Performance

An index and topic collection covering API and web performance, including load testing, performance benchmarking, real user monitoring (RUM), Core Web Vitals measurement, latency profiling, distributed tracing, and application performance monitoring (APM). Performance engineering ensures that APIs and web applications meet latency, throughput, and reliability expectations under realistic and adversarial load. This collection brings together open-source load generators like k6, Apache JMeter, Locust, Gatling, and Artillery; managed load and chaos platforms like StormForge and Speedscale; synthetic and real-user measurement tools like Google PageSpeed, Pingdom, and Akamai; and APM and tracing platforms like Datadog, New Relic, Dynatrace, AppDynamics, Sentry, Honeycomb, Instana, Lightstep, SigNoz, Uptrace, OpenTelemetry, Jaeger, Grafana Tempo, Google Cloud Profiler, Google Cloud Trace, and AWS X-Ray.

Performance is profiled on the APIs.io network. Tagged areas include Performance, Load Testing, Performance Testing, Real User Monitoring, and Core Web Vitals.

The Performance catalog on APIs.io includes 1 JSON-LD context.

Performance’s developer surface includes developer portal and 1 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

9.6/100 minimal ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-10-23 · rubric v0.25.0 · Show this score on your site →

0 APIs 8 Features 7 Use Cases

Performance Load Testing Performance Testing Real User Monitoring Core Web Vitals APM Distributed Tracing Latency

Kin Score

Kin Score How this is scored →
scored 2026-10-23 · rubric v0.25.0

Composite quality — 9.6/100 · minimal

Contract Quality 3.1 / 21

Developer Ergonomics 1.6 / 17

Access Clarity 0.0 / 17

Operational Transparency 0.6 / 11

Contract Governance 0.0 / 10

Discoverability 4.1 / 9

Regulatory Posture 0.7 / 15

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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It updates itself when the score changes. Paste this where you link to your API docs:

<a href="https://apis.io/providers/performance/"><img src="https://apis.io/badge/performance/card.svg" alt="Performance on APIs.io — Kin Score by API Evangelist"></a>

Other styles, a README pill and a shields.io endpoint are on the badge page.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from github.com/api-evangelist/performance: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Features 8

Notable capabilities this provider offers.

Load and Stress Testing

Open-source and managed load generators like k6, Apache JMeter, Locust, Gatling, and Artillery simulate concurrent users and traffic patterns against APIs to characterize throug...

Performance Benchmarking

Repeatable, scripted benchmark runs establish performance baselines and detect regressions in CI/CD pipelines using tools like k6 Cloud, BlazeMeter, and StormForge.

Real User Monitoring (RUM)

RUM products like Akamai mPulse, Datadog RUM, New Relic Browser, Sentry Performance, and Pingdom capture latency, errors, and Core Web Vitals from real browser sessions in produ...

Synthetic Monitoring and Web Vitals

Synthetic checks and lab-based audits from Google PageSpeed (Lighthouse), WebPageTest, and Pingdom measure Core Web Vitals (LCP, INP, CLS) and uptime from controlled environments.

Distributed Tracing

OpenTelemetry, Jaeger, Grafana Tempo, Honeycomb, Lightstep, Google Cloud Trace, and AWS X-Ray collect distributed traces across microservice calls to attribute latency to specif...

Application Performance Monitoring (APM)

APM platforms like Datadog APM, New Relic, Dynatrace, AppDynamics, Instana, SigNoz, and Uptrace correlate traces, metrics, and logs to surface slow transactions and code-level b...

Profiling and Code Hotspots

Continuous profilers like Google Cloud Profiler and Datadog Continuous Profiler identify CPU, memory, and lock contention hotspots in running services.

Traffic Replay and Chaos

Tools like GoReplay and Speedscale capture production traffic and replay it against staging or new versions to validate performance and behavior before release.

Scroll for all 8

Semantic Vocabularies 1

JSON-LD contexts and semantic vocabularies used across these APIs.

Performance Context

7 classes · 37 properties

JSON-LD

JSON Schema 2

Standalone JSON Schema definitions for this provider's data models.

LoadTestRun

10 properties

JSON SCHEMA

WebVitalSample

11 properties

JSON SCHEMA

JSON Structure 2

JSON Structure definitions describing this provider's data shapes.

Performance Load Test Run Structure

10 properties

JSON STRUCTURE

Performance Web Vital Sample Structure

11 properties

JSON STRUCTURE

Examples 2

Example request and response payloads for these APIs.

Performance Load Test Run Example

10 fields

EXAMPLE

Performance Web Vital Sample Example

11 fields

EXAMPLE

Use Cases 7

What developers build with this provider.

Pre-Release Load Testing in CI/CD

Engineering teams run k6 or JMeter scenarios on every pull request to verify that p95 latency and error rates stay within service-level objectives before merging.

Core Web Vitals Optimization

Web teams use Google PageSpeed, WebPageTest, and RUM data to optimize LCP, INP, and CLS to meet Google ranking and user-experience thresholds.

Capacity Planning and Scalability Validation

Platform teams use StormForge and BlazeMeter to drive sustained load tests against staging environments to determine maximum sustainable throughput and right-size infrastructure.

Latency Regression Detection

APM platforms like Datadog, New Relic, and Dynatrace alert on p95 and p99 latency regressions per endpoint or trace span after each deploy.

Production Traffic Replay

Teams use GoReplay or Speedscale to record real production traffic and replay it against release candidates to find performance and correctness regressions before rollout.

Distributed Trace Root-Cause Analysis

SREs use Honeycomb, Lightstep, Jaeger, or Grafana Tempo to drill into slow distributed traces and pinpoint the service, query, or downstream call responsible for tail latency.

Continuous Profiling of Hot Paths

Backend teams use Google Cloud Profiler or Datadog Continuous Profiler to identify CPU and memory hotspots in running services without rebuilding or redeploying.

Scroll for all 7

Integrations 8

Pre-built integrations with other platforms and tools.

k6

Open-source Grafana Labs load testing tool that uses JavaScript test scripts to drive load against HTTP, gRPC, and WebSocket APIs, with optional k6 Cloud for distributed runs.

Apache JMeter

Open-source Apache Software Foundation load testing tool for HTTP, REST, databases, JMS, and more, widely used for protocol-level performance testing.

Gatling

High-throughput, Scala-based open-source load testing tool with code-as-test scenarios and detailed HTML reports.

Artillery

Modern Node.js load testing toolkit with YAML and JavaScript scenarios, designed for serverless and Kubernetes-native workloads.

StormForge

Performance and load testing platform that uses machine learning to optimize Kubernetes resource configurations and validate scalability.

Datadog

Observability and APM platform offering distributed tracing, real user monitoring, synthetic monitoring, and continuous profiling.

New Relic

Full-stack observability platform with APM, browser-based RUM, synthetic monitoring, distributed tracing, and infrastructure metrics.

OpenTelemetry

Vendor-neutral CNCF standard for collecting traces, metrics, and logs, used to instrument applications for any compatible APM or tracing backend.

Scroll for all 8

Resources

Get Started 1

Portal, sign-up, and the first successful call

Portal

Portal

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Source (apis.yml)

aid: performance
name: Performance
description: An index and topic collection covering API and web performance, including load testing, performance benchmarking,
  real user monitoring (RUM), Core Web Vitals measurement, latency profiling, distributed tracing, and application performance
  monitoring (APM). Performance engineering ensures that APIs and web applications meet latency, throughput, and reliability
  expectations under realistic and adversarial load. This collection brings together open-source load generators like k6,
  Apache JMeter, Locust, Gatling, and Artillery; managed load and chaos platforms like StormForge and Speedscale; synthetic
  and real-user measurement tools like Google PageSpeed, Pingdom, and Akamai; and APM and tracing platforms like Datadog,
  New Relic, Dynatrace, AppDynamics, Sentry, Honeycomb, Instana, Lightstep, SigNoz, Uptrace, OpenTelemetry, Jaeger, Grafana
  Tempo, Google Cloud Profiler, Google Cloud Trace, and AWS X-Ray.
type: Index
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/apis-json-logo.jpg
tags:
- Performance
- Load Testing
- Performance Testing
- Real User Monitoring
- Core Web Vitals
- APM
- Distributed Tracing
- Latency
url: https://raw.githubusercontent.com/api-evangelist/performance/refs/heads/main/apis.yml
created: '2026-05-19'
modified: '2026-05-19'
specificationVersion: '0.19'
apis: []
network:
- name: k6
  url: https://raw.githubusercontent.com/api-evangelist/k6/refs/heads/main/apis.yml
- name: Apache JMeter
  url: https://raw.githubusercontent.com/api-evangelist/apache-jmeter/refs/heads/main/apis.yml
- name: Locust
  url: https://raw.githubusercontent.com/api-evangelist/locust/refs/heads/main/apis.yml
- name: Gatling
  url: https://raw.githubusercontent.com/api-evangelist/gatling/refs/heads/main/apis.yml
- name: Artillery
  url: https://raw.githubusercontent.com/api-evangelist/artillery/refs/heads/main/apis.yml
- name: StormForge
  url: https://raw.githubusercontent.com/api-evangelist/stormforge/refs/heads/main/apis.yml
- name: Speedscale
  url: https://raw.githubusercontent.com/api-evangelist/speedscale/refs/heads/main/apis.yml
- name: GoReplay
  url: https://raw.githubusercontent.com/api-evangelist/goreplay/refs/heads/main/apis.yml
- name: Google PageSpeed
  url: https://raw.githubusercontent.com/api-evangelist/google-pagespeed/refs/heads/main/apis.yml
- name: Pingdom
  url: https://raw.githubusercontent.com/api-evangelist/pingdom/refs/heads/main/apis.yml
- name: Akamai
  url: https://raw.githubusercontent.com/api-evangelist/akamai/refs/heads/main/apis.yml
- name: Datadog
  url: https://raw.githubusercontent.com/api-evangelist/datadog/refs/heads/main/apis.yml
- name: New Relic
  url: https://raw.githubusercontent.com/api-evangelist/new-relic/refs/heads/main/apis.yml
- name: Dynatrace
  url: https://raw.githubusercontent.com/api-evangelist/dynatrace/refs/heads/main/apis.yml
- name: AppDynamics
  url: https://raw.githubusercontent.com/api-evangelist/appdynamics/refs/heads/main/apis.yml
- name: Instana
  url: https://raw.githubusercontent.com/api-evangelist/instana/refs/heads/main/apis.yml
- name: Sentry
  url: https://raw.githubusercontent.com/api-evangelist/sentry/refs/heads/main/apis.yml
- name: Honeycomb
  url: https://raw.githubusercontent.com/api-evangelist/honeycomb/refs/heads/main/apis.yml
- name: Lightstep
  url: https://raw.githubusercontent.com/api-evangelist/lightstep/refs/heads/main/apis.yml
- name: SigNoz
  url: https://raw.githubusercontent.com/api-evangelist/signoz/refs/heads/main/apis.yml
- name: Uptrace
  url: https://raw.githubusercontent.com/api-evangelist/uptrace/refs/heads/main/apis.yml
- name: OpenTelemetry
  url: https://raw.githubusercontent.com/api-evangelist/opentelemetry/refs/heads/main/apis.yml
- name: Jaeger
  url: https://raw.githubusercontent.com/api-evangelist/jaeger/refs/heads/main/apis.yml
- name: Grafana Tempo
  url: https://raw.githubusercontent.com/api-evangelist/grafana-tempo/refs/heads/main/apis.yml
- name: Google Cloud Profiler
  url: https://raw.githubusercontent.com/api-evangelist/google-cloud-profiler/refs/heads/main/apis.yml
- name: Google Cloud Trace
  url: https://raw.githubusercontent.com/api-evangelist/google-cloud-trace/refs/heads/main/apis.yml
- name: AWS X-Ray
  url: https://raw.githubusercontent.com/api-evangelist/aws-x-ray/refs/heads/main/apis.yml
- name: Grafana
  url: https://raw.githubusercontent.com/api-evangelist/grafana/refs/heads/main/apis.yml
common:
- type: Portal
  url: https://apievangelist.com
- type: GitHubOrganization
  url: https://github.com/api-evangelist
- type: Features
  data:
  - name: Load and Stress Testing
    description: Open-source and managed load generators like k6, Apache JMeter, Locust, Gatling, and Artillery simulate concurrent
      users and traffic patterns against APIs to characterize throughput, error rate, and latency under load.
  - name: Performance Benchmarking
    description: Repeatable, scripted benchmark runs establish performance baselines and detect regressions in CI/CD pipelines
      using tools like k6 Cloud, BlazeMeter, and StormForge.
  - name: Real User Monitoring (RUM)
    description: RUM products like Akamai mPulse, Datadog RUM, New Relic Browser, Sentry Performance, and Pingdom capture
      latency, errors, and Core Web Vitals from real browser sessions in production.
  - name: Synthetic Monitoring and Web Vitals
    description: Synthetic checks and lab-based audits from Google PageSpeed (Lighthouse), WebPageTest, and Pingdom measure
      Core Web Vitals (LCP, INP, CLS) and uptime from controlled environments.
  - name: Distributed Tracing
    description: OpenTelemetry, Jaeger, Grafana Tempo, Honeycomb, Lightstep, Google Cloud Trace, and AWS X-Ray collect distributed
      traces across microservice calls to attribute latency to specific spans.
  - name: Application Performance Monitoring (APM)
    description: APM platforms like Datadog APM, New Relic, Dynatrace, AppDynamics, Instana, SigNoz, and Uptrace correlate
      traces, metrics, and logs to surface slow transactions and code-level bottlenecks.
  - name: Profiling and Code Hotspots
    description: Continuous profilers like Google Cloud Profiler and Datadog Continuous Profiler identify CPU, memory, and
      lock contention hotspots in running services.
  - name: Traffic Replay and Chaos
    description: Tools like GoReplay and Speedscale capture production traffic and replay it against staging or new versions
      to validate performance and behavior before release.
- type: UseCases
  data:
  - name: Pre-Release Load Testing in CI/CD
    description: Engineering teams run k6 or JMeter scenarios on every pull request to verify that p95 latency and error rates
      stay within service-level objectives before merging.
  - name: Core Web Vitals Optimization
    description: Web teams use Google PageSpeed, WebPageTest, and RUM data to optimize LCP, INP, and CLS to meet Google ranking
      and user-experience thresholds.
  - name: Capacity Planning and Scalability Validation
    description: Platform teams use StormForge and BlazeMeter to drive sustained load tests against staging environments to
      determine maximum sustainable throughput and right-size infrastructure.
  - name: Latency Regression Detection
    description: APM platforms like Datadog, New Relic, and Dynatrace alert on p95 and p99 latency regressions per endpoint
      or trace span after each deploy.
  - name: Production Traffic Replay
    description: Teams use GoReplay or Speedscale to record real production traffic and replay it against release candidates
      to find performance and correctness regressions before rollout.
  - name: Distributed Trace Root-Cause Analysis
    description: SREs use Honeycomb, Lightstep, Jaeger, or Grafana Tempo to drill into slow distributed traces and pinpoint
      the service, query, or downstream call responsible for tail latency.
  - name: Continuous Profiling of Hot Paths
    description: Backend teams use Google Cloud Profiler or Datadog Continuous Profiler to identify CPU and memory hotspots
      in running services without rebuilding or redeploying.
- type: Integrations
  data:
  - name: k6
    description: Open-source Grafana Labs load testing tool that uses JavaScript test scripts to drive load against HTTP,
      gRPC, and WebSocket APIs, with optional k6 Cloud for distributed runs.
  - name: Apache JMeter
    description: Open-source Apache Software Foundation load testing tool for HTTP, REST, databases, JMS, and more, widely
      used for protocol-level performance testing.
  - name: Gatling
    description: High-throughput, Scala-based open-source load testing tool with code-as-test scenarios and detailed HTML
      reports.
  - name: Artillery
    description: Modern Node.js load testing toolkit with YAML and JavaScript scenarios, designed for serverless and Kubernetes-native
      workloads.
  - name: StormForge
    description: Performance and load testing platform that uses machine learning to optimize Kubernetes resource configurations
      and validate scalability.
  - name: Datadog
    description: Observability and APM platform offering distributed tracing, real user monitoring, synthetic monitoring,
      and continuous profiling.
  - name: New Relic
    description: Full-stack observability platform with APM, browser-based RUM, synthetic monitoring, distributed tracing,
      and infrastructure metrics.
  - name: OpenTelemetry
    description: Vendor-neutral CNCF standard for collecting traces, metrics, and logs, used to instrument applications for
      any compatible APM or tracing backend.
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
humanURL: https://apievangelist.com

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