Technology Stack Advisor

Stack is an AI tool for choosing technologies for a build and for making decisions inside an existing technology stack.

Describe the build, what is already in place, or the technology choice to be made, and Stack matches the build, team, stage, budget, existing infrastructure and expected scale to appropriate languages, frameworks, databases, platforms and services.

Stack recommends a complete stack or resolves one decision inside an existing one, covering frontend, backend, database, hosting, authentication, storage, payments, search, real-time features, AI and background jobs.

Every recommendation carries why the technology fits, what the choice trades away, which combinations conflict, where the technology may become limiting later, and what moving away from it would involve.

tech stack picker

• Matches a planned build to specific technologies across the layers actually required, rather than returning a list of popular tools.

frontend framework selection

• Recommends a frontend framework or application approach for browsers, dashboards and user-facing interfaces, matched to the interface, the team and the rendering requirements.

backend selection

• Recommends a language, framework and server-side approach matched to the workload, the team's existing skills and the operational burden the build can carry.

database selection

• Matches a database to the actual data shape, access patterns, consistency requirements and operational capacity, rather than to a default.

hosting and deployment platform selection

• Recommends where the application runs — traditional hosts, cloud platforms, serverless systems or always-on infrastructure — against traffic pattern, cost and operational tolerance.

authentication selection

• Recommends managed authentication, custom authentication or an identity provider, and states what each commits the build to.

file and media storage selection

• Recommends where files, images, video and documents live, and what that means for delivery, cost and access control.

payment provider selection

• Recommends payment infrastructure for one-off payments, subscriptions, marketplace payouts and entitlement, matched to jurisdiction and business model.

search selection

• Determines whether database search is enough or a dedicated search system is required, and which one fits the data and query patterns.

real-time architecture selection

• Recommends how live updates, chat, presence and collaborative editing are delivered, and what each approach requires of the rest of the stack.

offline and sync architecture selection

• Recommends local storage, synchronisation and conflict-resolution approaches for builds that must work without a connection.

AI stack selection

• Recommends hosted models, self-hosted models, vector storage and background processing for AI-powered features, and what each costs to run.

background job and queue selection

• Recommends how scheduled work, long-running tasks and asynchronous processing are handled without blocking the application.

technology comparison

• Evaluates competing options against the build instead of treating one technology as universally better.

team and skill fit

• Accounts for a solo developer, small team, growing company or outside contractor, including familiarity, hiring pool and operational burden.

MVP against production

• Separates what suits an MVP from what has to survive production use.

over-engineering check

• Identifies choices that create unnecessary complexity too early.

monolith or microservices

• Determines whether a single application, a modular monolith or separate services fits the team size, domain boundaries and operational capacity.

managed against self-hosted

• Weighs managed services against self-hosting on setup speed, operations, cost, control, portability and vendor dependence.

build, buy or extend

• Determines whether a capability should be built, bought outright, or bought and extended through APIs and configuration.

vendor lock-in assessment

• States what depending on a platform commits the build to, and what that dependency costs if the product direction changes.

running cost estimate

• States what a stack costs to operate at the expected scale, which costs grow with users, traffic or data, and where a cheap early choice becomes expensive later.

compliance and data residency fit

• Identifies where regulatory, data-residency or industry requirements rule technologies in or out before any other criterion applies.

stack extension

• Finds compatible ways to add real-time updates, offline operation, search, payments, file storage, background jobs or AI without rebuilding the existing system.

conflict and overlap detection

• Flags overlapping responsibilities, incompatible architectural assumptions, duplicated auth or data logic, platform constraints and combinations that create avoidable maintenance.

scaling bottleneck identification

• Identifies which part of an existing architecture is limiting performance, scale, cost or capability.
• Shows what becomes difficult first as traffic, data, users, complexity or team size grows.

options before migration

• States what options remain before a larger migration becomes necessary, and what staying on the current stack costs.

rehost, replatform or rebuild

• Determines which kind of move the situation actually calls for — moving the same system, moving it onto managed services, or rebuilding it — and what each one would cost.

migration assessment

• Explains what moving away from a framework, database, platform or managed service would affect, and which dependencies make the migration easy or hard.

hiring and handoff specification

• States which technology decisions should be made before hiring, which to leave to the developer, and what a contractor or agency needs to be told.

requirements to technology translation

• Works from what a product needs to do when the person planning the build does not know the technical terms to ask for.

Describe the build, the technology decision being made, or the existing stack and what needs to change.

Include the product type, web or mobile requirements, team size, existing skills, timeline, budget, current stack, expected traffic or data scale, real-time or offline requirements, compliance or data-residency constraints, and tolerance for managed services.

Where a missing constraint would materially change the recommendation, Stack names the decision point before narrowing to a specific technology.

choose a complete stack

questions people ask:

"Solo developer building a SaaS app who needs to ship fast." · "What stack suits a two-sided marketplace?" · "Technologies for an AI-powered web app." · "What does an offline-capable mobile app need?"

choose a frontend framework

questions people ask:

"React or Vue for a team of three?" · "Is Svelte a risk for a product we need to hire for?" · "Does this need a framework at all?" · "Next.js or plain React?"

choose a backend

questions people ask:

"What backend for a team that only knows JavaScript?" · "Node, Python or Go for this?" · "Do we need a separate backend or will the framework do it?" · "What runs the API?"

choose a database

questions people ask:

"What database suits an appointment-booking product?" · "Postgres or MongoDB for event data?" · "Do we need a database or is a spreadsheet enough for now?" · "SQLite in production — is that mad?"

choose hosting and deployment

questions people ask:

"Vercel, Railway or a VPS?" · "What is the cheapest way to run this reliably?" · "Serverless or a long-running server?" · "Where should a Next.js app actually live?"

choose an authentication provider

questions people ask:

"Clerk, Auth0 or Supabase Auth?" · "Build our own auth or use a provider?" · "What do we need if enterprise customers want SSO?" · "What does auth cost at 100,000 users?"

choose file and media storage

questions people ask:

"Where should user uploads go?" · "S3 or the storage that comes with our platform?" · "How do we serve video without the bill exploding?" · "What handles image resizing?"

choose a payment provider

questions people ask:

"Stripe or Paddle?" · "Who handles sales tax and VAT for us?" · "What do we need to pay other people out?" · "What takes payments in a country Stripe doesn't cover?"

add search

questions people ask:

"Is database search enough or do we need a search engine?" · "What is the simplest way to add search to a recipe site?" · "Algolia, Typesense or Postgres full-text?" · "What does search cost at our catalogue size?"

add real-time features

questions people ask:

"MERN app that needs live collaborative editing." · "What powers presence and typing indicators?" · "WebSockets or a managed real-time service?" · "Why does an edge runtime conflict with a WebSocket requirement?"

work offline and sync

questions people ask:

"It has to work with no signal — what does that mean technically?" · "Can offline support be added to a Next.js app without a rebuild?" · "What handles conflicts when two devices edit the same thing?" · "What stores data on the device?"

choose an AI stack

questions people ask:

"Hosted model or self-hosted?" · "Do we need a vector database?" · "What does inference cost at our volume?" · "Where does AI work sit — in the request or in a queue?"

run background jobs

questions people ask:

"What should run background jobs on a Django project?" · "BullMQ, Celery or Sidekiq?" · "How do we send emails without blocking the request?" · "What handles scheduled tasks?"

compare two technologies

questions people ask:

"React or Vue for a team of three?" · "Postgres or MongoDB for event data?" · "Serverless or a long-running backend?" · "Supabase or a custom backend?"

decide between a monolith and services

questions people ask:

"Does a five-person team need microservices yet?" · "What is a modular monolith and is it enough?" · "When is splitting the backend actually justified?" · "Everyone says microservices — do we need them?"

work out what to build and what to buy

questions people ask:

"Build our own auth or use a provider?" · "Is this worth building or should we buy something?" · "Can we buy most of it and extend the rest?" · "What is worth owning and what is not?"

weigh managed against self-hosted

questions people ask:

"Managed database or self-hosted for a small team?" · "Is vendor dependence acceptable for an early-stage startup?" · "When does running your own Kubernetes cluster make sense?" · "What is given up by using a backend-as-a-service?"

work out what it will cost to run

questions people ask:

"What does this stack cost a month at 10,000 users?" · "Which of these bills grows fastest?" · "Is the free tier going to be enough?" · "What is the line that surprises people?"

meet compliance and data residency requirements

questions people ask:

"What can we use if we handle health data?" · "Who will sign a BAA?" · "EU customer data cannot leave the EU — what changes?" · "What does PCI compliance rule out?"

decide between mvp and production choices

questions people ask:

"Is a no-code backend good enough for a paying product?" · "What is enough to validate an idea in six weeks?" · "Will choosing Firebase now force a rewrite later?" · "What should not be over-engineered before launch?"

extend an existing stack

questions people ask:

"How should subscriptions be added to a Rails app?" · "Can we add a mobile app to what we already have?" · "What does adding search break?" · "How much of the current stack survives this?"

find conflicts in a stack

questions people ask:

"Does Clerk overlap with an existing session system?" · "Am I duplicating auth across two services?" · "Are two of these doing the same job?" · "Has this stack become too complicated for one maintainer?"

deal with a stack hitting its limits

questions people ask:

"A Postgres instance is slowing down — what changes first?" · "What breaks first at ten times the current traffic?" · "When is it time to move off Lambda?" · "Is the platform the ceiling, or is it our code?"

work out what staying or moving costs

questions people ask:

"What would migrating off this platform actually involve?" · "What is it costing us to stay?" · "Is this a rehost, a replatform or a rebuild?" · "How much of the work is the data?"

plan a build before hiring a developer

questions people ask:

"What stack should a contractor be told to use?" · "I don't code — what should be specified before hiring?" · "Which choices need deciding before getting a quote?" · "What should be left for the developer to choose?"

turn what the product needs into technology

questions people ask:

"Users need to work on the same document at once — what does that require?" · "I need to take payments and pay other people out." · "It has to keep working when the internet drops." · "What do I actually need for this to work?"

understand the decision itself

questions people ask:

"What is a tech stack?" · "What separates frontend from backend?" · "What does serverless mean for a small app?" · "Why would a simple booking site need a database?"

the recommendation

• a restatement of the build and the constraints shaping the decision;
• the recommended technology stack or individual technology choice;
• specific technologies for the relevant frontend, backend, database, hosting, authentication, storage, payments, search, real-time, offline, AI and background job layers;
• why each recommendation fits the described build;
• which constraints materially affected the recommendation;

the trade-offs

• the tradeoffs introduced by each important choice;
• meaningful alternatives and how their tradeoffs differ;
• whether a managed service or self-hosted approach fits the situation better;
• whether the capability should be built, bought or bought and extended;
• whether the current choice creates meaningful vendor lock-in;

cost and constraint

• what the stack costs to run at the expected scale;
• which costs grow with users, traffic, data or team size;
• which technologies are ruled out by compliance, data residency or industry requirements;

how the pieces sit together

• which technologies fit well together;
• which technologies overlap, conflict or create unnecessary duplication;
• which parts of an existing stack can remain when a new capability is added;

where it runs out

• where the current stack is likely to hit a practical ceiling;
• what becomes limiting first as scale increases;
• what staying on the current stack costs;
• whether the move required is a rehost, a replatform or a rebuild;
• what moving away from a technology later would involve;

what to decide now

• which MVP choices are likely to carry into production;
• which decisions should be made before development, hiring or handoff;
• which decisions can safely be left to whoever builds it;

where to go next

• follow-up paths for narrowing a layer, comparing options, changing constraints or examining migration and scale.

why no stack is universally best

The right technology depends on what is being built, who is building it, how quickly the product has to ship, what already exists and which constraints matter most. A recommendation that ignores those is a preference wearing the clothes of an answer.

why the stack a team can run beats the ideal one

Familiar technologies reduce implementation time, debugging cost and hiring difficulty, and technical elegance has limited value when nobody available can maintain the result. For most early products the specific choice matters far less than whether the person building it has shipped with it before.

the two ways early architecture goes wrong

Over-engineering creates complexity for scale that may never arrive; under-engineering forces a rebuild before the product has room to grow. A small number of early choices decide which side becomes expensive, and most of them are about data and identity rather than the parts that are visible.

why team size decides architecture more than traffic does

Splitting a system into services multiplies what has to be deployed, monitored, versioned and kept consistent, and that cost is paid in coordination. Under roughly ten engineers a single well-structured application almost always wins; the pressure to split usually arrives from team growth and deployment contention rather than from load.

why some layers are decided by the layer above

Choices are not independent. The backend language usually decides the job queue, the framework usually narrows the hosting, the database often brings authentication and storage with it. Picking each layer in isolation is how a stack ends up with two things doing the same job.

how to read a technology comparison

Every comparison ends by saying it depends on your context, which is true and useless without knowing which parts of the context decide it. Benchmarks measure the thing easiest to measure rather than the thing that will cost you. Market share predicts hiring, not fit. What actually decides most comparisons is which one the person building it already knows, what the surrounding stack expects, and which of the two would be harder to leave — and those are rarely the criteria the comparison is organised around.

what managed services actually exchange

Managed databases, authentication, hosting and backend platforms reduce what a team operates directly, at the cost of recurring price, platform constraints and migration difficulty. The exchange is operational time now for optionality later, and it is a good trade whenever the time saved is time the team does not have.

why vendor lock-in is a trade rather than a failure

A platform is worth depending on when the time and operational burden saved outweigh portability. The question is not whether lock-in exists but whether the dependency conflicts with the likely direction of the product, and how much of the system would have to be rewritten if it did.

why the bill grows differently from the product

Infrastructure pricing is metered on things that do not track revenue — bandwidth, compute seconds, monthly active users, storage, seats. A stack that costs nothing at launch can become the largest fixed cost in the business without the product changing, and the surprise is almost always one meter running far past its included allowance rather than the base price.

why each layer bills on a different meter

Hosting charges for bandwidth and compute seconds, authentication for monthly active users, databases for storage and connections, search for documents and queries, AI for tokens. None of those move together. A product with heavy media grows the hosting line while the database stays flat; a free-tier product with a million signups grows the auth line while nothing else does. Knowing which meter your product actually spins is what makes an estimate worth anything.

why compliance is decided before anything else

Health data, payment data, and data that cannot leave a jurisdiction remove technologies from the list regardless of how well they fit otherwise. A vendor either will sign the agreement or will not, and finding that out after the build is the expensive order to do it in.

why some decisions are much harder to reverse

Changing a presentation layer is painful; migrating core data, authentication rules or deeply embedded platform assumptions affects the entire system. A useful rule is that anything taking more than a day to undo deserves to be decided deliberately and written down.

what makes a migration hard

Difficulty is not proportional to how much code changes. It is proportional to how many other things hold a reference to what is moving. Data is the hardest because everything points at it, authentication is next because identity is embedded in every request, and anything with a foreign key into another provider's system — a payment customer ID, a storage path, a webhook subscription — has to be reconciled rather than copied.

how adding one capability changes the architecture

Real-time communication, offline operation, heavy media, AI workloads or strict compliance introduce requirements the original stack was never designed to satisfy. Each of them changes where state lives, and anything that changes where state lives changes the architecture regardless of which product it was added to.

why the limiting layer is found before the stack is replaced

A slow or expensive system does not automatically need a new architecture. The limiting layer can be a query, a connection pattern, a cache, a service boundary or a workload that changes without touching the rest, and replacing the platform when the ceiling is in the code moves the problem without solving it.

what staying costs, as against what moving costs

A migration estimate says what switching costs; it does not say what staying costs. That number is visible in the work being done to route around the current stack — the tickets that exist only because of a platform limitation, the integrations that get more expensive each time one is added, the things that cannot be shipped without a workaround.

technology decisions run across every layer of a build

Technology decisions run across every layer of a build:

• complete stacks: the technologies needed to build and run a product end to end;
• frontend: frameworks and application approaches for browsers, dashboards and user-facing interfaces;
• backend: languages, frameworks, APIs, application servers and server-side architecture;
• databases: relational, document, local and managed data stores matched to the actual data and access patterns;
• hosting and infrastructure: deployment platforms, cloud services, serverless systems and always-on infrastructure;
• authentication: managed auth, custom auth and identity systems appropriate to the build;
• storage and media: files, images, video, documents and the delivery around them;
• payments and commerce: payment providers, subscriptions, marketplace payouts, tax handling and entitlement;
• search: database search, dedicated search systems and managed search services;
• real-time systems: WebSockets, managed real-time providers, chat, presence, collaboration and live updates;
• offline-first builds: local storage, sync, reconciliation and multi-device data requirements;
• AI-powered products: hosted models, self-hosted models, vector search, background processing and AI workload architecture;
• background jobs: queues, workers, scheduled tasks, retries and long-running work outside the request;
• supporting services: email, notifications, analytics, error monitoring and logging;
• architecture patterns: monoliths, modular monoliths, microservices, serverless systems, event-driven systems and managed backends;
• technology comparisons: frameworks, databases, languages, hosting platforms and services competing for the same role;
• cost: what a stack costs to run, which meters grow, and where a cheap choice becomes expensive;
• compliance and data residency: HIPAA, GDPR, PCI and industry requirements that rule technologies in or out;
• MVP versus production: what can remain, what needs replacing and which early decisions are expensive to reverse;
• build, buy or extend: when to own a capability, when to buy one, and when to buy most of it and extend the rest;
• managed versus self-hosted: when operational simplicity is worth cost or vendor dependence;
• stack extension: adding a capability without creating overlap or conflict;
• scaling: what breaks first and what should change before redesigning the system;
• migration: rehosting, replatforming and rebuilding, and what each one actually costs;
• hiring and handoff: defining a sensible stack before hiring a developer or handing a build to an outside team;
• build types: SaaS, marketplaces, dashboards, mobile apps, offline-first products, AI applications and content sites.