Choose Bolt.diy if your priority is open-source control, local setup, model choice, and long-term code ownership. Choose Bolt.new if you want the fastest hosted experience with less setup and tighter integration in the browser. For teams building serious AI-assisted apps, the decision is less about which tool feels flashier and more about who controls the runtime, data, model access, and deployment path.
TLDR: Bolt.diy is the better fit for open-source AI app development when you need self-hosting, provider flexibility, and transparent customization. Bolt.new is better for quick prototypes, demos, and teams that do not want to manage local infrastructure. For example, a three-person startup building a customer portal could use Bolt.new to create a first working prototype in one afternoon, then move to Bolt.diy once private API keys, custom prompts, and internal code policies become serious concerns. In practice, saving even 20 to 30 minutes per setup session matters when developers repeat the workflow several times per week.
Bolt.diy and Bolt.new solve similar problems, but for different users
Both tools aim to turn natural language prompts into working web applications. You describe what you want, the AI writes code, edits files, installs packages, and helps debug errors. That is the shared promise.
The difference sits under the surface. Bolt.new is the polished hosted product from StackBlitz. It runs in the browser, uses WebContainers, and is built for speed. You open the site, type a prompt, and start building. There is little friction.
Bolt.diy is the open-source option. It gives developers more control over the environment, AI provider, configuration, and source code. It is often used by people who want the Bolt-style workflow without being locked into one hosted service or one pricing model.
Where Bolt.new is stronger
Bolt.new is hard to beat for speed. If you need a React app, landing page, dashboard, or proof of concept, it can be very effective. The upload-free browser workflow feels clean. There is no local dependency mess. No broken Node version. No “why is this package failing on my machine” spiral.
Its main strengths are clear:
- Fast start: no local installation for basic use.
- Clean interface: good for non-specialists and founders.
- Browser execution: apps can run directly in the workspace.
- Good prototype flow: suitable for quick UI work and demos.
- Managed platform: fewer setup decisions for the user.
That makes Bolt.new useful for product managers, indie hackers, designers, and developers who want to test an idea before turning it into a real codebase. If the goal is a clickable demo by Friday, Bolt.new is often the practical choice.
The catch is that convenience has limits. Hosted tools usually come with account rules, usage caps, provider choices, and limited control over internals. That may be fine for a weekend project. It can be a problem for a company with compliance rules or a team that wants repeatable development environments.
Where Bolt.diy is stronger
Bolt.diy is more attractive when control matters. Since it is open source, developers can inspect the code, change behavior, connect different AI models, and run it in their own environment. That is not just a philosophical win. It affects cost, security, and maintainability.
For open-source AI app development, Bolt.diy has several clear advantages:
- Self-hosting: run it locally or on your own infrastructure.
- Model choice: connect supported providers instead of relying on one default path.
- Code transparency: inspect how the tool works and modify it.
- Team policy control: adapt it to internal rules for keys, prompts, logs, and repositories.
- Lower vendor risk: avoid building your workflow around a single hosted service.
This matters for teams working on private code. A company may not want proprietary prompts, database schemas, or unreleased product logic moving through a hosted workflow. Even when a provider has reasonable policies, legal and security teams may still object. Bolt.diy gives technical teams a stronger case because the system can be contained and audited.
The model question is a big deal
AI coding tools live or die by model quality. A weak model creates messy code, fake fixes, and broken assumptions. A strong model can save hours. Bolt.new hides more of that complexity from the user, which is useful for beginners. Bolt.diy exposes more of it, which is useful for advanced teams.
With Bolt.diy, teams can test different providers and compare outputs. One model may be better at TypeScript. Another may be cheaper for repeated edits. Another may work better for backend tasks. This flexibility is valuable when a team runs hundreds of prompts per month.
Honestly, it feels wasteful when a tool forces every task through the same model. A small CSS tweak does not need the same reasoning power as a database refactor. Bolt.diy gives teams more room to match cost to task.
Cost and usage control
Bolt.new is simple to start, but hosted AI usage can become expensive as work increases. The exact cost depends on current pricing, limits, and provider arrangements, so teams should check live terms before making a decision. Still, the pattern is familiar: prototypes feel cheap, then daily usage grows.
Bolt.diy can reduce surprise costs because teams can bring their own model keys, set internal limits, and monitor usage directly. This does not mean it is automatically cheaper. You still pay for tokens, hosting, and developer time. But the spending is easier to measure and tune.
For example, a small agency generating 40 client prototypes per month may prefer Bolt.new for the first draft. Yet the same agency may use Bolt.diy for production-bound work where each project needs a private repository, repeatable prompts, and controlled API access.
Security and privacy concerns
Security is where Bolt.diy often wins for serious teams. Self-hosting allows stricter handling of secrets, environment variables, source files, and logs. That can be the deciding factor in sectors such as finance, healthcare, legal services, or enterprise software.
Bolt.new is not “unsafe” by default. It is a managed service, and many teams will find that acceptable. But managed services require trust in external systems. Bolt.diy allows a team to reduce that trust requirement by running more of the workflow under its own control.
Developer experience: simple versus adjustable
Bolt.new feels smoother at the start. Bolt.diy feels better once a team wants to adjust details. That is the trade.
Expect to spend time on setup with Bolt.diy. Local dependencies, provider keys, environment configuration, and updates may take longer than expected. It drives me crazy when a small configuration issue burns 15 minutes before real work begins. Still, that setup cost can pay off if the tool becomes part of a stable team workflow.
Bolt.new avoids much of that early pain. For a first-time user, that matters. The browser-first experience lowers the barrier and makes AI app generation feel immediate.
Which should you choose?
Use Bolt.new if you need speed, simplicity, and a polished hosted workflow. It is best for prototypes, learning, demos, hackathons, and quick product experiments.
Use Bolt.diy if you care about open-source control, self-hosting, model choice, and private development workflows. It is better for technical teams that expect to customize the tool and use it across many projects.
The safest path for many teams is mixed. Start in Bolt.new when speed matters. Move to Bolt.diy when the project becomes serious, private, or cost-sensitive. That split keeps early work fast while giving production work the control it deserves.
Bottom line: Bolt.new is the cleaner front door. Bolt.diy is the stronger workshop. If open-source AI app development is the goal, Bolt.diy is the more flexible and durable choice.