Self-hosted vs SaaS project management is a trade between control and convenience. SaaS tools run in the vendor's cloud, so you get no servers to manage but no say over where data lives or how pricing grows. Self-hosted tools run on your own server, so you own the data, the costs and the upgrades. With AI now reading every project, where that data lives matters more than it used to.
Key takeaways
- SaaS buys convenience. Self-hosting buys control. Neither is free of cost.
- With AI in your project tool, your data location decides your AI risk.
- Per-seat pricing punishes growth. Server costs mostly do not.
- Self-hosting needs an owner for backups and upgrades. Name them first.
- You can run your own AI model too, so nothing leaves your network.
The core difference
| SaaS | Self-hosted | |
|---|---|---|
| Where it runs | Vendor's cloud | Your server or cloud account |
| Who upgrades | Vendor | You, on your schedule |
| Pricing | Usually per seat, per month | Your server and storage costs |
| Data location | Vendor decides | You decide |
| Customisation | Settings only | Settings, plus the code if open source |
| AI model | Vendor's choice | Your choice, including on-premise |
| Setup time | Minutes | Minutes to hours, depending on your setup |
Neither column wins outright. The right choice depends on your team, your data and who will look after it.
When SaaS makes sense
- You have no one to run a server, and no budget for help.
- Your data has no residency or confidentiality requirements.
- The team is small and stable, so per-seat cost stays modest.
- You want the vendor to own uptime entirely.
When self-hosting makes sense
- Client contracts or regulation require data on your infrastructure.
- Your team is growing, or you have many occasional users and guests.
- You need regulated or air-gapped installs.
- You want to change or extend the tool itself.
If a client asks "where does our data live?", self-hosting lets you answer with an address, not a policy page.
Why AI changes the question
Project tools used to store tasks. Now AI reads them, summarises them and acts on them. That raises two questions every team should ask:
Where does the AI send your data?
In a SaaS tool, the vendor chooses the model and the route. In AlianHub, only the model is external, and you choose it. You can pin a model per agent, follow a workspace routing policy, or run a self-hosted model such as Ollama or vLLM on your own GPU, so prompts never leave your network.
What may the AI do?
In AlianHub, the limits are part of the product. Agents cannot delete tasks, docs, comments or projects, remove members, change permissions or touch billing. These actions do not exist for agents at all. Wider changes wait in the AI Inbox, and every action goes into the audit log. See the AI controls page.
What running AlianHub involves
AlianHub is free and open source under AGPL-3.0, with no per-seat pricing. You can run it with Docker Compose (images on GHCR for amd64 and arm64) or from source with Node.js and MongoDB. The admin guide covers:
- Install and first run. The first account becomes the owner, the only one who sees Settings, Instance.
- HTTPS behind your reverse proxy.
- Upgrades. Settings, Instance, Upgrade shows the running version, the latest release and pending migrations.
- Backup and restore. Settings, Instance, Backups writes a .tar.gz archive you can restore on the same or a newer version.
- Health checks through a /health endpoint.
Tip: take a backup before every upgrade, and test a restore on a separate machine once. The upgrade page reminds you, and the backups page is one click away.
If you would rather not run it yourself, Aliansoftware offers paid help with hosting, customisation and support. Contact the team.
How AlianHub's AI helps self-hosted teams
Self-hosting no longer means giving up modern AI. AlianHub's AI features run against your own instance.
- Set your model. Point AI at a provider key or a self-hosted model like Ollama or vLLM, and set the workspace routing policy.
- Switch on what you need. Some features, such as team packs, workflows, connecting your AI by address and skills that read outside your workspace, are switched on by whoever runs the server.
- Add an agent. Open AI, then the Agents tab, choose an agent and set its autonomy level from L0 to L3.
- Set limits. Give it a daily action limit and a monthly spend cap.
- Connect personal AI apps. Each person opens AI, then Connect your AI to link Claude, ChatGPT or Claude Code. An owner approves the app under Settings, Agent clients.
- Review. Approve proposals in the AI Inbox, and check the Audit log.
More on the AI page.
A business scenario: winning a client that said no to the cloud
Picture an IT services firm bidding for a client with strict data rules. Their SaaS project tool is a blocker: the client will not allow project data in a third-party cloud.
The firm self-hosts AlianHub in its own environment, runs a self-hosted model for AI, and gives the client a Guest login to their project. A Reporter agent posts weekly digests, and every agent action sits in an audit log the client can be shown.
The firm can now say yes to clients it used to turn away. Its tool costs do not rise with each new contractor, and the AI saves the same reporting time it would in the cloud. Control over your data is not just a compliance box. It can be a reason clients pick you.
FAQ
Is self-hosted project management cheaper than SaaS?
Often, as teams grow, because there is no per-seat pricing. You still pay for a server, storage, AI model usage and someone's time for upkeep.
Is self-hosting less secure than SaaS?
Not necessarily. You control where data lives and who can reach it, but you also own patching and backups. AlianHub's admin guide covers HTTPS, upgrades and backups.
Can I use AI with a self-hosted project tool?
Yes. AlianHub lets you choose the model, including self-hosted options like Ollama or vLLM, with agents, approvals and an audit log built in.
How do I move from a SaaS tool?
AlianHub has importers for Jira, Trello and Monday, plus CSV or Excel import from ClickUp. See moving from ClickUp.
Weigh the trade-off with your real data, not a feature chart. Open the live demo, self-host a test copy for free, and share this comparison with whoever signs off on your tools.