FAQ

Look at shipped products, not certificates. A strong full stack developer can show you live projects, explain the architecture decisions behind them and talk comfortably about both the database and the pixel. Review case studies, have a short technical conversation about your idea, and judge how clearly they explain tradeoffs. If they make complexity sound simple, that is the one to hire.

The best senior developers are rarely on bidding marketplaces. Look for people who publish real case studies, write about their work and can be verified through live products and LinkedIn history. Referrals and direct outreach to developers whose portfolio matches your product type beat posting a generic job ad.

An experienced freelance developer gives you senior hands on your code at a fraction of agency rates, direct communication and no account-manager layer. An agency makes sense when you need five or more people immediately. For MVPs, SaaS products and AI features, one senior full stack developer usually ships faster than an agency team of juniors.

Three things: evidence of shipped production work, communication you actually enjoy, and honesty about what they do not know. Skills can be verified in an hour; those three qualities decide whether your project succeeds over months.

Senior freelance full stack developers typically range from $30 to $120 or more per hour depending on region and specialty, with AI experience at the upper end. A focused MVP usually lands between $5,000 and $30,000. The real question is cost per outcome: a senior at twice the rate who ships in half the time with fewer defects is the cheaper developer.

A marketing site with a CMS runs roughly $2,000 to $8,000, a custom web application $8,000 to $40,000, and complex platforms beyond that. Cost tracks complexity: user roles, payments, integrations and AI features move the number more than page count ever does.

A SaaS MVP estimate depends on the workflow, integrations, roles, quality bar and whether billing is part of what you need to validate. I scope those assumptions first, then provide a range with explicit inclusions, exclusions and decision points. Reducing scope rather than engineering quality is usually the safest cost lever.

A production chatbot grounded in your own data with RAG typically costs $3,000 to $15,000 to build, plus monthly model API costs that usually start under $100 and scale with usage. The build cost is mostly in data preparation, evaluation and guardrails, not the chat window.

A focused MVP takes 4 to 10 weeks with an experienced developer. A content site takes 2 to 4 weeks. Complex platforms run three to six months. Timelines slip mostly from unclear requirements and slow feedback, not slow typing.

Six to ten weeks is realistic for authentication, billing, one core feature done well and an admin panel. Anyone promising two weeks is building a prototype, not a product. Anyone quoting six months for an MVP is building too much.

Scope and architecture first, then short build cycles with working software delivered every week or two, then launch and iteration. You should see running features early and often. If the first demo is scheduled for month three, change developers.

Weekly at minimum, with working software rather than status slides. A good developer shows progress in a staging environment you can click through, tells you what is next and raises risks early instead of the day before deadline.

Boring and proven wins: Next.js with React on the frontend, a Python or Node.js backend, PostgreSQL, Stripe for billing and a platform like Vercel or AWS for hosting. This stack hires easily, scales far beyond MVP stage and has answers on every corner of the internet.

Sell before you build: a landing page describing the product, honest conversations with ten potential customers and ideally pre-orders or signed letters of intent. If nobody bites on the promise, the product will not fix that. Build only after the pitch works.

No-code is excellent for validating demand and internal tools. Move to custom code when you hit no-code ceilings: complex logic, performance, AI features, or per-user costs that scale badly. Many good products start no-code and rebuild the proven core in custom code.

Use Stripe. It handles plans, trials, proration, invoices, tax and card recovery, and it integrates cleanly with modern stacks. The engineering work is mapping billing states to your product: what happens on upgrade, downgrade, failed payment and cancellation. That mapping is where experience matters.

Start with one workflow where AI removes obvious drudgery: summarizing, drafting, extracting or answering questions over your data. Ship it behind a feedback mechanism, measure whether users accept the outputs and expand from evidence. Bolting a generic chatbot onto the corner of the screen is the pattern to avoid.

Retrieval-augmented generation fetches relevant passages from your own content and hands them to the model as grounding before it answers. If your chatbot must be accurate about your product, policies or data, you need RAG or something like it. Without grounding, the model improvises, confidently.

Far less than people assume. RAG works with whatever documents you already have, and modern models handle most tasks without custom training. Custom model training only enters when you have thousands of labeled examples and a task generic models fail at.

Design your integration to be model-agnostic, then choose per task: a top-tier model for complex reasoning, a fast cheap model for classification and simple extraction. Providers leapfrog each other every few months; the products that win switch models in a config file, not a rewrite.

Next.js for anything public-facing: server rendering gives you the SEO and load speed plain React cannot, plus routing, image optimization and API routes in one framework. Plain React remains fine for internal dashboards where search engines do not matter.

Django when you want a complete product fast: admin panel, authentication and ORM included. FastAPI when you are building lean, high-performance APIs, especially around AI and async workloads. Strong teams often run both, and I have shipped production systems where they work side by side.

Both are excellent; the decision is ecosystem fit. Python wins for AI-heavy products because the entire ML ecosystem lives there. Node.js wins for realtime features and teams that want one language across the stack. The wrong choice costs far less than choosing slowly.

Content that genuinely answers what people search, served fast, structured with clean semantics and schema markup, on a site Google can crawl effortlessly. Technical SEO is table stakes; the compounding wins come from consistently useful content and pages that load instantly.

Start with a responsive web app unless you need device hardware, offline depth or app-store presence as distribution. A progressive web app installs to the home screen and sends notifications at a fraction of native cost. Ship native when users prove they want your product in their pocket.

Mobile backends face flaky networks, aggressive caching, token-based authentication, push notifications and API versioning, because users do not update apps on your schedule. I build backends that serve web, iOS and Android from one API designed around those realities.

An API is the contract through which apps, partners and services talk to your backend. A clean API lets one backend power your website, mobile apps and future integrations without duplicating logic. Products with messy APIs pay for it in every feature that follows.

Through Apple and Google's push services, coordinated by your backend: device token management, notification templates, delivery scheduling and user preferences. The engineering is straightforward; the product discipline of not spamming users is the hard part.

Use a framework's built-in protections, validate every input on the server, keep dependencies updated, store secrets outside code and put authentication on every private endpoint. Most breaches exploit basics done sloppily, not sophisticated attacks. Security is a habit, not a feature.

OAuth is the standard that powers secure logins, including sign-in with Google and API authorizations, without your app ever seeing passwords it should not hold. Login is the front door to everything your users trust you with; it is the wrong place to improvise.

Only as salted hashes using algorithms designed to be slow, like bcrypt or argon2, so stolen databases stay useless. Any system that can email you your old password is storing it wrong. Better yet, modern auth libraries make doing this correctly the default.

Constantly, because attacks are automated and indiscriminate: bots scan every site on the internet for outdated software and exposed admin panels. Small sites are not targets; they are inventory. Updates, strong authentication and backups remove you from the easy pile.

Yes, both. I take on freelance builds end to end and I am open to full-time senior engineering roles, working remotely with teams worldwide. The fastest way to start is the contact form at ijazkhan.com/contact.

Production SaaS platforms, AI and LLM applications, healthcare products, real estate platforms, e-commerce stores and WebAR tools. Recent work includes DentaSmart, an AI dental analysis platform, and OnMLS, a flat-fee real estate listing platform. Case studies live at ijazkhan.com/projects.

Backend: Django, FastAPI, Node.js, Express and NestJS. Frontend: React and Next.js with TypeScript. Plus PostgreSQL, LLM and RAG workflows, Stripe billing, Docker and cloud deployments on AWS and Azure.

Ijaz has more than five years of professional full-stack experience. At Zigron Pakistan, his work on DentaSmart covers backend microservices, web applications, subscription workflows and AI integrations. This is team-based employment work, not a claim of sole ownership of DentaSmart, its mobile application, clinical models or product outcomes.