Vibe Coding, Explained: What You Really Need to Know in 2025

Author(s): Michele Mostarda

Originally published on Towards AI.

A practical guide to AI app builders — Replit, Bolt, Lovable, Databutton, Base44, Cursor, and Windsurf — and how to pick the right one.

TL;DR

Prompt-driven development (aka vibe coding) compresses idea → app into minutes. It’s fast — and risky. Use builder tools (Lovable, Bolt, Replit Agent, Databutton) for velocity and agentic IDEs (Cursor, Windsurf) for precision. Match cost meters (tokens vs credits vs $ pools vs compute-hours) to your workload. Add tests, approvals, and least-privilege guardrails, or speed becomes debt.

Last year, software moved into a new lane: you state intent in natural language, and an AI builder/agentic IDE scaffolds, runs, and iterates your app. It feels like magic — until cost, accuracy, and lock-in bite. This guide gives founders, PMs, and hybrid teams a grounded way to choose among six leading tools without the hype.

“Vibe coding” captures the shift from formal specs to intent and feel — you guide by outcomes, not boilerplate. That’s powerful — and risky — because speed can mask trade-offs in cost, accuracy, governance, and lock-in.

This article gives product managers, founders, and hybrid teams a practical way to choose between seven leading options: Replit, Bolt, Lovable, Databutton, Base44, Cursor, and Windsurf.

I keep it concrete:

• What they do best (and where they struggle), their usability for non-technical users, and how far they scale.
• How each one charges — not all costs are “tokens”: you’ll see prompt/credit, monthly $ pools, and compute-hours in play, with simple rules of thumb to predict spend.
• Which LLMs (Large Language Models) they use or support, and where BYOK (Bring Your Own Key) gives you control over cost, compliance, and model choice.
• This is not a structured benchmark; it’s a practical comparison: expect actionable insights, clear caveats, and decisions you can make today.

1. At a Glance

• Non-technical / fast MVPs: start with Lovable (credits; usability 5/5) or Bolt (tokens; usability 4/5) or Base44 (credits + integration tokens; usability 5/5) for simple sites & lightweight apps. Plan an early handoff to dev-centric tools.
• Repo-wide precision: choose Cursor or Windsurf for multi-file refactors, clear diffs, and test-friendly workflows.
• Integrated loop: Replit Agent does plan → build → test → preview in one place, with automatic error detection.
• Data tools & hosting: Databutton for Python/FastAPI + React apps, where compute-hours map to backend use.
• Simple rule of thumb: Prototype fast (Lovable/Bolt/Base44) → stabilize with tests (Cursor/Windsurf) → operate end-to-end (Replit Agent, Databutton for data-centric tools).
• Governance first: least-privilege, secrets hygiene, preview envs, CI tests, human approvals for risky “computer-use,” and audit logs.

2. Why Now: From Low-Code Blocks to Prompt → Code → Build → Test → Preview

The last wave of “no-code/low-code” tools taught teams to assemble apps with visual blocks. Useful — but brittle at scale, and usually limited once you need custom logic or deep integrations. Prompt-driven development is a different step: you write intent in natural language and an agentic IDE or builder translates it into runnable code, then executes build, tests, and a live preview — often in the same workflow. In short: from drag-and-drop to prompt → code → build → test → preview.

Why it matters:

• Throughput and iteration speed. You can draft a feature, see it run, and refine it in minutes, not sprints. That compresses product discovery cycles and lets small teams ship credible MVPs without a large bench of specialists.
• Leverage existing code. Agentic IDEs operate on real repositories, refactor across files, and keep diffs transparent. This is closer to how engineers already work, which lowers the handoff friction from prototype to production.

Where the magic ends:

• You still need engineering discipline. Without tests, version control, and security checks, fast changes become fast regressions. Treat the agent as a powerful collaborator, not an oracle: write smoke tests, keep CI running, and gate risky actions (package upgrades, schema changes) behind review.
• Context isn’t free. Long histories, large repos, and ambiguous prompts degrade accuracy and cost more under token/credit models. Small, scoped prompts and clear acceptance criteria consistently yield better results.

Choosing tools is a portfolio of trade-offs:

• Speed vs control. Builder-style tools maximize velocity with opinionated flows; IDE-style tools give finer control over diffs, tests, and model choice.
• Precision vs effort. More deterministic outcomes typically require better prompts, tighter scopes, and test scaffolds you curate.
• Cost predictability vs capability. Token models track text volume; prompt/credit models track requests; monthly $ pools blend flexibility with monitoring overhead; compute-hours tie spend to backend runtime. Pick the meter that matches your usage pattern, not just the sticker price.
• Model lock-in vs flexibility. Some tools run on a managed backend; others let you BYOK to OpenAI/Anthropic/Gemini/Llama, which can improve compliance and swapability — but also shifts responsibility for keys, quotas, and data handling to you.

Prompt-driven development is not a silver bullet — it’s an accelerator. Teams that pair it with guardrails (tests, reviews, permissions), cost awareness, and clear prompting habits will turn speed into sustained product quality, not technical debt.

3. Tools overview

This section is a quick, at-a-glance map of where each product fits. Read it left-to-right for what it does, who it’s for, and how you’ll be billed; then scan the rightmost columns to see usability and practical trade-offs. The usability score (1–5) reflects how comfortably a non-technical user can get value without developer help (5 = easiest). The cost model column is crucial: not every platform meters usage the same way — some count tokens (text volume), others count prompts/credits (requests), some give you a monthly $ pool, and others bill on compute-hours for backends. That meter should match your workload: short prototyping loops behave differently from long, repo-wide refactors or always-on services.

Use the Strengths/Weaknesses column to anticipate operational friction: e.g., whether error handling is manual vs automated, how well the tool separates planning from build, and whether UI patterns are reusable. For non-technical creators, Lovable and (for quick MVPs) Bolt are the most forgiving. For precision on real repos, Cursor and Windsurf shine. If you want an integrated loop (plan → build → test → preview), Replit Agent is compelling; for data-centric internal tools with hosted backends, consider Databutton. Keep in mind that model choice/BYOK nuances appear later in the LLM section.

4. How You Really Pay — Cost Cheat-Sheet

Not all “AI coding” tools measure the same way. Picking the right meter for your workload is as important as choosing the tool itself, because it drives predictability and total cost of ownership.

Here’s the updated glossary of payment models — polished for Medium readability.

Token-based

• What it means: you pay per text processed (prompt + response), measured in tokens.
• Pro: fine-grained control, great for rich context and adaptive tasks.
• Con: costs can explode with long chat histories, large repos, or verbose stack traces.
• Tip: cap context length, summarize diffs, and set explicit acceptance criteria in prompts.

Prompt / Credit-based

• What it means: You pay per request (i.e., a message or action).
• Pro: Predictable budgeting, simple to track.
• Con: You can burn credits quickly in long back-and-forth loops.
• Tip: Keep sessions short and stateless, with well-scoped tasks.

Monthly $ Credit Pool (multi-meter wallet)

• What it means: you get a wallet in dollars (e.g., $20–$25/month) that depletes across multiple meters, not just LLM tokens — including model calls, agent actions, compute/hosting, storage, bandwidth, and build/test operations (varies by tool).
• Why it’s not just another “token-based”:
• It covers heterogeneous resources (compute, storage, and agent use) with different rates.
• Model-specific pricing (Claude, GPT, Gemini) and hidden overheads (tool-use, retries, logging).
• Minimum charges per request and possible platform mark-ups make the burn rate nonlinear.
• Tips:
• Set burn-rate alerts at 50/75/90%.
• Log spending mix (LLM vs compute/hosting).
• Calculate monthly effective $/1K tokens to compare fairly with pure token-based plans.

Compute-hours

• What it means: you pay for backend runtime (containers, APIs, background jobs).
• Pro: intuitive for hosted services and server workloads.
• Con: costs grow with always-on or real-time workloads.
• Tip: enable auto-sleep / scale-to-zero, size instances conservatively, and separate batch from real-time jobs.

Fit Costs with Real Use Cases

• 🧩 MVP or demo (short cycle, low budget)
  Use Lovable (prompt/credit-based) or Bolt.new (token-based).
  Both are great for quick, low-cost prototypes. You describe what you want, get a live app, and can iterate fast.
  Perfect for idea validation, product pitches, or first user tests — not ideal for complex or long-term builds.
• 🏢 Internal business tool (usage-based spend)
  Use Databutton, which charges by compute-hours — you only pay for backend runtime when your app is actually doing work.
  It’s best for dashboards, admin panels, or small APIs.
  Estimate your traffic volume and duty cycle (how long the service stays active) to keep costs predictable.
• ⚙️ End-to-end builder (plan → build → test → preview)
  Use Replit Agent, which uses a monthly $ credit pool shared across AI actions and compute.
  It’s well-suited for teams who want integrated build, test, and deploy loops without managing infrastructure.
  Set burn-rate alerts and monitor the wallet balance — it’s flexible but can deplete fast on large projects.
• 👩‍💻 Development team (structured, multi-file projects)
  Use Cursor or Windsurf, which rely on a monthly $ pool or prompt credits.
  Ideal for codebases that require refactoring, precision edits, and version control.
  Combine them with a test suite and short, well-scoped tasks to reduce retries and control spend.

Choosing the meter by workload

• Short, bursty prototyping: prefer prompt/credit (Lovable) for predictability or tokens (Bolt) when you need larger context windows briefly.
• Repository-wide refactors: token usage explodes with context; either pin the context (work file-by-file with tests) or move to tools where you can cap spend (monthly $ pools) and enforce shorter sessions.
• Always-on backends/data apps: cost maps to compute-hours (Databutton). Size instances conservatively; use sleep/scale-to-zero where possible.

Budget heuristics (back-of-the-envelope)

• Token tools: Monthly cost ≈ avg tokens/request × requests/month × $/token. Reduce context (truncate logs, collapse diffs).
• Prompt tools: Monthly cost ≈ credits/month ÷ avg credits/request. Reduce retries with acceptance criteria in the prompt.
• $ pools: set burn-rate alerts (e.g., 50% mid-month).
• Compute: Cost ≈ hours × instance rate. Add idle timeouts and job queues.

Cost-control knobs that actually work

• Scope prompts (single task, explicit done-criteria); prefer stateless short sessions over chatty meanders.
• Pin model + temperature for repeatability; cache results of deterministic steps (scaffolds, boilerplate).
• Automated smoke tests before long agent runs; fail fast.
• Trim context (summaries over raw logs; file-level diffs, not repo dumps).
• BYOK where it helps (bulk/large-context tasks) and set quotas on keys.
• Watch hidden sinks: background agents stuck in loops, verbose logs fed back into context, unbounded preview builds.

Match the meter to the motion of your work. Prototype on credits/tokens, harden with tests to cap retries, and tie service spend to compute you actually need.

Under the hood — LLM & BYOK

Model choice — and who controls it — shapes cost, quality, and compliance. Some tools run on a managed backend with a single frontier model (fast to start, less to configure), while others expose a model picker and even BYOK (bring-your-own-key) so you can select OpenAI, Anthropic/Claude, Gemini, or Llama variants and pay your own provider directly. Managed setups reduce operational friction and smooth the UX, but they also centralize costs and can increase vendor lock-in. BYOK increases control and potential savings (especially for large-context or high-volume work), yet pushes responsibility for keys, quotas, logging, and data handling onto your team.

A second axis is how models are used: builder-style platforms often focus on prompt → scaffold → live preview, while IDE-style tools apply models to repo-wide edits, diffs, tests, and refactors. Long conversations and big repositories favor models with long context windows and good tool-use (code edits, test execution, shell commands). If your workload is mostly short, deterministic actions, stability and cost predictability might matter more than squeezing maximum context.

Use the table below to see each product’s defaults, alternatives, and BYOK posture. Then skim the notes for the governance angle: where you can swap providers, what remains managed/opaque, and which features still rely on the vendor’s own models even when BYOK is enabled. Treat this as a control matrix: start managed for speed; switch to BYOK on high-impact paths (e.g., long-context coding sessions or regulated data) once you’ve set guardrails for observability, usage caps, and secrets rotation.

5. The Right Tool by Usage Pattern

Rule of thumb: prototype on Lovable or Bolt, operationalize data tools on Databutton, harden and refactor code in Cursor or Windsurf, and use Replit Agent to accelerate integrated build/test cycles — always with tests and spend caps in place. If you’re interested in more detail, you can read the section below.

For non-technical or rapid prototyping → use Lovable (simplicity) or Bolt (speed)

When you need a quick prototype or a non-technical demo, choosing the right tool is key to avoiding wasted time and tokens.
Lovable is ideal for low-complexity projects — landing pages, forms, mini dashboards, or light CRUD apps. Its prompt/credit model is predictable, and the UI is forgiving of minor ambiguity or errors. Keep your prompts tightly scoped (“one page with fields X/Y/Z and a visible success state”) and always include explicit acceptance criteria to minimize iteration cycles. The clearer the goal, the fewer retries you’ll need.

Bolt, on the other hand, is the right choice when you need a live app — something running immediately in React/Next.js and connecting to simple APIs. It’s great for testing dynamic flows or interaction logic, but you’ll need to manage context carefully: tokens burn fast if the model reprocesses the whole project on every iteration. Keep your instructions focused (“edit only page A” instead of “refactor the whole project”) to stay efficient.

In both cases, plan an early handoff to a more developer-centric tool once the prototype shows potential. Lovable and Bolt excel in the exploration phase, but maintenance and scalability will require more structured tools once the concept is validated.

Internal, data-centric tool → Databutton (pipeline + hosting + compute discipline)

Choose Databutton when the real value lies in your data flows — small APIs, internal dashboards, admin panels, or lightweight back-office automation. The platform scaffolds a FastAPI + React stack and takes care of hosting, letting you focus on logic rather than infrastructure.

Because Databutton’s pricing is tied to compute-hours, it’s important to design efficiently: size your instances modestly, and enable sleep or scale-to-zero to avoid idle costs. Keep your endpoints lean and store secrets properly; bring your own key (BYOK) only where LLM calls directly add value — for example, in classification, summarization, or enrichment tasks.

It’s an excellent fit for internal teams that need fully working data tools without the overhead of managing servers, cloud infrastructure, or deployment pipelines. Databutton lets you go from concept to running service with minimal ops friction — perfect for disciplined, data-driven prototyping.

End-to-end build/test/preview → Replit Agent

Use Replit Agent when you want a full plan → build → test → preview loop within a single environment. It’s especially effective for greenfield projects, workshops, and rapid product spikes that benefit from automatic error detection, live previews, and a containerized runtime capable of executing tests.

Apply least-privilege “computer-use” permissions, review shell actions carefully, and monitor your monthly credit pool to avoid runaway costs. Replit excels in early-stage, iterative work where speed matters more than structure — but once the project gains complexity, migrate the codebase to your team’s standard CI pipeline and continue development in Cursor or Windsurf for better control and maintainability.

All-in-one builder for non-coders → Base44

Choose Base44 when you want a one-stop platform that handles frontend, backend, database, authentication, and hosting automatically. It’s ideal for non-technical founders who need visually refined results fast — Base44’s focus on UI/UX polish often produces cleaner layouts than other builders in the same tier. Unlike Bolt or Lovable, Base44’s managed environment abstracts away nearly all code and infrastructure decisions. This makes early prototyping effortless but limits flexibility when you need to customize logic, access raw code, or control deployment workflows.

Dev team / multi-file refactor → Cursor or Windsurf (model control, diffs, agents)

When working with real repositories and precise, code-level edits, Cursor and Windsurf are your best options. Both tools give you granular model control, structured diff management, and intelligent agent support for complex changes.

Always pin your model version to maintain consistency, and run with your test suite active to validate every change. Break down refactors into small, well-defined tasks rather than long conversational sessions — shorter scopes mean cleaner diffs and fewer regressions. Let the agent generate proposals, then review and apply in small batches to stay in control. Avoid pushing the entire repository as context; targeted edits are faster, cheaper, and safer.

Cursor stands out with its partial BYOK support, useful for teams managing cost or compliance constraints. Windsurf, on the other hand, excels at structured multi-step refactors thanks to its flow-based approach. Both tools deliver their best results when conversations stay scoped and your CI pipeline stays green.

6. Trade-offs & governance

Precision vs speed. Prompt-driven builders make it trivial to ship an MVP in hours, but that first success can hide fragile scaffolding: ambiguous prompts, implicit assumptions, and missing tests. Treat rapid prototypes as throwaway spikes unless you harden them: freeze scope, add tests, and refactor before layering features. Precision improves when you shrink the unit of work (one component, one API, one migration) and state explicit acceptance criteria in the prompt.

Cost mechanics. Different meters = different ways to leak money. Tokens explode with long histories and big diffs; prompt/credits punish chatty back-and-forth; $ pools drift without alerts; compute-hours climb with always-on backends. Mitigate with: short, stateless sessions; context caps (summaries over raw logs); mid-month burn-rate alerts (e.g., 50/75/90%); and autosleep/scale-to-zero for services. Prefer BYOK where large-context tasks dominate and you can negotiate model pricing.

LLM lock-in & BYOK. Managed backends are convenient, but they bind you to a provider’s roadmap and pricing. Favor tools that: (1) expose a model selector; (2) allow BYOK for heavy workloads; (3) keep prompts/data portable (no proprietary spec). Keep a baseline provider-agnostic prompt library and document minimal model requirements (context length, JSON mode, tool-use).

Security & compliance. Treat agents like junior SREs with guardrails. Apply least privilege (read-only by default; write/exec only in sandboxes). Isolate secrets (scoped tokens, short TTL, rotation). Require human approval for sensitive actions (shell commands, package installs, schema changes). Log who/what/when for each agent action; store traces for audit. Use staging environments for “computer use” and block prod credentials by policy.

Quality & reliability. Bake in a minimum test suite (smoke + a few high-value integration tests) and run them before large edits. Keep CI on, even for prototypes. Enforce small PRs with clear diffs; reject repo-wide edits without tests. Pin model/temperature for repeatability; prefer plan → apply → verify loops over open-ended chats.

7. Operating Checklist

• Scope small, write acceptance criteria.
  Define narrow, well-bounded tasks. Make sure each prompt or change has clear success conditions and measurable outcomes. Small, testable scopes reduce iteration loops and make debugging faster.
• Cap context; prefer summaries over raw logs.
  Keep input context lean. Instead of feeding entire logs or verbose outputs, summarize what matters. This saves tokens, improves model accuracy, and prevents context dilution.
• Tests + CI before multi-file edits.
  Always have a functioning test suite and CI pipeline in place before allowing large or multi-file changes. This ensures regressions are caught early and gives you a reliable feedback loop as the model edits your codebase.
• Least-privilege + approvals for risky steps.
  Grant minimal permissions for agents or automated processes, especially those with “computer-use” or shell access. Require human review or approval for any high-impact action like deployments, deletions, or migrations.
• Burn-rate alerts; autosleep services.
  Monitor compute and credit consumption actively. Set up alerts for cost thresholds and configure idle services to auto-sleep or scale to zero. This helps control spending and keeps environments efficient.
• BYOK where it meaningfully reduces cost/compliance risk.
  Use “bring your own key” setups only when they add real value — typically for cost optimization, data residency, or compliance reasons. Avoid unnecessary complexity if it doesn’t materially improve security or efficiency.

8. A Mini Benchmark

This chapter reports an indicative test run on a Habit App generated with different tools. The baseline is a set of shared specifications, public and available here. The exact same specs were provided to all generation tools, with no tailoring, to keep the comparison as consistent as possible.

Cursor and Windsurf are excluded from this test, as they are not suitable for creating a project entirely from scratch with a few shots.

How to read the results

• For each tool we observed: the initial outcome, the number of interactions needed to reach a working app, the presence/absence of automated tests, and the visibility of planning and verification phases.
• The goal is not an exhaustive benchmark but a practical hint about out-of-the-box behavior and UX.
• The tests were conducted in early October 2025, and the tools may have since undergone performance or feature improvements.
• This is a single example; results should not be generalized.

A note on prompts and vibe coding

The prompt makes a decisive difference. In vibe coding, a well-structured prompt is essential to get effective results — it shapes generation quality, consistency, and the overall development feel.

A) Setup

• Input: the spec prompt to generate the Habit App.
• Observed criteria: number of interactions needed, perceived functional coverage, presence/absence of automated tests, and visibility of planning and verification phases.

B) Results by tool

Lovable

• Initial outcome: working app with 1 single interaction.
• Additional interactions: 0.
• Automated tests: not exposed (if present, they’re hidden).
• Planning/Verification: hidden from the user.
• Key observation: extremely linear experience; you don’t see what happens “under the hood.”

Replit

• Initial outcome: app ~90% complete.
• Additional interactions: +2 prompts to fix blocking details.
• Automated tests: yes, run at each step (good coverage).
• Planning/Verification: exposed and adjustable by the user.
• Observed limit: some micro-issues or missing UI interactions weren’t caught by the automatic tests.

Bolt.new

• Initial outcome: app ~90% complete.
• Additional interactions: +3 prompts to resolve issues.
• Automated tests: no; errors surface at deploy time.
• Planning: exposed and precise.
• Verification: partially exposed, imprecise.
• Key observation: fixes handled via prompts; part of verification is deferred to deployment.

Databutton

• Initial outcome: scaffolding and layout generated.
• Progression: planned the next steps but waited for confirmation before continuing.
• Additional interactions: +5 prompts required to complete the missing functionalities.
• Automated tests: absent; manual verification expected.
• Planning/Verification: exposed, not autonomous.
• Observed limit: functionality incomplete; some interactions are missing.

Base44

• Initial outcome: scaffolding and layout generated.
• Additional interactions: all functionalities developed, but required +3 prompts to fix interaction issues
• Automated tests: absent; manual verification expected.
• Planning/Verification: exposed, autonomous.
• Observed limit: functionality incomplete; some interactions are missing.

C) Conclusion (for this indicative test)

• Lovable maximizes low friction (planning/verification hidden) and delivers immediately.
• Replit makes the pipeline and tests transparent; good coverage, complemented with UI checks.
• Bolt.new is fast, but not completely accurate in functional development and testing
• Base44 delivers accuracy comparable to Bolt.new, with greater attention to UI and UX polish — but less flexibility in code handling and deployment options.
• Databutton structures the work and asks for confirmation before advancing; manual verification and incomplete features at the first step.

9. Closing Thought

Prompt-driven development is an accelerator, not an autopilot. It can turn ideas into code in hours — but acceleration without control gets you to the wrong place faster. The winning teams treat AI as a co-pilot: it drafts and executes, while they constrain, verify, and make the final decisions. Keep the essential guardrails in place — tests, approvals, least-privilege access, scoped prompts, and cost awareness — and speed becomes leverage, not liability; that’s how you turn intent into impact.

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Published via Towards AI