The best observability tools in 2026

Gatling tells you what your users experience under load: response-time percentiles, throughput, error rates. It won't tell you why the p99 spiked at 4,000 concurrent users. That answer lives inside your system, in traces, logs, and infrastructure metrics, which is where an observability platform takes over.

This guide compares ten tools worth pairing with Gatling: Datadog, Dynatrace, New Relic, Splunk, InfluxDB, VictoriaMetrics, Elastic, Dash0, Tsuga, and SigNoz.

Best observability tools comparison at a glance

What are observability tools

Datadog, New Relic, and Dynatrace lead the observability tools market for tracking system metrics, logs, and traces. These platforms help engineering teams understand not just what is happening in their systems, but why something went wrong. That's the line between observability and traditional monitoring.

Observability tools collect three types of telemetry data:

• Metrics: Numerical measurements like CPU usage, memory consumption, and request latency
• Logs: Timestamped records of discrete events showing what happened and when
• Traces: End-to-end request paths revealing how a single transaction flows across distributed services

When a user reports slow checkout times, metrics tell you latency spiked. Logs show error messages. Traces pinpoint which microservice caused the delay. Together, they answer questions you didn't know to ask when you set up your monitoring.

Observability tools vs. observability platforms

A single observability tool typically handles one pillar: logs or metrics or traces. Unified observability platforms combine all three into one interface with correlated dashboards and shared context.

If your infrastructure is straightforward, individual tools can work well. Prometheus handles metrics, a separate log aggregator handles logs, and you correlate manually. However, as systems grow more distributed, switching between tools and piecing together data often costs more time than a unified platform saves in licensing fees.

The tradeoff comes down to flexibility versus convenience. Individual tools give you more control. Unified platforms reduce operational overhead.

Types of observability tooling

Before diving into specific products, it helps to understand the categories you'll encounter.

Application performance management tools

APM tools track application-level performance: response times, error rates, and transaction tracing across services. Dynatrace, New Relic, and AppDynamics are established players here, offering code-level visibility and automatic instrumentation that requires minimal manual setup.

Open source observability tools

Community-driven options like Prometheus, Grafana, Jaeger, and SigNoz carry no licensing costs. The tradeoff is operational investment: you host and maintain the infrastructure yourself. Teams that want full control over their telemetry stack often prefer this route.

Log management and analysis tools

Elastic (the ELK stack), Splunk, and Coralogix centralize and search log data from multiple services. Each has different tradeoffs around cost, query speed, and retention policies.

Network observability tools

Network observability tools monitor traffic, latency, and connectivity between services. Teams managing complex infrastructure with multiple availability zones or hybrid cloud deployments find these especially relevant.

Unified observability platforms

All-in-one enterprise observability platforms combine APM, logs, metrics, and traces in a single product. Datadog, New Relic, and Dynatrace lead this space, trading flexibility for convenience and correlated insights out of the box.

How we tested these observability solutions

We evaluated each tool against criteria that matter most to engineering teams running production workloads.

Data collection and telemetry capabilities

We assessed the breadth of supported telemetry (metrics, logs, traces) and compatibility with OpenTelemetry, the emerging standard for vendor-neutral instrumentation.

Real-time observability dashboards

Dashboard customization, visualization options, and out-of-the-box templates were key factors. Teams want relevant data immediately, not hours spent building charts.

Alerting and incident response features

We tested alert configuration flexibility, noise reduction capabilities, and integration with on-call tools like PagerDuty and Opsgenie.

Scalability and pricing transparency

High-cardinality data can break some tools, or break your budget. We reviewed how each platform handles scale and whether pricing stays predictable as data volume grows.

Developer experience and CI/CD integration

Ease of instrumentation, API quality, and native integrations with build pipelines matter for teams practicing continuous delivery. The best tools fit into existing workflows rather than requiring new ones.

Best observability tools

Datadog

A widely used all-in-one SaaS platform spanning infrastructure, APM/tracing, logs, and real user monitoring. Its Gatling integration is one of the deepest on this list: 25+ metrics pushed automatically, tagged by scenario and status, with injection events marking the start and end of a run inside Datadog's timeline. The tradeoff is cost. Load tests generate exactly the kind of high-cardinality metric bursts that drive up custom-metric charges on usage-based platforms, so it's worth watching the bill during a large campaign.

Best for: teams that want a direct Gatling connector and are willing to pay for turnkey breadth across every signal.

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Dynatrace

Enterprise-grade APM built around automatic dependency mapping and its Davis AI engine for root cause analysis. The Gatling-Dynatrace pairing is where correlation shows up most concretely: a Gatling-reported error rate can be traced straight to a saturated database connection pool through Dynatrace's topology view, turning "something failed" into a specific, named cause.

‍Best for: large organizations that already run Dynatrace for production APM and want the same lens over their load tests.

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New Relic

A single consolidated platform with a data-ingest-plus-per-user pricing model rather than per-host billing. That pricing structure tends to be more predictable under load-test bursts than platforms that charge per custom metric or per host, since the variable to watch is total data volume rather than cardinality.

Best for: teams that want one bundled platform and prefer ingest-based pricing over host-based or cardinality-based billing.

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InfluxDB

The classic Gatling pairing on the metrics side. Gatling writes directly via InfluxDB Line Protocol, and InfluxDB 1, 2, and 3 are all supported, along with other line-protocol-compatible databases. It's a purpose-built time-series store, not a full APM suite, so you won't get distributed tracing or log correlation without pairing it with something else. What you do get is low cost, full data ownership, and live dashboards during a run.

Best for: teams that want a self-hosted, purpose-built metrics backend without the overhead of a full observability suite.

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Splunk

An enterprise observability suite built on Splunk's log-analytics heritage plus the real-time streaming metrics engine it acquired with SignalFx in 2019, now operating under Cisco after a roughly $28 billion acquisition that closed in March 2024. Splunk APM is OpenTelemetry-native, and Splunk ships its own distribution of the OpenTelemetry Collector as the recommended way to get data in.

There's no dedicated Gatling connector, so the integration path is Gatling's OpenTelemetry export into that collector. The pricing dimensions to watch during a load test are host count and metric time series (MTS): tests that spin up many short-lived hosts or emit high-cardinality custom metrics can push both up quickly.

Best for: enterprises already standardized on Splunk or Cisco's observability stack that want load-test data sitting next to the same logs and traces they use in production.

VictoriaMetrics

A high-performance, Prometheus-compatible, open-source time-series database with no dedicated Gatling connector, but no gap either: it natively accepts the Graphite and InfluxDB line protocols that Gatling's own data writers already speak, so it drops in with no extra bridge and renders through Grafana or any Prometheus-compatible dashboard.

It's built specifically for high-cardinality workloads, which is exactly the profile of a load test's metric output, and it's designed to run on meaningfully less RAM and disk than comparable stores.

Best for: cost-conscious teams already in the Prometheus ecosystem who want a cardinality-tolerant, self-hosted backend without operating a heavier stack.

Elastic

The Elastic Stack (Elasticsearch, Kibana, Beats, Logstash) extended into a full observability suite: logs, metrics, APM/traces, synthetics, and profiling. Its differentiator is a search-first architecture, telemetry stored as queryable documents in Elasticsearch's Lucene-based index, which makes ad-hoc log correlation and free-text search stronger than in most metrics-first platforms. Elastic's licensing has moved twice: from Apache 2.0, to a dual SSPL/Elastic License v2 in 2021, to adding AGPLv3 back in 2024, making the core open-source again.

Elastic has its own OpenTelemetry distribution, EDOT, and Elastic APM accepts OTLP natively. There's no dedicated Gatling connector; the path is Gatling's OTel export into EDOT or Elastic APM's OTLP endpoint, or, for open-source Gatling, a custom shipper using the Graphite writer. Cost scales with ingest volume and retention rather than host count, which tends to be gentler on short, bursty load-test windows than host- or MTS-based pricing.

Best for: teams that want search-heavy log analysis and the flexibility to self-host, run on Elastic Cloud, or use consumption-based Serverless.

Dash0

An OpenTelemetry-native platform built around metrics, logs, and traces ingested through OTLP by design, not as an afterthought. Dash0 positions itself directly against the unpredictable, cardinality-driven billing of incumbent SaaS observability tools, emphasizing transparent, usage-based pricing.

There's no dedicated Gatling connector, but because both Gatling and Dash0 are OTLP-native, the integration path is the same generic Gatling-to-OTLP bridge that works with any OTel-compatible backend. It's a newer entrant in this space, so it's worth confirming current pricing and feature maturity directly against its own documentation before standardizing on it.

Best for: teams already committed to OpenTelemetry who want APM-grade correlation without host- or cardinality-based billing.

Tsuga

A bring-your-own-cloud (BYOC) observability platform for logs, metrics, traces, and APM, where the entire data plane, storage, indexing, and compute, runs inside the customer's own cloud account rather than the vendor's. Tsuga positions itself as OpenTelemetry-native throughout, which means the same generic Gatling-to-OTLP path applies here too. It's a newer, less independently documented product than the others on this list, so treat its specific claims around setup time, pricing, and architecture as vendor-reported until you've verified them directly.

Best for: regulated industries or teams with hard data-residency requirements, where telemetry needs to stay inside their own cloud account rather than a third party's.

SigNoz

An open-source, OpenTelemetry-native platform positioned as a self-hosted alternative to Datadog or New Relic, built by Pranay Prateek and Ankit Nayan and backed by Y Combinator. Logs, metrics, traces, and APM all live in one pane, stored on ClickHouse, a columnar database whose compression handles high-cardinality metric bursts more economically than most time-series stores, which is exactly the profile a load test produces. Deployment is self-hosted (Docker or Kubernetes) or SigNoz Cloud. There's no dedicated Gatling connector, but since SigNoz ingests OTLP natively with no proprietary agent, Gatling's OpenTelemetry export reaches it directly. Self-hosted cost is infrastructure and ops, not a vendor bill, putting it in the same cost category as VictoriaMetrics; SigNoz Cloud switches to usage-based pricing on ingested GB and metric samples.

Best for: cost-conscious teams that want an open-source, OTel-native alternative to the big APM suites, with no per-host or per-seat lock-in, and are willing to operate ClickHouse.

How to choose the best observability platform

Match capabilities to your cloud architecture

AWS, Kubernetes, and on-premises environments each favor different tools. Cloud observability tools like Datadog integrate deeply with AWS and GCP. Prometheus suits Kubernetes-native setups. Hybrid infrastructure often benefits from SolarWinds or Elastic.

Calculate total cost of ownership

License price is just the start. Factor in data ingestion costs, retention fees, and the time your team spends on setup and maintenance. A "free" open-source tool can cost more than a commercial platform if it requires dedicated engineering time to operate.

Prioritize integration with your CI/CD pipeline

Observability frameworks that connect to your build and deployment tools enable faster feedback loops. Performance testing data from tools like Gatling can feed directly into observability dashboards, giving you pre-production insights alongside production telemetry.

Weigh open source vs. enterprise observability options

Open source observability tools offer flexibility and cost savings but require operational investment. Enterprise platforms trade cost for managed infrastructure and support. The right choice depends on your team's capacity and priorities.

Choosing by use case

• Cost-sensitive and want full control: InfluxDB, VictoriaMetrics, or self-hosted SigNoz, all reachable through Gatling's existing metrics writers or its OTel export, with infrastructure cost rather than a vendor bill.
• Need deep APM and tracing correlation, budget isn't the constraint: Datadog, Dynatrace, or New Relic, all with direct Gatling connectors, or Splunk if your organization is already standardized on it.
• Log-heavy diagnosis or already invested in a specific ecosystem: Splunk if you're on Splunk/Cisco; Elastic if you want search-driven log analysis with the flexibility to self-host or move to Elastic Cloud/Serverless.
• Standardizing on OpenTelemetry to avoid lock-in: Dash0, Tsuga, or SigNoz, all reachable through Gatling's native OTel integration, with the choice coming down to transparent SaaS pricing (Dash0), keeping data inside your own cloud account (Tsuga), or a fully open-source, self-hosted stack you control end to end (SigNoz).
• Data residency is a hard requirement: Tsuga's BYOC model is the most directly relevant option here, since telemetry never leaves your own cloud account.

Why observability data benefits from performance testing

Observability shows what's happening in production, but by then, users are already affected. Load testing reveals how systems behave under stress before deployment.

• Observability without load testing: Reactive, you see problems after users report them.
• Observability with load testing: Proactive, you catch performance risks before deployment.
• Gatling Enterprise integration: Stream performance test metrics directly to Datadog, Dynatrace, or your observability platform

When you combine continuous performance testing with observability, you get a complete picture: how your system will behave under load, and how it is behaving in production.

Why pair Gatling with an observability tool at all

Gatling measures the system from the outside, the way a real user would experience it. Observability platforms measure from the inside: APM traces, infrastructure metrics (CPU, memory, GC pauses, connection pools, database query latency), and logs.

The value is in correlation. Overlay the Gatling load profile against backend telemetry on one timeline, then drill from a latency spike into the trace of a slow request to find the offending service or query. In one internal demo, Gatling reported a 10% error rate during a run. Correlating that against Dynatrace data traced the failure to a database throughput bottleneck, turning "something failed" into a specific, named cause.

How Gatling connects: direct integrations, plus OpenTelemetry for everything else

Gatling Enterprise documents direct APM integrations for four platforms:

• Datadog: pushes 25+ metrics automatically (request timing, TCP connections, TLS handshakes, bandwidth), tagged by team, test, scenario, and status, along with injection start and end events. Custom tags are supported via system properties.
• Dynatrace: correlates Gatling's client-side results with Dynatrace's topology-aware root cause analysis.
• New Relic: streams Gatling metrics directly into New Relic's unified telemetry data model.
• InfluxDB: writes via InfluxDB Line Protocol over HTTP, compatible with InfluxDB 1, 2, and 3.

On top of those four, Gatling has a native OpenTelemetry integration where metrics and logs go to any OTel-compatible collector, using exponential histograms for response times and resource attribution per run. That single integration is what extends Gatling's reach past its four named connectors.

Any backend that speaks OTLP, whether it has a dedicated Gatling connector or not, can receive Gatling's telemetry through this path. It's also why protocol-compatible stores that accept Gatling's Graphite or InfluxDB line protocol output, like VictoriaMetrics, work without any extra bridge.

In practice, this means the newer OTel-native challengers built specifically around OTLP, like Dash0, Tsuga, and SigNoz, are already reachable from Gatling even without a named integration guide for them: instrument once with Gatling's OTel path, then send the data wherever your team has standardized.

The same is true of Splunk Observability Cloud and Elastic Observability. Neither has a documented, Gatling-specific connector page, but both accept OTLP directly (Splunk via its own distribution of the OpenTelemetry Collector, Elastic via its EDOT distribution), so the generic OTel bridge covers them too.

Distributed tracing: the newest addition

Gatling Enterprise's latest release adds distributed tracing on top of its existing metrics and events integrations. The idea: metrics tell you a request was slow, tracing tells you where the time went.

When tracing is enabled, Gatling propagates a W3C trace context on the requests it sends and exports its own view of each one as an OpenTelemetry span, over OTLP (HTTP/protobuf). Your application, already instrumented, continues that same trace on its side. Both halves meet in your observability backend under a single trace ID. Gatling's span measures what the client experienced: total time on the wire, from the injector. Your application's spans measure what it spent internally: controllers, database calls, downstream services. The gap between the two is network time and queueing, and now you can actually see it instead of inferring it.

Because spans export over standard OTLP, any backend that accepts it works: Datadog, Dynatrace, Elastic, Honeycomb, Jaeger, New Relic, Splunk (via its OpenTelemetry Collector distribution), or a collector you run yourself, which covers most of the tools compared in this piece.

A few mechanics worth knowing before turning it on:

• It's Enterprise-only, requires Gatling 3.5 or later (every version Enterprise currently supports it), and needs an application under test that's instrumented to continue an incoming trace rather than start its own.
• There's no separate switch. Tracing turns on the moment you configure an OTLP traces endpoint, either at the private-location level (applies to every test running there) or per test, and turns off the moment you remove that configuration.
• Sampling is rate-based, not percentage-based. You set a target number of traced requests per second for the whole run (10 by default), and Gatling splits that budget across all load generators. A run at the default rate sends about 10 traced requests per second in total whether it's running on one injector or forty, so if the configured rate is lower than the number of generators, some of them trace nothing at all.
• It's HTTP-only for now. Other protocols don't report spans, and sub-timings like DNS resolution, TCP connect, and TLS handshake still live in Gatling's metrics rather than as child spans.
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Build your unified observability strategy

The best observability approach pairs the right platform with proactive performance validation. Here's where to start:

• Start with clear SLOs for latency, error rate, and throughput
• Instrument critical services first, then expand coverage as your practice matures
• Integrate performance testing into your observability workflow
• Explore Gatling Enterprise for performance testing that streams data to your observability stack

The takeaway

The strategic move for a performance testing practitioner isn't picking a single "best" backend, it's standardizing on OpenTelemetry as the instrumentation layer and picking a backend that fits your team's budget and maturity today, knowing you can change backends later without re-instrumenting.

Gatling's four direct integrations cover the established players; its OTel integration, now paired with the newest release's distributed tracing, keeps every OTel-compatible option on the table too, from enterprise suites like Splunk and Elastic to newer, cost-transparent entrants like Dash0, Tsuga, and SigNoz.

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