6 Aug 2026

How Many Concurrent Users Can Lovable Handle? AI, Images, Video and Scaling Explained

MarketMeGlobal Team

Lovable can host applications ranging from simple landing pages to full SaaS products with authentication, databases, file storage and artificial intelligence. However, asking how many concurrent users Lovable can handle does not produce one universal number.

The answer depends on what those users are doing.

One thousand people viewing static pages is fundamentally different from one thousand people running database searches, generating images or making simultaneous requests to an AI model. Lovable’s frontend hosting can scale automatically, but database compute, file transfer, AI rate limits and usage credits introduce practical constraints.

As of 5 August 2026, Lovable states that published applications do not have plan-level caps on visitors, requests or bandwidth. The platform also says that its Cloud backend can scale from prototypes to applications serving millions of users. Nevertheless, Lovable does not publish guaranteed requests-per-second figures or a fixed number of concurrent backend users for each Cloud instance size.

This article explains what Lovable can realistically handle in four scenarios:

1. Applications without images, video or AI
2. Applications that store and serve images
3. Applications that contain or stream video
4. Applications using AI chat, prompt optimization, image generation or other AI features

It also provides practical recommendations for applications such as AIQuickPrompt, where most activity involves text, database records and occasional AI processing.

The direct answer

For a lightweight, text-based application, Lovable can potentially handle hundreds of concurrent users, provided the database is properly indexed, queries are efficient and the Cloud instance is appropriately sized.

Lovable does not document a hard numerical limit, so this should be treated as an engineering estimate rather than a platform guarantee.

A reasonable initial capacity interpretation for a product such as AIQuickPrompt is:

The attached capacity report correctly distinguishes between online users and AI-active users. It cites community observations suggesting that approximately 10–15 simultaneous heavy AI sessions may begin triggering 429 Too Many Requests responses. However, Lovable does not publish the numerical requests-per-minute limit behind its AI gateway, so this figure cannot be treated as an official Lovable concurrency limit.

Concurrent users are not the same as registered users

A common scalability mistake is to confuse four different measurements:

Registered users

These are accounts stored in the database. An application can have 1,000, 10,000 or more registered users without all of them placing simultaneous demand on the system.

A user record containing an email address, password reference, subscription status and a few preferences consumes very little database capacity.

Monthly active users

Monthly active users are people who use the application at least once during a month. They do not normally use it at the same time.

Concurrent online users

These are users with the application open during the same period. Some may only be reading a page or leaving a browser tab open.

Concurrent active requests

These are users actively making requests at the same moment: searching the database, saving a prompt, generating an image or calling an AI model.

The final category creates the greatest infrastructure pressure.

Therefore, an application with 10,000 registered users might have only 50 people online at once and five actively using AI. That workload can be easier to handle than an application with 500 registered users where 100 people simultaneously trigger expensive AI operations.

How Lovable hosting is structured

A Lovable application can involve several separate infrastructure layers:

1. Frontend hosting: Delivers pages, JavaScript, CSS and static assets.
2. Database: Stores users, prompts, folders, subscriptions and application records.
3. Authentication: Manages sign-in, sessions and user permissions.
4. Storage: Holds uploaded or generated images, documents, audio and video.
5. Backend compute: Runs Edge Functions, payments, webhooks and private API calls.
6. AI gateway: Sends model requests to supported OpenAI and Google models.

Lovable hosts published frontends globally and states that plans do not cap visitors, requests or bandwidth. Automated abuse protections may still challenge unusually aggressive traffic from one source.

Backend activity is different. Database work, storage, network transfer, Edge Functions and AI calls consume Run credits. Larger or busier applications therefore use more credits even though their frontend traffic is not subject to a fixed visitor allowance.

How much can Lovable handle without images, video or AI?

A text-only application is one of the least demanding workloads Lovable can host.

Consider an application where users can:

1. Register and sign in
2. Create folders
3. Save text prompts
4. Search their saved prompts
5. Edit or delete prompts
6. Copy prompt text
7. Manage a subscription

This workload contains small database rows and relatively small API responses. There is no image processing, video streaming or continuous AI inference.

Frontend capacity

Lovable does not impose a published visitor limit on the frontend. Static pages and application code are distributed globally, so large numbers of visitors can access the interface without every request reaching the database.

For a content website or mostly static application, the meaningful constraint is therefore unlikely to be the number of people who can load the homepage.

Database capacity

The database becomes involved when users sign in, search, save, update or delete information.

Lovable Cloud offers multiple backend instance sizes:

1. Tiny
2. Mini
3. Small
4. Medium
5. Large

Lovable does not publish CPU, memory, connection or request-per-second figures for these instance sizes. It describes larger instances as capable of handling more users and traffic, while also consuming more credits.

Because the Cloud backend uses Supabase’s open-source foundation, it benefits from PostgreSQL, connection pooling, row-level security and scalable compute concepts. However, Lovable’s instance names should not be assumed to map directly to the public Supabase compute tiers.

Practical planning estimate for a text application

For a well-designed text application, the following ranges are reasonable capacity-planning targets, not documented Lovable limits:

These targets assume:

1. Queries are indexed
2. Search results are paginated
3. Each request retrieves only the required columns
4. Users can access only their own data through row-level security
5. Repeated requests are debounced or cached
6. The database instance is resized when metrics show sustained pressure

The correct conclusion is not that Lovable has a 500-user ceiling. The conclusion is that hundreds of concurrent text users represent a sensible load-testing range for a lightweight application.

Lovable could perform above or below that range depending on implementation quality.

What could reduce text-only capacity?

Even applications without media or AI can become slow when their data layer is poorly designed.

Unindexed searches

Searching a large prompt table with inefficient wildcard queries can force PostgreSQL to scan many rows repeatedly.

Loading every prompt at once

An application should paginate records rather than downloading a user’s entire prompt library every time the dashboard opens.

Excessive real-time subscriptions

Real-time updates are useful for collaboration, but maintaining unnecessary subscriptions increases Realtime usage and backend activity.

Repeated database requests

Typing one character into a search box should not necessarily make one new database request. Debouncing the input by a few hundred milliseconds can significantly reduce unnecessary traffic.

Incorrect row-level security

Row-level security protects user data, but complicated or inefficient policies can increase query cost. Missing policies can expose data and create a much more serious security problem.

Lovable’s security guidance recommends using server-side Edge Functions for private business logic and PostgreSQL row-level security to control database access.

How images change Lovable’s capacity

There are two distinct image workloads:

1. Serving images that already exist
2. Generating or editing images with AI

They affect the application differently.

Serving existing images

Lovable Cloud includes storage for uploaded files. Storage and network transfer consume Run credits, and usage rises when users upload, download or repeatedly view larger files.

An application containing a few optimized logos, thumbnails or profile pictures should not experience a dramatic scalability reduction. Modern browsers can cache those assets, and the frontend remains globally distributed.

Problems appear when an application serves:

1. Large, uncompressed photographs
2. Full-resolution files where small thumbnails would be sufficient
3. Galleries containing dozens of images per page
4. Frequently downloaded private images
5. User uploads without file-size restrictions
6. Duplicate copies of the same image

Lovable recommends compressing images to the size at which they will actually be displayed. It also warns that large media files stored directly in a project can slow loading and preview performance.

Recommended image architecture

For an image-enabled Lovable application:

1. Store images in Lovable Cloud Storage, Supabase Storage or a dedicated object-storage service.
2. Generate small thumbnail versions for listings and search results.
3. Load full-resolution files only when a user opens them.
4. Use modern formats such as WebP or AVIF where browser compatibility permits.
5. Add lazy loading so off-screen images are not downloaded immediately.
6. Apply upload limits and validate file types server-side.
7. Use signed URLs for private files.
8. Delete abandoned and duplicate files regularly.

With these controls, an application can continue supporting substantial traffic. The main change is that network and storage consumption—and therefore cost—will rise faster than in a text-only product.

AI image generation

Lovable’s built-in AI connector supports image generation and editing through supported OpenAI and Google image models. It can generate marketing visuals, thumbnails, illustrations, mockups and edited versions of uploaded images.

AI image generation is much heavier than simply serving an existing image because each request involves:

1. A backend AI call
2. Model processing time
3. AI gateway credits
4. Storage for the generated image
5. Network transfer when the result is displayed or downloaded

An application should not allow unlimited parallel image generation without controls.

A more reliable design is to:

1. Place generation requests in a queue
2. Limit the number of active jobs per user
3. Prevent repeated clicks from creating duplicate jobs
4. Display job status while processing
5. Store and reuse completed results
6. Add daily or monthly generation allowances
7. Use lower-cost image models for drafts
8. Reserve premium models for paid plans or final outputs

Lovable does not publish a numerical image-generation concurrency limit. The appropriate queue size must therefore be established through controlled testing and the AI activity dashboard.

How video changes Lovable’s capacity

Video produces a much larger data-transfer workload than text or images.

A two-minute compressed video may still be tens of megabytes. If hundreds of users stream it simultaneously, the application can transfer gigabytes of data in a short period.

Lovable explicitly recommends hosting videos through a service such as YouTube rather than storing large video files directly in the project repository.

Embedded externally hosted video

When a YouTube, Vimeo, Mux or similar hosted video is embedded in a Lovable application, the video platform delivers most of the video data.

Lovable still serves the page and player integration, but the external video platform handles:

1. Video encoding
2. Adaptive bitrate streaming
3. Global content delivery
4. Playback bandwidth
5. Device compatibility
6. Large traffic spikes

This is the recommended arrangement for public marketing videos, course previews and other non-sensitive content.

User-uploaded or private video

Private video applications require more planning. Object storage alone is not a complete video platform.

A production video system may need:

1. Resumable uploads
2. Malware and file validation
3. Video transcoding
4. Multiple resolutions
5. Adaptive streaming
6. Thumbnail generation
7. Signed playback URLs
8. Content-delivery caching
9. Retention policies
10. Download protection

For these applications, Lovable can remain the frontend and business-logic layer while a dedicated media service handles storage, processing and playback.

AI video generation

Lovable’s current runtime AI documentation lists chat, image, embedding, text-to-speech and speech-to-text models, but it does not list a native runtime video-generation model category. Lovable’s development agent can generate short videos while building a project, but an application offering end-user video generation would normally need to integrate an external video-generation API.

In that architecture, video-generation concurrency is mainly determined by:

1. The external provider’s quota
2. The number of jobs the provider allows
3. Generation duration
4. Queue configuration
5. Webhook delivery
6. Storage and download traffic
7. The application’s subscription and usage controls

Video generation should be asynchronous. The user submits a request, receives a job identifier and is notified when the result is ready. The browser should not keep one long request open for the entire generation process.

How much AI concurrency can Lovable handle?

Lovable’s built-in AI connector supports:

1. Chatbots and assistants
2. Text generation
3. Summarization
4. Classification
5. Translation
6. Document question answering
7. Image and document analysis
8. Semantic search
9. Retrieval-augmented generation
10. Embeddings
11. Image generation and editing
12. Text-to-speech
13. Speech-to-text
14. Workflow automation

AI calls run through secure backend infrastructure rather than directly from the browser, and streaming responses are supported for chatbot-style experiences.

The important limitation: workspace-level rate limits

Lovable applies AI rate limits at the workspace level. These limits are measured in model calls per minute, not tokens per minute.

When the limit is exceeded:

1. The request returns 429 Too Many Requests.
2. The request is not processed.
3. Free workspaces have more restrictive limits.
4. Paid users can contact Lovable Support to request additional capacity.

Lovable does not publish the exact number of requests per minute for each plan.

This means no independently verifiable article can state that Lovable officially supports exactly 10, 20 or 100 simultaneous AI users.

Why “10–15 users” can be misleading

Suppose 15 users each make one AI request every two minutes. That represents approximately 7.5 calls per minute.

Now suppose five users repeatedly click an AI button and collectively generate 50 requests in one minute.

The group of five may create substantially more pressure than the group of 15.

AI concurrency therefore depends on:

1. Calls per user per minute
2. Number of model calls behind each visible action
3. Prompt length
4. Output length
5. Model selected
6. Whether RAG or embeddings are involved
7. Whether requests generate images or audio
8. Whether the application retries failed requests

The attached report’s 10–15-user observation should be interpreted as a warning about heavy simultaneous AI sessions, not a limit on the number of people who can be signed in or browsing the application.

A better AI capacity formula

The useful formula is:

Estimated simultaneous AI-active users = workspace calls-per-minute limit ÷ average calls per user per minute

For example, assume hypothetically that a workspace can process 60 calls per minute:

1. At one call per user per minute: approximately 60 AI-active users
2. At two calls per user per minute: approximately 30 AI-active users
3. At six calls per user per minute: approximately 10 AI-active users

This is only an example. Lovable’s actual limit must be measured or obtained from Lovable Support.

Practical capacity estimate for AIQuickPrompt

AIQuickPrompt is relatively lightweight because it primarily stores:

1. Prompt titles
2. Prompt text
3. Categories and folders
4. Search data
5. User accounts
6. Subscription information
7. Small settings and metadata

It does not depend on large images or video files.

The AI Optimize function is the principal scalability variable.

A defensible operating estimate is:

Normal activity

AIQuickPrompt should be able to support thousands of registered users without the number of database records alone becoming an immediate problem.

Concurrent text activity

A properly optimized version should reasonably target 100–500 concurrent users browsing, searching, copying and organizing prompts.

This is an engineering target for testing, not an official Lovable guarantee.

Concurrent AI activity

The application should initially be configured conservatively so that only a controlled number of AI calls can be active at once.

A sensible rollout would load-test:

1. 5 simultaneous AI requests
2. 10 simultaneous AI requests
3. 20 simultaneous AI requests
4. 40 simultaneous AI requests

At each stage, measure:

1. Success rate
2. Average response time
3. 95th-percentile response time
4. 429 error rate
5. AI credit consumption
6. Backend CPU and database usage

The point at which errors or unacceptable latency appear becomes the application’s measured capacity under that exact workload.

Lovable AI costs and credits

AI gateway usage is based on the model selected and the amount of work performed. Cost drivers include:

1. Input tokens
2. Output tokens
3. Model selection
4. Number of calls
5. Image generation
6. Audio processing
7. Document context
8. Embedding volume

Lovable currently provides monthly Cloud and AI grants on Free, Pro and Business plans, after which eligible usage draws from the workspace’s general credit balance. A depleted balance can cause AI or backend-dependent functionality to pause until credits are added.

For a prompt-optimization application, the best default model is usually not the most powerful model available. A smaller model may be sufficient for:

1. Rewriting a prompt
2. Improving structure
3. Adding constraints
4. Correcting grammar
5. Converting a rough request into a reusable template

Premium reasoning models should be reserved for complex workflows where their additional cost delivers measurable value.

Recommended actions for scaling safely

1. Stay on Lovable while actual usage is modest

There is no strong technical reason to migrate a lightweight application merely because it reaches 100, 500 or 1,000 registered users.

Registered accounts do not create constant load. Migration should be driven by measured constraints, not fear of a hypothetical user count.

Lovable itself recommends beginning with its integrated hosting and moving components only when real requirements justify the operational complexity.

2. Connect GitHub immediately

GitHub synchronization should be enabled before the application becomes business-critical.

Lovable supports two-way synchronization, code backup, local development and external deployment. The application code can also be downloaded directly on eligible paid plans.

This provides:

1. An external code backup
2. Version control
3. Easier developer collaboration
4. A staging path outside Lovable
5. Lower migration risk
6. The ability to deploy the frontend elsewhere

Connecting GitHub is valuable even when no migration is planned.

3. Add per-user AI rate limiting

The application should control how frequently each user can invoke AI.

Possible controls include:

1. One active optimization per user
2. A short cooldown between requests
3. Daily request allowances
4. Monthly credit allowances
5. Different limits by subscription plan
6. CAPTCHA or abuse checks for suspicious traffic
7. Maximum prompt length
8. Maximum AI output length

Client-side button disabling improves the interface, but enforcement must happen server-side because frontend controls can be bypassed.

4. Queue AI-intensive operations

AI calls should not be sent without coordination during traffic spikes.

A queue can:

1. Smooth sudden bursts
2. Prevent duplicated requests
3. Protect workspace rate limits
4. Prioritize paid users
5. Control image-generation concurrency
6. Retry temporary failures safely
7. Provide an estimated job status

For 429 responses, use exponential backoff with jitter rather than immediate repeated retries. Immediate retries can intensify the overload.

5. Cache and reuse results

When a user optimizes the same prompt with the same settings, the application may be able to reuse a previous result rather than paying for another AI call.

A cache key could be created from:

1. Original prompt
2. Selected optimization mode
3. Model
4. Language
5. Output settings
6. Prompt-template version

Caching reduces cost, latency and pressure on AI rate limits.

6. Monitor Cloud and AI usage separately

Lovable provides project-level usage information for:

1. Database
2. AI
3. Compute
4. Network
5. Storage
6. Realtime

The AI dashboard also shows model, duration, token usage, cost, request status and success rate.

Track at least:

1. Daily active users
2. Peak concurrent sessions
3. Database requests per minute
4. AI calls per minute
5. AI cost per active user
6. 429 and 402 errors
7. Login failures
8. Slow database queries
9. Storage growth
10. Network transfer
11. Subscription revenue relative to infrastructure cost

7. Resize Cloud only after confirming the bottleneck

Lovable allows paid users to resize Cloud instances from Tiny through Large.

Lovable generates an upgrade warning when:

1. Less than 10% of disk space remains
2. The IO budget falls below 50%
3. CPU load exceeds 80%

Before upgrading, slow queries and missing indexes should be investigated. Increasing compute does not correct inefficient application logic.

8. Use external hosting for video

For public videos:

1. Use YouTube, Vimeo or another dedicated video service.
2. Embed the player in the Lovable application.
3. Avoid committing large videos to the application repository.

For private or paid video:

1. Use a dedicated video or object-storage platform.
2. Generate multiple streaming resolutions.
3. Protect playback with signed URLs or tokens.
4. Store only metadata and authorization information in Lovable.

9. Consider your own AI provider account when needed

Lovable’s built-in AI connector is convenient because it manages provider credentials and billing.

However, Lovable also permits applications to call an AI provider directly from a backend Edge Function using the application owner’s API key. In that arrangement:

1. The AI provider bills the model usage directly.
2. Lovable charges only the normal backend compute involved in running the function.
3. Provider-specific quotas and rate limits replace the Lovable AI gateway limit.
4. The developer gains greater control over models, retries and observability.

Lovable confirms that Anthropic models, for example, are not part of its built-in connector but can be called directly through an Edge Function using a securely stored API key.

This hybrid approach may solve AI scaling limitations without requiring a complete platform migration.

10. Load-test before marketing campaigns

Do not wait for a viral campaign to discover the application’s real capacity.

Create a staging environment and test progressively:

1. Establish normal single-user behaviour.
2. Test 25 concurrent text users.
3. Increase to 50, 100, 250 and 500.
4. Test database searches separately from writes.
5. Test AI at 5, 10, 20 and 40 simultaneous calls.
6. Test sudden bursts rather than only gradual traffic.
7. Record latency, failures, credits and backend metrics.
8. Repeat after each major database or AI change.

The purpose is not to prove the platform can survive one brief test. It is to determine the sustainable load at which latency and error rates remain acceptable.

When should an application migrate from Lovable?

Migration should be considered when one or more of the following becomes persistent:

1. AI requests repeatedly encounter workspace rate limits.
2. Cloud costs become unpredictable relative to revenue.
3. Database performance remains insufficient after optimization and resizing.
4. The application needs infrastructure controls Lovable does not expose.
5. A large private-media pipeline is required.
6. The business needs specialized compliance or data-residency controls.
7. Advanced observability, networking or deployment requirements emerge.
8. The application requires provider-specific AI capacity or custom model routing.
9. A recurring platform limitation directly affects paying customers.

Lovable states that users own their code and data and can deploy outside the platform. Applications use standard technologies and can be moved to managed hosting or self-managed infrastructure.

Recommended migration path

A complete rebuild is usually unnecessary.

A lower-risk sequence is:

Phase 1: Backup and portability

1. Connect GitHub.
2. Document environment variables and secrets.
3. Export the database regularly.
4. Record authentication and storage configuration.
5. Maintain a list of external integrations.

Phase 2: External frontend staging

Deploy the frontend to a platform such as Vercel, Cloudflare Pages or another managed frontend service while leaving the current backend in place.

Phase 3: Separate AI infrastructure

Move AI calls to direct provider APIs or a dedicated AI gateway while retaining the existing database.

Phase 4: Move backend data if necessary

Migrate PostgreSQL data, authentication, storage files and Edge Functions to a managed Supabase project or another infrastructure provider.

Lovable warns that database exports do not automatically include storage files, secrets or reusable user passwords. Those components require separate migration planning.

Key takeaways

1. Lovable does not publish a fixed frontend visitor or bandwidth limit.
2. A text-only application is significantly easier to scale than an image-, video- or AI-heavy product.
3. Thousands of registered users do not imply thousands of concurrent backend requests.
4. Hundreds of concurrent text users are a reasonable testing target for a properly optimized application, but not a guaranteed Lovable capacity figure.
5. The built-in AI connector is the most likely bottleneck for AIQuickPrompt because its exact workspace requests-per-minute limits are not public.
6. The report’s 10–15 heavy AI sessions should be viewed as an anecdotal early-warning threshold, not a platform-wide user limit.
7. Existing images primarily increase network and storage usage; AI-generated images additionally consume model credits.
8. Public video should normally be hosted externally.
9. AI video-generation products require an asynchronous external provider and job queue.
10. GitHub sync, monitoring, rate limiting, caching and load testing should be implemented before major growth.
11. Migration should follow measured constraints rather than an arbitrary account count.

FAQ

Can Lovable handle 1,000 users?

Yes, 1,000 registered or monthly active users should not inherently be a problem for a lightweight application. What matters is how many are active simultaneously and what operations they perform.

Lovable does not publish an official guarantee for 1,000 concurrent backend-active users.

Can Lovable handle 1,000 concurrent visitors?

Lovable states that published applications automatically scale and that plans do not cap visitors, requests or bandwidth. Static or mostly frontend traffic is therefore designed to scale automatically. Backend-heavy activity must still be measured separately.

Does Lovable have a concurrency limit?

No universal concurrency number is publicly documented. Frontend traffic is uncapped at the plan level, while backend capacity depends on instance size, application design and workload.

The AI connector has workspace-level requests-per-minute limits, but Lovable does not publish their numerical thresholds.

Is the Lovable limit only 10–15 users?

No. The 10–15 figure in the attached report concerns anecdotal observations of simultaneous heavy AI sessions. It does not mean only 10–15 users can browse, search or use the application.

How many users can AIQuickPrompt handle?

A well-optimized AIQuickPrompt application should be able to maintain thousands of registered accounts and target hundreds of concurrent text users.

Simultaneous AI optimization must be load-tested because the built-in AI gateway’s numerical rate limit is not public.

Will images make a Lovable app slow?

A few optimized images should not create a major problem. Large, uncompressed images, excessive galleries and repeated private-file downloads increase network usage and can reduce page performance.

Should videos be uploaded directly to Lovable?

For public playback, Lovable recommends embedding videos from a dedicated hosting service such as YouTube instead of storing large video files in the project repository.

Can a Lovable app use OpenAI or Gemini?

Yes. Lovable’s built-in AI connector supports multiple OpenAI and Google models for chat, reasoning, image generation, embeddings, speech and transcription. It manages the provider credentials automatically.

Can a Lovable app use its own AI API key?

Yes. A provider can be called directly through a secure backend Edge Function with the key stored as a secret. The provider then bills AI usage directly, while Lovable charges the normal Cloud usage required to run the backend function.

Can a Lovable application be migrated later?

Yes. Lovable supports GitHub synchronization, code downloads on eligible plans, database exports and external deployment. Storage files, secrets, authentication and external services still require separate migration work.

Conclusion

Lovable should not be evaluated through one arbitrary number such as “15 users,” “100 users” or “1,000 users.”

Its frontend hosting is designed to scale automatically and does not have a published plan-level visitor or bandwidth cap. Lightweight text applications can reasonably target hundreds of concurrent sessions, provided their database queries and security policies are well designed.

The more important limitation for AIQuickPrompt is the built-in AI connector. Lovable applies workspace-level model-call limits, but it does not disclose the exact requests-per-minute thresholds. The only reliable method for establishing capacity is controlled load testing combined with the Cloud and AI usage dashboards.

For AIQuickPrompt, the recommended course is to remain on Lovable during initial growth, connect GitHub immediately, optimize database requests, implement server-side AI throttling, queue expensive operations and monitor the cost and success rate of every AI feature.

Images can be supported effectively with compression, thumbnails and proper storage. Public video should be hosted externally. AI image or video generation should use controlled queues and explicit subscription allowances.

Migration should begin only when measured usage demonstrates a real constraint. By preparing code backups, direct AI-provider options and a staged external-hosting environment in advance, the application can grow without waiting for an emergency.

Sources

1. How Lovable Hosts Your App

Publisher: LovableURL: https://docs.lovable.dev/features/hostingSupports: Global frontend delivery, automatic traffic scaling, no plan-level visitor or bandwidth cap and the distinction between frontend hosting and backend activity.

2. Lovable Cloud

Publisher: LovableURL: https://docs.lovable.dev/features/cloudSupports: Built-in database, authentication, storage, Realtime, Edge Functions, Supabase foundation and automatic backend scaling.

3. Advanced Settings

Publisher: LovableURL: https://docs.lovable.dev/features/advanced-settingsSupports: Tiny-to-Large Cloud instance sizes, resizing guidance, CPU, disk and IO warning thresholds, database exports and migration limitations.

4. AI Features for Your App

Publisher: LovableURL: https://docs.lovable.dev/features/aiSupports: Supported AI capabilities and models, secure backend calls, streaming, workspace rate limits, 429 and 402 responses, AI monitoring and direct-provider API options.

5. Credits and Usage

Publisher: LovableURL: https://docs.lovable.dev/introduction/credits-and-usageSupports: Run credits, Cloud and AI billing, cost drivers, network, database, storage and model-call usage.

6. Project Usage and Costs

Publisher: LovableURL: https://docs.lovable.dev/features/project-usageSupports: Usage categories for database, AI, compute, network, storage and Realtime.

7. Chat With Lovable to Build Your App

Publisher: LovableURL: https://docs.lovable.dev/features/projects/chatSupports: Image and video handling, file-attachment limits, image compression guidance and recommendation to host videos externally.

8. Sync Your Lovable Project With GitHub

Publisher: LovableURL: https://docs.lovable.dev/integrations/githubSupports: Code ownership, two-way GitHub synchronization, backups, local development and external deployment.

9. Deployment, Hosting and Ownership Options

Publisher: LovableURL: https://docs.lovable.dev/tips-tricks/deployment-hosting-ownershipSupports: Code and data portability, external hosting and Lovable’s open-technology architecture.

10. Deploying and Hosting Outside Lovable

Publisher: LovableURL: https://docs.lovable.dev/tips-tricks/external-deployment-hostingSupports: Staged migration options, external frontend hosting, managed backends and self-hosted infrastructure.

11. Connect to Supabase

Publisher: LovableURL: https://docs.lovable.dev/integrations/supabaseSupports: Supabase database and storage integration, file uploads, RLS requirements and external backend management.

12. Compute and Disk

Publisher: SupabaseURL: https://supabase.com/docs/guides/platform/compute-and-diskSupports: PostgreSQL compute sizing, database connections, connection pooling, disk throughput and the importance of load testing.

13. Security Best Practices for Lovable Apps

Publisher: LovableURL: https://docs.lovable.dev/tips-tricks/security-best-practicesSupports: Server-side business logic, secrets management, authentication and row-level database security.

14. Integrate Any API With Lovable

Publisher: LovableURL: https://docs.lovable.dev/integrations/any-apiSupports: Secure external API integrations through Edge Functions and server-side secrets.

15. Lovable Pricing

Publisher: LovableURL: https://lovable.dev/pricingSupports: Current plan credits, Cloud and AI grants, usage-based application costs and unlimited workspace members.