Client loginBook a call
Build
Custom Software DevelopmentSystems built to your process
Web Application DevelopmentReact, Next.js, Rails, Django
Mobile App DevelopmentiOS, Android, React Native
App DevelopmentWeb and mobile apps, end to end
SaaS Product DevelopmentMulti-tenant from day one
MVP DevelopmentFirst release in 6–10 weeks
Enterprise SoftwareSSO, audit trails, scale
Connect & operate
API Development & IntegrationREST, GraphQL, webhooks
Cloud & DevOps EngineeringAWS, GCP, Azure, Terraform
QA & Software TestingAutomated suites, load, E2E
Legacy ModernizationStrangler-fig migrations
Cybersecurity EngineeringHardening and remediation
Data EngineeringPipelines, warehouses, reporting
Design & teams
UI/UX DesignInterfaces people finish tasks in
Prototyping & DiscoveryScope before you spend
Staff AugmentationSenior engineers on your team
Dedicated Dev TeamsA full pod, one contract
White-Label DevelopmentEngineering under your brand
Technology ArchitectureDecide once, correctly
Project ManagementDelivery you can audit
Start hereScope a build in one callBring a spec, a wireframe, or a paragraph. You leave with an architecture, a timeline, and a number.Book a scoping call
All software development services →How we price work →Talk to an engineer →
AI software
AI Application DevelopmentFull-stack AI products
AI Agent DevelopmentMulti-step autonomous agents
AI SaaS DevelopmentMulti-tenant AI products
Workflow AutomationReplace manual ops
Custom LLM AppsBranded internal assistants
LLM & data systems
Private LLM DevelopmentSelf-hosted Llama, Mistral, Qwen
RAG DevelopmentCited answers over your docs
MCP & WebMCPMake your systems agent-callable
Vector Database Setuppgvector, Pinecone, Qdrant
Document IntelligenceOCR and extraction pipelines
Vibe coding
AI-Assisted BuildsFast, with senior review
Rapid PrototypingClickable in days
Concept to CodeIdea to working software
Code Review & CleanupMake AI output shippable
Fractional AI TeamOngoing build capacity
Ready to ship?Put AI where the work isAgents, RAG, and private LLMs wired into the systems your team already uses — not a chatbot bolted to a homepage.Discuss an AI project
AI development services →AI readiness checklist →AI vs. traditional development →
Domain firstWe learn your workflow before we model itRegulated, operational, or high-volume — the constraints belong in the schema, not in a training doc.Talk about your domain
Selected workSoftware we own and operateMost agencies show screenshots. We run the products — the same stack, uptime, and on-call we hand to clients.View the portfolio
Plan smarterEstimate before you commitCost ranges, scope templates, and the questions we ask in discovery — free, no form.Open the cost calculator
Real conversationsTalk with a technical leadNo SDR, no discovery gauntlet. The person on the call is the one who scopes the build.Book a call
Open-Source Observability · flask-dashboard

Flask-MonitoringDashboard

Flask-MonitoringDashboard is a Python extension that adds automatic performance monitoring, request profiling, and exception tracking to Flask web applications. It provides a web-based dashboard showing endpoint performance metrics, execution bottlenecks, and outlier request details with minimal setup overhead.

Talk to DEV.coDiscuss Implementation
Source: GitHub — github.com/flask-dashboard/Flask-MonitoringDashboard
828
GitHub stars
166
Forks
Python
Primary language
MIT
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
Repositoryflask-dashboard/Flask-MonitoringDashboard
Ownerflask-dashboard
Primary languagePython
LicenseMIT — OSI-approved
Stars828
Forks166
Open issues74
Latest releasev3.0.7 (2020-02-28)
Last updated2026-06-29
Sourcehttps://github.com/flask-dashboard/Flask-MonitoringDashboard

What Flask-MonitoringDashboard is

A Flask middleware extension that intercepts requests, profiles execution paths, logs performance metrics to a database, detects outlier requests with stack traces, and surfaces findings via a dashboard UI. Supports git-based version tracking of performance evolution and manual exception capture via dashboard.capture().

Quickstart

Get the Flask-MonitoringDashboard source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/flask-dashboard/Flask-MonitoringDashboard.gitcd Flask-MonitoringDashboard# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Performance Debugging in Development

Quickly identify slow endpoints and CPU-intensive functions without manual profiling. Stack traces on outlier requests pinpoint exact bottlenecks in request handling.

Production Monitoring for Small-to-Medium Flask Apps

Track endpoint throughput, latency trends, and exception rates over time. Lightweight addition to existing Flask code with zero endpoint changes required.

Pre-Deployment Optimization

Compare performance across git versions to validate whether refactors improve latency. Collect database query counts and custom metrics (e.g., user registration rate) per time interval.

Implementation considerations

  • Call dashboard.bind(app) after all endpoints are registered; early binding may miss routes. Requires a persistent database (SQLite by default) to store metrics.
  • Overhead scales with request volume; high-traffic endpoints will accumulate large profile datasets. Plan database capacity and retention policies.
  • Custom metrics (e.g., 'SELECT COUNT(*) FROM users') require manual configuration; not auto-discovered from codebase.
  • Latest release (v3.0.7) is from Feb 2020; last commit June 2026 suggests ongoing maintenance but no version bump in ~6 years. Verify compatibility with your Flask and Python versions.
  • Exception capture via dashboard.capture() requires code instrumentation of try/except blocks; not fully automatic for all exceptions.

When to avoid it — and what to weigh

  • High-Scale Distributed Systems — Built for single-instance or small-cluster Flask apps. No sharding, cross-process aggregation, or distributed tracing support. Not suitable for 100+ node deployments.
  • Real-Time Alerting Requirements — Dashboard is pull-based; no push notifications, webhooks, or integration with incident management tools (PagerDuty, Slack). Designed for retrospective analysis, not active alerting.
  • Strict Performance SLAs with Low Overhead Budget — Profiling and request tracing add measurable overhead (CPU, I/O, memory for DB storage). Unsuitable if you require sub-millisecond latency guarantees or cannot tolerate per-request instrumentation cost.
  • Complex Authentication or Authorization — Dashboard access control appears basic (username/password in README). No mention of LDAP, OAuth, role-based access, or multi-tenant isolation for large teams.

License & commercial use

MIT License. Permissive OSI-approved open-source license allowing commercial use, modification, and distribution with attribution and no liability.

MIT license permits commercial deployment. However, provided as-is with no warranty or SLA. For production use, evaluate your organization's tolerance for community-maintained software (no official support). Verify compatibility with your Flask/Python stack and test thoroughly before critical deployments.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceModerate
DocumentationAdequate
License clarityClear
Deployment complexityLow
DEV.co fitGood
Assessment confidenceMedium
Security considerations

Dashboard adds a web UI endpoint (/dashboard) to your app; ensure it is protected behind authentication and HTTPS in production. Profiling and exception capture may expose sensitive data in stack traces, request values, and headers—review what is logged. No mention of PII masking, encryption at rest, or access controls beyond basic credentials. Verify stored profiling data does not breach compliance (PCI, GDPR, HIPAA) before storing in production databases.

Alternatives to consider

New Relic Python Agent

Commercial APM with automatic instrumentation, alerting, and distributed tracing. Higher cost but enterprise support, scalability, and integration with 1000+ services.

Datadog APM

Cloud-native monitoring with real-time dashboards, alerting, and log aggregation. Requires agent deployment but handles high-scale and multi-service architectures.

Prometheus + Grafana

Open-source metrics and visualization stack. Requires more setup (exporter, time-series DB, Grafana UI) but highly flexible and widely adopted for containerized deployments.

Software development agency

Build on Flask-MonitoringDashboard with DEV.co software developers

Install Flask-MonitoringDashboard with pip and add one line of code to start tracking performance, profiling requests, and debugging bottlenecks—no endpoint changes needed.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

monitorordjango-prometheusAppMetrics

Related on DEV.co

Explore the category and the services that help you build with it.

Open-Source ObservabilityWeb App DevelopmentDevOpsTalk to DEV.co

Flask-MonitoringDashboard FAQ

Does Flask-MonitoringDashboard require code changes to my endpoints?
No. You only add one line (dashboard.bind(app)) after defining routes. All profiling and monitoring is automatic via middleware.
What database does it use?
SQLite by default (file-based, no external service needed). Configurable to support other databases. Check documentation for supported backends.
Can I use it in production?
Yes, but evaluate the profiling overhead and ensure the dashboard endpoint is protected by authentication. Not recommended if you have strict latency SLAs or very high request volume without scaling the database.
Is it actively maintained?
Unknown. Last release is v3.0.7 (Feb 2020), but recent git activity (June 2026) suggests ongoing patches. No published roadmap or version plan visible. Test with your Flask/Python versions before committing to production.

Custom software development, end to end

Standing up Flask-MonitoringDashboard properly takes more than a deploy. As a software development agency, DEV.co handles the open-source observability architecture, the web development on top, and the operational work underneath.

Ready to Monitor Your Flask App?

Install Flask-MonitoringDashboard with pip and add one line of code to start tracking performance, profiling requests, and debugging bottlenecks—no endpoint changes needed.

Talk to DEV.coSchedule a Consultation