Streamlit vs Grafana: When to Use Each

I was on a call with a customer last week when they asked: "Should we use Streamlit or Grafana for our new dashboard?"

I paused for a second, because honestly, that's like asking "Should I get a truck or a sports car?" The answer is "depends what you're hauling," but also... why are these two even in the same conversation?

Here's the thing: Streamlit and Grafana keep getting compared on Reddit, in Slack channels, and in team discussions, even though they're fundamentally different tools. And after watching dozens of teams wrestle with this decision, I think I finally understand why the confusion exists—and more importantly, how to actually choose.

Why the confusion exists

People aren't confused because they're clueless. They're confused because both tools can technically display charts and data. That surface-level similarity obscures a much deeper difference in purpose, architecture, and ideal use case.

It's like how people compare Excel and SQL databases because "both store data in tables." Technically true, but... come on.

What Grafana actually is (and what it's brilliant at)

Grafana started life as a monitoring and observability tool. It was designed to answer questions like: "Is my server on fire right now?" and "Why did response times spike at 3am?"

The core strength of Grafana is time-series visualization. It plugs into Prometheus, InfluxDB, CloudWatch, Datadog, and dozens of other metrics systems. You get beautiful, real-time graphs showing CPU usage, request rates, error counts, database connections—all the operational signals that keep SRE teams sane.

Grafana dashboards refresh automatically (typically every few seconds), they support alerting based on thresholds, and they're optimized for always-on monitoring. They’re the dashboards you keep open in a tab all day.

The mental model is: "I need to know if something breaks, ideally before customers notice."

When I think of Grafana, I think of dashboards tracking uptime, error rates, latency percentiles, disk space, and memory pressure. It's the tool you reach for when you need to answer "Is the system healthy?" at a glance.

And for that use case? Grafana is exceptional. AWS even offers Amazon Managed Grafana as a first-party service, and Microsoft has Azure Managed Grafana. That's how mainstream this tool has become for ops and SRE workflows.

What Streamlit actually is (and why it feels so different)

Streamlit came from a completely different origin story. It was built for data scientists and analysts who needed to turn Python scripts into interactive web apps—fast.

Before Streamlit, if you wanted to share an analysis or an ML model with stakeholders, you basically had to ship a notebook, build a real web app, or give up and send screenshots.

Streamlit's genius was removing all that friction. You write Python, add a few st.slider() and st.dataframe() calls, and suddenly you have a web app with dropdowns, filters, charts, file uploads, and buttons. No JavaScript, no CSS, no deployment headaches.

The mental model is: "I built something useful in Python. Now I need to share it with people who don't write code."

I've seen Streamlit used for ML model demos, portfolio optimization tools, A/B test result explorers, financial scenario planners, internal admin panels, and even customer-facing data products. It's the tool you reach for when you need custom logic and interactivity, not just passive monitoring.

Streamlit apps are typically on-demand. A user visits the app, picks some parameters, and the app runs Python code in response to those inputs. It's not "always watching" like Grafana—it's "show me the answer when I ask."

Why people compare them (even though they shouldn't)

Here's where it gets interesting. Both tools can display charts. Both can connect to databases. Both can run inside Docker containers. And both show up when someone Googles "open source dashboard tool."

But the comparison usually emerges in one specific scenario: someone needs an internal dashboard and they're not sure which tool fits.

Let's say you're building a dashboard to track daily revenue, top customers, and conversion rates. Should you use Grafana or Streamlit?

The answer depends on a few key questions:

Do you need real-time monitoring or on-demand analysis? If you want a dashboard that's always visible and updates every few seconds (like a wall-mounted TV showing live metrics), Grafana makes sense. If you want something people pull up occasionally to explore data interactively, Streamlit fits better.

Is your data time-series metrics or business events? Grafana is optimized for time-series data—things that happen continuously and get aggregated over time windows. If you're querying PostgreSQL tables or running custom Python logic to reshape data, Streamlit is more natural.

Do you need custom interactivity? Grafana has variables and templating, but it's limited. You can't write Python logic, run ML models, or build complex forms. Streamlit gives you the full power of Python—pandas, scikit-learn, requests, whatever you need.

Who's maintaining this? Grafana is configured through JSON or the UI. Non-developers can tweak dashboards without touching code. Streamlit apps are Python scripts—you need someone comfortable with code to maintain them.

When I actually recommend Grafana

I tell teams to use Grafana when they're building operational dashboards for monitoring infrastructure, application health, or system performance.

Examples where Grafana shines: tracking API latency and errors, watching DB health, monitoring Kubernetes, and alerting when metrics cross thresholds.

If the primary goal is "stay aware of system health" and the data is coming from metrics systems like Prometheus, CloudWatch, or Datadog, Grafana is hard to beat. It's mature, widely adopted, and purpose-built for this.

The Grafana documentation is excellent, and there are thousands of pre-built dashboards you can import for common tools (PostgreSQL, Redis, Kubernetes, etc.). You're not starting from scratch.

When I actually recommend Streamlit

I tell teams to use Streamlit when they're building custom data apps or interactive analysis tools.

Examples where Streamlit shines: internal tools with forms and controls, what-if planners, model demos, and “let me slice and dice this dataset” apps.

If the primary goal is "give non-technical users access to custom logic" or "turn analysis into a shareable app," Streamlit is the right call. It's Python all the way down, which means you can do literally anything you could do in a script—just with a UI wrapped around it.

The Streamlit documentation makes it incredibly easy to get started, and the community gallery has hundreds of example apps you can fork and adapt.

The pricing question (because it always comes up)

Both tools are open source, which is great—until you need to deploy them for a team and suddenly you're managing Docker containers, authentication, and SSL certificates.

Grafana Cloud exists, and it's a solid managed option if you're already in the Grafana ecosystem. Pricing scales with metrics volume and users, and for teams already using Prometheus or Loki, it's a natural fit.

For Streamlit, there's Streamlit Community Cloud, which is great for quick demos and sharing. For production use cases, teams either self-host or use a managed option that fits their security and reliability requirements. If you self-host, you're dealing with container orchestration, secrets management, WebSocket-friendly networking (Streamlit relies on WebSockets), and user authentication yourself.

That's actually one of the reasons we built Fastero the way we did. We wanted to host Streamlit apps for teams without forcing them to become Kubernetes experts—but also connect those apps to event-driven triggers so they could update automatically when data changed, not just when someone clicked a button. More on that in a second.

The hybrid reality (because real projects are messy)

Here's the honest truth: most teams I talk to end up using both.

They'll have Grafana dashboards monitoring their infrastructure and application metrics, tracked by the SRE team. And they'll have Streamlit apps for internal tools—a pricing calculator for the sales team, a fraud detection model interface for the ops team, a forecast adjustment tool for the finance team.

The tools serve different needs. Grafana is reactive monitoring (tell me when something breaks). Streamlit is proactive exploration (let me dig into this question).

Where things get messy is authentication, deployment, and the inevitable "Can we just have one tool?" question from leadership. Because maintaining two stacks (Grafana + its metrics backends, Streamlit + its hosting infrastructure) is real overhead for small teams.

How to build real-time dashboards with Streamlit (the missing piece)

Here’s where things get interesting. Traditional Streamlit apps are on-demand: someone visits the URL, the Python runs, charts render, done. If the underlying data changes, the user typically refreshes the page to see it.

That works fine for interactive exploration tools, but it falls short when you need always-current dashboards like Grafana provides. And that's the gap most teams hit: "I want Streamlit's flexibility, but I need Grafana's real-time refresh."

The solution isn't choosing one or the other. It's making Streamlit apps event-driven.

At Fastero, we built hosted Streamlit with triggers baked in. The pattern is simple: you deploy a normal Streamlit app, then you connect it to triggers. Those triggers can fire on database changes (CDC), webhooks, Kafka events, dbt runs, or schedules.

When a trigger fires, Fastero forces a rerun by updating (touching) the app’s files inside the running container. Streamlit’s built-in file watcher detects that change and reruns the app. The goal is simple: you don’t have to teach everyone to smash refresh, and you don’t have to build your own polling loop.

From a user perspective, it feels like a live dashboard. From a developer perspective, it’s just Python plus “when this data changes, rerun the app.”

To make this concrete, here’s a tiny end-to-end pattern that’s hard to get wrong: a webhook handler inserts rows into a table, and a Streamlit app queries that same table. A trigger on that table is what makes the app rerun.

This example assumes Postgres for the SQL function names (date_trunc, interval). If you’re on a different database, you’d tweak the SQL. It also assumes a simple table like this:

-- Example schema (Postgres)
create table if not exists webhook_events (
  id bigserial primary key,
  received_at timestamptz not null default now(),
  source text not null,
  event_type text,
  payload_text text not null
);

# app.py
import os
import pandas as pd
import streamlit as st
from sqlalchemy import create_engine, text
 
st.set_page_config(page_title="Revenue (Live)", layout="wide")
st.title("Webhook Events (Live)")
 
engine = create_engine(os.environ["DATABASE_URL"])
 
def load_events():
  sql = text("""
    select
      date_trunc('hour', received_at) as hour,
      coalesce(event_type, 'unknown') as event_type,
      count(*) as events
    from webhook_events
    where received_at >= now() - interval '24 hours'
    group by 1, 2
    order by 1, 2
  """)
  with engine.connect() as conn:
    return pd.read_sql(sql, conn)
 
df = load_events()
 
left, right = st.columns([1, 2])
left.metric("Events (24h)", f"{int(df['events'].sum()):,}")
right.line_chart(df.pivot(index="hour", columns="event_type", values="events").fillna(0))
st.caption("Hosted normally, this updates when someone reloads. Hosted with a trigger, it reruns when data changes.")

If you host that app “plain,” it updates when someone reloads. If you host it on Fastero and attach a trigger to it, you get a different behavior: the app reruns when the trigger fires, so the dashboard stays current without relying on manual refresh.

One pattern I like is “CDC drives the reruns.” You set the app to rerun whenever a table changes, and the Streamlit code stays completely ordinary.

That’s the whole loop: webhook → table write → trigger → Streamlit rerun → dashboard reflects the new data.

If you want a minimal local version of the “incoming events” side, here’s the smallest thing that resembles it. This webhook handler inserts into webhook_events. (In a real Stripe integration you should verify the signature; this is intentionally minimal.)

# webhook_receiver.py
import os
import json
from flask import Flask, request
from sqlalchemy import create_engine, text
 
app = Flask(__name__)
engine = create_engine(os.environ["DATABASE_URL"])
 
@app.route("/stripe/webhook", methods=["POST"])
def stripe_webhook():
    payload_text = request.get_data(as_text=True)
    event = json.loads(payload_text)
 
    with engine.begin() as conn:
        conn.execute(
            text(
                "insert into webhook_events (source, event_type, payload_text) values (:s, :t, :p)"
            ),
            {"s": "stripe", "t": event.get("type"), "p": payload_text},
        )
 
    return ("ok", 200)

On Fastero, the missing piece is the trigger wiring: “when webhook_events changes, rerun the Streamlit app.” The webhook handler doesn’t “push” into Streamlit directly. It updates data, and the trigger is what causes the rerun.

Under the hood, Fastero handles the messy stuff: secure hosting, WebSocket-friendly routing for Streamlit, container lifecycle, file sync, org/project RBAC, and reruns driven by triggers instead of cron + refresh buttons.

This gives you Grafana’s “always-current” feel, but with Streamlit’s full Python stack: forms, custom business logic, ML inference, API calls, and anything else you can import.

Why this comparison keeps coming up (and what we built)

This comparison keeps coming up for a simple reason: teams often need both real-time monitoring (Grafana's strength) and custom interactive apps (Streamlit's strength), but they don't want to manage two completely separate stacks.

That's why the event-driven Streamlit pattern described above exists. It closes the gap between "always watching" and "on-demand exploration." You get Grafana-like refresh behavior without giving up Python's flexibility.

We also added enterprise-grade security (SSO, RBAC, audit logs), WebSocket-preserving proxies (so interactive widgets don't break during reloads), and project-scoped isolation. You don't need Kubernetes expertise or a dedicated DevOps person to deploy Streamlit apps anymore. You just write Python, configure triggers, and deploy.

And because Fastero also has native dashboards (which work more like Grafana's passive monitoring model), you can have both patterns in one platform. Use dashboards for SRE-style monitoring. Use event-driven Streamlit apps for custom data tools. Same authentication, same deployment pipeline, same trigger architecture underneath.

You can read more about our approach on the Hosted Streamlit Apps page.

The comparison table (because you probably skipped to this)

I know how this works. You skimmed the whole article looking for "the table." So here it is—an honest side-by-side:

What I'd actually do if I were starting fresh today

If I were building something new and had to choose, here's my honest decision tree:

If my goal is monitoring a production system (APIs, databases, servers), I'd use Grafana. I'd either run Grafana Cloud or Amazon Managed Grafana so I don't have to manage it myself. I'd pair it with Prometheus for metrics and Loki for logs. This is the standard stack for a reason—it works.

If my goal is building internal tools or data apps, I'd use Streamlit. But I wouldn't self-host it unless I had a dedicated DevOps person. I'd either use a managed option (Fastero, Streamlit in Snowflake, or Streamlit’s enterprise offering) or accept the limitations of Community Cloud for early prototypes.

If I needed both operational dashboards and custom data apps, I'd evaluate whether I have the team bandwidth to manage two tools. If yes, Grafana + Streamlit (managed versions). If no, I'd look for a unified platform that handles both use cases—which is exactly why we built Fastero the way we did.

The real question isn't "which tool"—it's "what problem am I solving?"

This whole comparison only matters if you're clear on what you're actually building.

Are you trying to make sure your engineering team knows when production breaks? Grafana.

Are you trying to give your sales team a tool to run custom pricing scenarios? Streamlit.

Are you trying to visualize time-series metrics from CloudWatch or Prometheus? Grafana.

Are you trying to build an ML model interface where users can upload data and get predictions? Streamlit.

The confusion happens when someone says "I need a dashboard" without clarifying what kind of dashboard. That's the real question.

Try it yourself (and see what fits)

Both tools are open source and easy to experiment with.

For Grafana, the quickest way to get started is Grafana Cloud's free tier—no installation needed, just connect a data source and start building.

For Streamlit, you can run pip install streamlit and have an app running locally in 5 minutes. The Streamlit docs are genuinely excellent.

And if you're curious about event-driven Streamlit apps (the kind that refresh automatically when data changes, not when someone clicks a button), you can try Fastero's approach for free:

👉 Start your free 30-day trial (no credit card required)

Or if you just want to see how we handle Streamlit hosting with real-time updates:

💬 Book a demo

Last updated: December 2025. Both Grafana and Streamlit evolve quickly—always check their official docs for the latest features.