Blog
No items found.
5
minutes

How to migrate your PostHog self-hosted to PostHog Cloud

PostHog is an open-source product analytics tool that we use at Qovery to improve the developer experience. PostHog is similar to famous proprietary product analytics tools like Mixpanel, Heap, Amplitude.
September 26, 2025
Romaric Philogène
CEO & Co-founder
Summary
Twitter icon
linkedin icon

At Qovery, we were using PostHog self-hosted for 8 months in production. It was running nicely on Qovery (yes, we deployed PostHog with Qovery 😎 #eatYourOwnDogFood), but we decided to move to the PostHog Cloud. Here are the two main reasons why we decided to make this move:

If you are not yet using it, give it a try with Qovery or PostHog Cloud version.
  1. To support the PostHog project: Because we love their product, keeping them by using their Cloud version makes complete sense to us.
  2. Stay focused on our business: Using the self-hosted version of PostHog requires you to spend time to maintain it. Meaning, you have to handle the upgrade yourself and make sure the service is up and running all day long.

How to migrate

Looking at their documentation, there is no guide to migrate from PostHog self-hosted to their Cloud version. I asked them the procedure on Slack, and Paolo from the PostHog team responded that it should not be too complicated to transfer the data by fetching the data from the PostHog source and pushing them to the PostHog destination via the web API.

Conversation with Paolo from Posthog on Slack

So the idea was to make a Python script to fetch the data from our self-hosted PostHog instance and forward the data to the PostHog Cloud version.

(Self-hosted PostHog) <--[fetch event data via web API]-- Python Script --[send event data via web API]--> (PostHog Cloud)

Before migrating

As I know that we have more than one million events to send, I notified the PostHog support team that we will migrate our self-hosted version. Just in case they have to adjust their infrastructure. Who knows :) Paolo (once again, he is everywhere 🙂) responded to me and was super proactive.

null
null

I encourage you always to keep informed the support team of service when you are about to migrate. They can help you. It was the case here with the PostHog support team.

We can migrate now

To migrate, I made a simple Python script using no external dependencies. Only HTTP requests, and that's it. Note: This script is compatible with Python 3.6+.

#!/usr/bin/env python
import copy
import time
import uuid
from urllib.parse import urlparse

import requests

# Your source PostHog instance
source_posthog_scheme_and_host = 'https://posthog.your-domain.tld'

# Generate a personal API key to read the data from your source PostHog instance
source_api_key = 'xxx'

# Your project API key provided by PostHog in your project settings
dest_project_api_key = 'xxx'


def is_valid(key: str, data: dict) -> bool:
"""
Helper function to check if the value is empty or none
:param key: data key to check
:param value: dict
:return: true if valid (not empty, None), false otherwise
"""
if key not in data or not data[key] or str(data[key]).strip().lower() == 'none':
return False

return True


def clean_source_data(results: [dict]) -> [dict]:
"""
Clean up data from PostHog source
Note: this function do not mutate `results`
:param results:
:return:
"""
_results = []
if not results:
return _results

distinct_id = 'distinct_id'

for result in results:
data = copy.deepcopy(result)
del data['id']

if not is_valid(distinct_id, data):
data[distinct_id] = str(uuid.uuid4())

if 'properties' in data:
properties = data['properties']
if not is_valid(distinct_id, properties):
# copy distinct_id value from the parent object
properties[distinct_id] = data[distinct_id]

_results.append(data)

return _results


def capture(data: [dict], count: int = 0):
"""
Function to send data to the dest PostHog instance
:param data:
:param count:
:return:
"""
res = requests.post('https://app.posthog.com/capture', json={'api_key': dest_project_api_key, 'batch': data},
headers={'Content-type': 'application/json'})

if res.status_code != 200:
if count >= 100:
print('retry exceeded')
exit(1)
time.sleep(3)
print('Retry sending data (status code: {}) to dest PostHog with data {}'.format(res.status_code, data))
return capture(data, count + 1)

return res


def get_source_data() -> dict:
headers = {'Authorization': 'Bearer {}'.format(source_api_key)}

url = '{}/api/event'.format(source_posthog_scheme_and_host)

with open('posthog_completed_urls', 'a') as f:
while 1:
query = urlparse(url).query
if query:
# PostHog return weird next URL with tons of 'before' params
url = '{}/api/event?before={}'.format(source_posthog_scheme_and_host, query.split('=')[-1])

res = requests.get(url, headers=headers)
if res.status_code == 200:
j_res = res.json()
_data = clean_source_data(j_res['results'])

yield _data

f.write(url + '\n')
url = j_res['next']
else:
print('Retry fetching events (status code: {}) from PostHog source with URL {}'.format(res.status_code, url))
time.sleep(3)


if __name__ == '__main__':
for data in get_source_data():
capture(data)
print('{} lines migrated'.format(len(data)))

print('ok')
Before running the migration script, I strongly recommend making your apps send the data to the PostHog Cloud instance before and waiting for the old instance to stop receiving new events.

Now it is time to:

  1. Copy this script.
  2. Change the value of the variables source_posthog_scheme_and_host, source_api_key, dest_project_api_key
  3. Run the script and wait until it is done 👌

Wrapping up

The migration went smoothly and took one day because we had more than 1 million events. We are super excited to use PostHog Cloud. It is fast and efficient for improving the developer experience on Qovery. Any questions about our usage of PostHog? Join our Discord to chat about it.

Resources:

  • PostHog: open-source product analytics tool.
  • Qovery: the simplest way to deploy your apps on AWS.
Share on :
Twitter icon
linkedin icon
Tired of fighting your Kubernetes platform?
Qovery provides a unified Kubernetes control plane for cluster provisioning, security, and deployments - giving you an enterprise-grade platform without the DIY overhead.
See it in action

Suggested articles

AI
Compliance
 minutes
Agentic AI infrastructure: moving beyond Copilots to autonomous operations

The shift from AI copilots to autonomous agents is redefining infrastructure requirements. Discover how to build secure, stateful, and compliant Agentic AI systems using Kubernetes, sandboxing, and observability while meeting EU AI Act standards

Mélanie Dallé
Senior Marketing Manager
Kubernetes
8
 minutes
The 2026 guide to Kubernetes management: master day-2 ops with agentic control

Effective Kubernetes management in 2026 demands a shift from manual cluster building to intent-based fleet orchestration. By implementing agentic automation on standard EKS, GKE, or AKS clusters, enterprises eliminate operational weight, prevent configuration drift, and proactively control cloud spend without vendor lock-in, enabling effective scaling across massive fleets.

Mélanie Dallé
Senior Marketing Manager
Kubernetes
 minutes
Building a single pane of glass for enterprise Kubernetes fleets

A Kubernetes single pane of glass is a centralized management layer that unifies visibility, access control, cost allocation, and policy enforcement across § cluster in an enterprise fleet for all cloud providers. It replaces the fragmented practice of switching between AWS, GCP, and Azure consoles to govern infrastructure, giving platform teams a single source of truth for multi-cloud Kubernetes operations.

Mélanie Dallé
Senior Marketing Manager
Kubernetes
 minutes
How to deploy a Docker container on Kubernetes (and why manual YAML fails at scale)

Deploying a Docker container on Kubernetes requires building an image, authenticating with a registry, writing YAML deployment manifests, configuring services, and executing kubectl commands. While necessary to understand, executing this manual workflow across thousands of clusters causes severe configuration drift. Enterprise platform teams use agentic platforms to automate the entire deployment lifecycle.

Mélanie Dallé
Senior Marketing Manager
Kubernetes
Terraform
 minutes
Managing Kubernetes deployment YAML across multi-cloud enterprise fleets

At enterprise scale, managing provider-specific Kubernetes YAML across multiple clouds creates crippling configuration drift and operational toil. By adopting an agentic Kubernetes management platform, infrastructure teams abstract cloud-specific configurations (like ingress controllers and storage classes) into a single, declarative intent that automatically reconciles across 1,000+ clusters.

Mélanie Dallé
Senior Marketing Manager
Kubernetes
Cloud
AI
FinOps
 minutes
GPU orchestration guide: How to auto-scale Kubernetes clusters and slash AI infrastructure costs

To stop GPU costs from destroying SaaS margins, teams must transition from static to consumption-based infrastructure by utilizing Karpenter for dynamic provisioning, maximizing hardware density with NVIDIA MIG, and leveraging Qovery to tie scaling directly to business metrics.

Mélanie Dallé
Senior Marketing Manager
Product
AI
Deployment
 minutes
Stop Guessing, Start Shipping. AI-Powered Deployment Troubleshooting

AI is helping developers write more code, faster than ever. But writing code is only half the story. What happens after? Building, deploying, debugging, scaling. That's where teams still lose hours.We're building Qovery for this era. Not just to deploy your code, but to make everything that comes after writing it just as fast.

Alessandro Carrano
Head of Product
AI
Developer Experience
Kubernetes
 minutes
MCP Server is the future of your team's incident’s response

Learn how to use the Model Context Protocol (MCP) to transform static runbooks into intelligent, real-time investigation tools for Kubernetes and cert-manager.

Romain Gérard
Staff Software Engineer

It’s time to change
the way you manage K8s

Turn Kubernetes into your strategic advantage with Qovery, automating the heavy lifting while you stay in control.