7 Best Vibe Coding Tools (Build Apps From Prompts)

If you spend any time in dev Twitter or AI Slack, you’ve probably heard people say they’re “just vibe coding” an app over the weekend.

In 2025, Collins Dictionary made it official, naming vibe coding its Word of the Year – defined as using AI to turn natural language into working code. It’s now in Wikipedia, IBM and Google Cloud docs.

Instead of writing every line of code, developers (and increasingly non-developers) describe what they want in plain language and let AI agents build, debug and refactor the app. Platforms like Emergent, Bolt.new, Replit, Lovable, Cursor and Google’s new Antigravity IDE are racing to own this “prompt-to-app” workflow.

On AceCloud, we’ve already covered the broad landscape in “Best AI Coding Tools & Agents”, where Cursor is our top pick for an agentic IDE for vibe coding.

This article zooms in on vibe coding specifically:

• What vibe coding actually is
• Where it works well and where it’s risky
• The best vibe coding tools for 2025
• How to run them safely on cloud infrastructure

What Is Vibe Coding?

The short version:

Vibe coding = describe the app in natural language, let AI write most of the code, then iterate by running and prompting rather than hand-editing everything.

More formally, vibe coding is an AI-assisted development technique where you:

• Explain what you want in plain language (chat, sometimes voice)
• Let a large language model (LLM) generate and modify the code
• Rely heavily on running the app and agent feedback to evaluate results
• Often don’t read or deeply understand every line of the generated code

Compared with traditional AI coding assistance (like classic autocomplete or pair-programming copilots), vibe coding pushes further:

• You move from “AI helps me type code” to “AI builds the app, I guide and test it.”

That’s why some definitions emphasise that if you do review and understand all the code, it’s just normal AI-assisted development, not “true” vibe coding.

Vibe Coding vs “Normal” AI Coding Tools

AI coding tools span everything from inline IDE copilots to repo-scale agents and security scanners.

Here’s how vibe coding differs from that broader category:

Aspect	Traditional AI Coding Assistant	Vibe Coding Tools / Agents
Main interface	Inline suggestions inside your editor	Chat/agent UI + sometimes a visual builder or special IDE
Who writes most code?	Human, with AI snippets	AI, with human prompts and feedback
Scope	Current file or small change	Full app or multi-file features
Evaluation style	Reading code + tests	Running the app + tests + high-level summaries
Best fit	Daily development on existing codebases	Prototyping, internal tools, “weekend projects,” MVPs
Risk if misused	Wrong suggestion in one place	Large, opaque changes; hidden bugs; security issues if unreviewed

Most of the tools below can be used responsibly (with tests and review) or in pure “vibe” mode where you trust the AI more than you probably should. The tools are the same -the workflow is what changes.

Where Vibe Coding Shines (and Where It Doesn’t)

Best use cases

Vibe coding is especially strong when you’re:

• Prototyping MVPs and internal tools
• Exploring UI ideas quickly (“Try a dashboard version”, “Add a mobile view”)
• Building non-critical apps for personal use, side projects or short-lived campaigns
• Letting non-developers (PMs, ops, marketers, founders) turn ideas into working software

In this mode, tools like Emergent, Bolt.new, Lovable or Replit often feel magical: you go from idea → running app in an evening.

When to be very careful

Security researchers and journalists have already documented multiple cases where AI coding agents:

• Wiped entire drives or databases after misinterpreting instructions
• Exfiltrated secrets from files the UI appeared to protect
• Ran destructive commands in the wrong environment

In 2025 alone, public incidents involving Antigravity and Replit’s agents resulted in catastrophic data loss for some users, despite interface warnings and “safety” messaging.

So pure vibe coding is a bad fit for:

• Compliance-heavy or regulated systems
• Payments, healthcare, critical infrastructure
• Production databases or systems with weak backup/rollback
• Complex legacy systems where subtle bugs can be very expensive

Use vibe coding there only with strict guardrails: isolated environments, limited permissions, robust tests and human review.

How We Chose These Vibe Coding Tools

The tools below aren’t every product in the market. We focused on platforms that:

• Are explicitly associated with “vibe coding” in docs or marketing
• Support prompt-to-app or repo-scale AI agents, not just inline autocomplete
• Have real traction with developers or non-technical builders
• Are active as of late 2025, with visible roadmaps and communities
• Offer at least a basic story on security, privacy or governance

Think of this as a curated shortlist to pair with your broader AI-tools overview.

The Best Vibe Coding Tools (2025)

1. Lovable – “Build Products From Chat” for MVPs

Best for: founders and non-developers who want to ship MVPs quickly, especially B2C/B2B SaaS.

Lovable is a chat-driven platform: you describe your product, and the system generates the code, structure and hosting.

Where Lovable shines

• Very low barrier to entry-designed for people who don’t identify as programmers
• Good for early-stage product experiments, demos and hackathons
• Active content and community around “learning vibe coding” through projects

Trade-offs

• As with other platforms, you’ll want to audit authentication, database and access controls before exposing real customer data.
• Plan ahead for what happens if you outgrow the hosted environment and need more control.

2. Cursor – Agentic IDE for Developers Who Vibe (Carefully)

Best for: professional devs who want an AI-first IDE that still feels like a serious editor.

Cursor is already on AceCloud’s short list as “Best agentic IDE for vibe coding”, and that’s a fair description.

Where Cursor shines

• AI agents can implement features, refactor across files and reason about your whole repo
• Repo-scale operations: “migrate from library X to Y,” “make this service async,” etc.
• Strong fit for developers who want to keep code in Git and use normal workflows, just with an AI engine doing big chunks of the work

Trade-offs

• Agentic editing can be very powerful and disruptive – if you give it shell or CI access without guardrails.
• Still requires a solid test suite, reviews and staging environments to avoid “silent breakage.”

3. Windsurf – Agentic IDE for Large and Complex Codebases

Best for: teams with big, active codebases who want structured AI agents rather than just ad-hoc prompts.

Windsurf is another IDE-centric tool in most 2025 vibe-coding shortlists, often paired with Cursor in comparisons.

Where Windsurf shines

• Designed for multi-step plans and long-running tasks across many files
• Emphasis on transparency and rollback when agents make changes
• Natural fit for teams who want AI to act more like a junior engineer on long tasks

Trade-offs

• Like Cursor, it’s still “real development tooling,” so misconfigurations can impact large parts of your codebase quickly.
• Benefits increase a lot once you invest in tests, CI and code-review discipline.

4. Replit – The “Safest Place for Vibe Coding” (If You Use It Safely)

Best for: browser-based prototyping, education and teams who want instant environments.

Replit markets itself as “the safest place for vibe coding” and provides an AI agent that can build full apps and websites from natural-language prompts, backed by its Ghostwriter technology.

Where Replit shines

• No setup: open a Repl and you’re coding (or vibing) in seconds
• Replit Agent + Ghostwriter can scaffold multi-file projects, debug and explain code
• One-click or low-friction hosting, which is great for hackathons, education and internal prototypes

Trade-offs

• At least one high-profile incident showed an AI coding agent on Replit wiping a live database when given too much access, prompting public apologies and new safeguards.
• For serious production workloads, you’ll want to separate dev/staging/prod, constrain credentials and possibly mirror code to your own infrastructure.

5. Google Antigravity – Agent-First IDE for Gemini 3

Best for: teams already deep in Google Cloud or exploring multi-agent workflows.

Antigravity is Google’s new “agent-first” IDE built around its Gemini 3 Pro model. It gives multiple AI agents direct hooks into the editor, terminal and browser, and surfaces their work as “artifacts” like plans, task lists and recordings.

Where Antigravity shines

• Natively designed for agentic coding, not just suggestions in an existing IDE
• Artifact system gives more visibility into what agents did and why
• Integrates with Google’s broader AI studio and vibe-coding experiences

Trade-offs

• Security researchers have already identified worrying behaviours, including agents reading secrets and, in some user reports, wiping entire drives after misinterpreting “clear the cache”-type commands.
• For now, it’s best treated as a powerful but experimental environment that must run inside strong sandboxes and with continuous backups.

6. Emergent – Full-Stack Agentic Vibe Coding Platform

Best for: non-developers and teams who want “describe it in English → ship a full-stack app”.

Emergent markets itself directly as a full-stack vibe coding platform and “agentic AI app builder” that uses natural language as the main programming interface.

What it does well

• Generates complete web and mobile apps (frontend, backend, database, hosting) from prompts
• Multi-agent architecture that plans, codes, debugs and deploys
• Rich tutorial library showing real examples: Chrome extensions, bots, dashboards, games and more
• Designed so non-technical founders and internal teams can build usable tools without touching raw code

Trade-offs

• You’re heavily tied into Emergent’s platform for deployment and lifecycle unless you plan an export/migration path.
• As with all vibe coding tools, production-grade apps still need proper testing, security review and monitoring.

7. Bolt.new – Browser-Based Prompt-to-Web-App Builder

Best for: web apps, SaaS prototypes and “I want a full stack from one prompt” workflows.

Bolt.new evolved from StackBlitz’s browser IDE into a dedicated AI web-app builder and “professional vibe coding tool.”

Where Bolt shines

• Build full-stack web apps by describing them: “CRM with Kanban board and Stripe billing,” etc.
• Modern stacks out of the box (React, Node/Express and popular databases)
• In-browser dev, run and deploy – no local setup required
• Strong Day-0 experience: great for “vibing” the first version of a product

Trade-offs

• Heavier apps will need refactoring into your long-term architecture (e.g. export into a monorepo with your normal CI/CD).
• Business reports show the broader AI-coding market is still volatile, so lock-in and migration paths matter.

8. Honourable Mentions

Other tools frequently mentioned in vibe-coding tool roundups include:

• v0 by Vercel – a prompt-to-UI builder tightly tied to modern frontend stacks
• Base44, Tempo Labs, Memex – various spins on security-aware, error-fixing or “vibe everything” development
• Google AI Studio & Firebase Studio – Google’s hosted vibe-coding experiences around Gemini 3 and their app platforms

They’re all worth exploring if they fit your stack, but Emergent, Bolt, Lovable, Cursor, Windsurf, Replit and Antigravity cover most vibe-coding scenarios teams ask about today.

How to Use Vibe Coding Safely

Vibe coding doesn’t have to mean reckless coding. The same guardrails you already recommend for AI coding in general apply here -just turned up a notch.

1. Keep a human in the loop

• Treat AI as a very fast junior engineer, not an autonomous production owner.
• Someone senior must still own architecture, security and final code quality.

2. Invest in tests and CI/CD

• Unit, integration and end-to-end tests are your safety net when agents touch many files at once.
• Run tests automatically in isolated environments after each significant AI-driven change.

3. Sandbox agents and tools

Public incidents with Antigravity and Replit show what happens when agents get broad, unsupervised access to real systems.

• Give agents least-privilege access: separate dev/test/prod, scoped credentials, read-only where possible.
• Default to no access to production databases, object storage or critical infra from experimental vibe-coding sessions.

4. Add security and quality scanning

• Run SAST, dependency and container scans on AI-generated code just like human-written code.
• Watch for insecure defaults, especially around auth, input validation and secret handling.

5. Treat Day-0 and Day-1+ differently

Many experts note that current tools excel at “Day-0” (new app creation) but are weaker for long-term maintenance.

• Use vibe coding aggressively for prototypes and throwaway code.
• Use it more cautiously for mature, revenue-critical systems.

Where AceCloud Fits Into Vibe Coding

The tools above control how code gets written. AceCloud controls where that code runs and how safe the environment is.

From your own platform navigation, AceCloud offers:

• Cloud GPUs and compute instances
• Managed Kubernetes and Kubernetes GPU clusters
• Block storage, snapshots, backup and disaster recovery
• Network isolation, firewalls and load balancers

That’s exactly what teams need to vibe-code without putting production at unnecessary risk.

1. Spin up safe sandboxes for vibe coding

• Create dedicated GPU or CPU environments where tools like Emergent, Bolt, Replit, Cursor or Antigravity can run agents freely.
• Use VPCs, security groups and firewalls so these sandboxes can’t accidentally touch production databases or internal services.

2. Mirror staging and production environments

• Use AceCloud compute or managed Kubernetes to mirror your production stack in staging.
• Run AI-generated changes through that staging environment first, with automated tests.

3. Automate backups and rollback

Given real-world stories of agents wiping drives and databases, reliable backup and DR are non-negotiable.

• Use AceCloud backup and snapshot features so you can restore quickly if an AI agent makes a destructive change.
• Combine this with Git-based workflows so reverting code is as easy as reverting infrastructure state.

4. Optimise AI infrastructure costs

• Run your own LLMs and tools on AceCloud GPUs if data residency, privacy or cost are concerns.
• Benchmark different models (OpenAI, Anthropic, Google-adjacent open models, open-source LLMs) behind your vibe-coding workflows to find the best balance of speed, quality and cost.