage instead of an estimate? [See how Last9 works](https://app.last9.io/) with your real telemetry volume, or [book a demo](https://last9.io/schedule-demo/) and bring your current bill._

## Where Last9 isn't the best fit

Every credible comparison includes the cases where the answer isn't "us," so here's where Last9 genuinely isn't the right call:

- **You want a single platform that includes built-in Security monitoring.** Last9 now covers APM, RUM, Synthetic monitoring, and infrastructure alongside metrics, logs, and traces, but security monitoring isn't part of the platform, so that piece still needs a separate tool if it's a hard requirement.
- **Your team isn't on, or moving toward, OpenTelemetry or Prometheus-compatible instrumentation.** Last9's advantages are strongest for teams already using or adopting these open standards. If your stack is built entirely around a different vendor's proprietary agent and you're not planning to change that, the switching cost may not be worth it yet.

## How to choose the right Datadog alternative

Start from the problem, not the brand. If the pain is per-host APM cost at scale, look at usage-based options like Last9 or SigNoz. If it's log analytics specifically, Splunk or Elastic's depth may matter more than switching cost. If it's a smaller team wanting open standards without operating the backend, Grafana Cloud's free tier is worth testing first before any of the enterprise options.

Whichever direction looks promising, the lowest-risk path is dual shipping: run the new backend alongside Datadog for a few weeks before committing to a full migration. It costs a bit more temporarily, but it means you're evaluating with your actual production telemetry instead of a synthetic test, and you can back out with zero disruption if it doesn't work out.

It depends on your situation more than it depends on which tool has the longer feature list. The honest answer for most teams is to test the two or three candidates that match their actual bottleneck (cost, log analytics, or full-stack breadth) against real traffic before deciding.

Whatever you choose, the highest-leverage first step is usually the same: get an accurate read on what's actually driving your current bill (host count, log volume, or metric cardinality) before evaluating alternatives, since that answer points you toward a different tool depending on which one it is.

## FAQ

### What's the best Datadog alternative overall?

There isn't a single best answer, it depends on what's actually driving you to look. For unpredictable, cardinality-driven cost, Last9 and SigNoz are usage-based rather than per-host. For broad APM with a strong free tier, New Relic is worth testing. For open-standards visualization without running your own backend, Grafana Cloud's free tier is a low-risk starting point.

### What's a cheaper alternative to Datadog?

SigNoz's self-hosted Community Edition is free. Grafana Cloud's always-free tier (10,000 active series, 50GB logs, 50GB traces monthly) covers small-team usage at no cost. Last9's usage-based model avoids the per-host charge that drives a lot of Datadog's cost growth as infrastructure scales.

### Is Last9 a Datadog alternative?

Yes. Last9 is a glassbox (BYOC) platform, built OTLP-native and compatible with Prometheus and OpenTelemetry, positioned specifically for teams whose Datadog costs are being driven by high-cardinality metrics and per-host pricing. It also covers APM, RUM, Synthetic monitoring, and infrastructure, not just metrics, logs, and traces.

### How hard is migrating from Datadog?

The lowest-risk approach is dual shipping: send telemetry to both Datadog and the new backend for a few weeks before cutting over, so you're validating against real production data rather than a synthetic test and can revert with no disruption if needed. Last9 specifically supports this: it dual ships directly from the Datadog Agent, so you can compare cost and workflows side by side before replacing Datadog broadly.

### How does SigNoz compare to Datadog?

SigNoz is OpenTelemetry-native with unified metrics, logs, and traces in one application, and offers a genuinely free self-hosted Community Edition, something Datadog doesn't have an equivalent to. The tradeoff is that self-hosting means owning the operational work of running that backend yourself.

### How does Splunk compare to Datadog on cost?

Both price primarily on ingest volume (per GB), so both can see costs escalate similarly as telemetry volume grows. Splunk's specific strength is log search and analytics depth at scale, which is a reason to choose it over Datadog if that's your specific bottleneck, not a reason it's inherently cheaper.

### How does New Relic compare to Datadog?

New Relic bundles all telemetry types into one ingest-based pricing pool rather than pricing infrastructure and APM separately, and its 100GB free tier is more generous than Datadog's free tier. The tradeoff shows up in per-user pricing on the Pro tier, which gets expensive for teams needing more than five full-platform seats.

### Why do teams switch away from Datadog?

The most common reasons are unpredictable, hard-to-forecast bills driven by multiple independent pricing dimensions (per-host, per-GB, per-event), platform complexity that exceeds what a smaller team actually needs, and cost spikes driven by high-cardinality metric labels that aren't easy to see coming until the invoice arrives.

_If cardinality and per-host cost are the actual problem, [see how Last9 works](https://app.last9.io/) with a workload like yours, or [book a demo](https://last9.io/schedule-demo/) to walk through your specific numbers._