$10K–$60K typical projects
Direct answer
PostgreSQL database development with me runs $10K–$60K — the low end for schema design and query optimization on a single application database, the high end for large migrations, multi-tenant architectures, or rescuing a database that's become the bottleneck. I have 7+ years of production delivery, I'm Top Rated on Upwork with $100K+ earned and verified client reviews, and I've built Postgres-backed systems behind apps used by millions of users. Hiring starts with a read-only look at your schema and slow-query stats, from which I give you a concrete findings-and-fixes plan before you commit to anything.
Almost every scaling crisis I get called into is a database problem wearing an application costume — slow pages, timeouts, and surprise bills that trace back to schema decisions made in week one. I design, tune, and rescue PostgreSQL databases so they stay boring: fast queries, safe migrations, and a data model your next three features fit into without a rewrite.
You need an MVP or v2 shipped on budget with someone who makes architecture decisions and owns delivery end-to-end.
You need a senior IC to augment the team, rescue a codebase, or lead mobile + AI integration without months of hiring.
You need a reliable senior subcontractor for client projects — clear communication, store-ready quality, white-label friendly.
30 minutes — goals, stack, timeline, budget range.
Fixed milestones, clear deliverables, start date.
Weekly demos, async Slack updates, production standards.
Store launch, documentation, knowledge transfer.
A growing SaaS sees pages that took 200ms now taking 8 seconds, and the team is debating a caching layer. I profile the workload with query statistics, find the handful of queries doing sequential scans over millions of rows, add the right composite indexes, rewrite two ORM-generated monsters, and fix a connection-pool misconfiguration. Response times return to normal without new infrastructure.
A funded startup is about to build and wants the data model right before code exists. I design the schema around their actual access patterns — tenancy, permissions, soft deletes, audit history — with constraints that make bad data impossible rather than merely discouraged. The team builds on it for two years without a structural migration, which is the entire point.
A company needs to restructure core tables holding years of production data — the kind of change that locks tables and takes the app down if done naively. I plan an online migration: new structure built alongside old, dual writes, backfill in batches, verification queries, then a cutover measured in seconds. Customers never notice, which is what success looks like.
Three kinds of work, often blended. Design: modeling a schema for a new product or major feature — tables, constraints, indexes, and tenancy strategy chosen against your real access patterns rather than textbook normalization. Performance: profiling a live database with its own statistics, then fixing the actual bottlenecks, which are almost always missing or wrong indexes, ORM-generated query patterns, N+1 loops, and connection pooling — in that order of likelihood.
Migration: restructuring production data safely, whether that's a schema overhaul, a move from another database into Postgres, or splitting a monolith table before it hits a ceiling. Every engagement ends with the same deliverables regardless of type: migration scripts that run in CI, documentation of why the schema is shaped the way it is, and a monitoring setup so the next slow query announces itself before your customers do.
Data volume and uptime requirements set the floor. Tuning a 5GB database you can briefly take offline is quick, honest work at the bottom of the range. Restructuring a terabyte-scale database that can never stop serving traffic requires online-migration engineering — dual writes, batched backfills, verification, rehearsed rollback — and that rigor is where budgets grow, justifiably.
The second driver is how much application code touches the schema. Adding an index touches nothing; changing a core table's shape means coordinating every query that reads it, which turns a database project into a database-plus-application project. Multi-tenancy decisions, compliance requirements like auditability and retention, and pgvector or search workloads each add scope. What doesn't drive cost much is table count — a hundred well-behaved tables are easier than five pathological ones.
The most expensive mistake is trusting the ORM blindly. ORMs are fine tools that generate terrible queries when nobody looks — N+1 patterns, unindexable filters, loading whole rows to read one column. Every serious performance engagement I've done has found ORM-generated queries in the top offenders, and the fix is usually a better query or a targeted index, not abandoning the ORM.
Second: adding indexes speculatively instead of measuring. Unused indexes slow every write and bloat storage while helping nothing; the query statistics tell you exactly what's needed, so use them. Third: postponing constraints. Teams skip foreign keys and check constraints 'for flexibility,' then spend weeks cleaning impossible data that constraints would have rejected for free. Fourth: treating the database as a queue or a cache because it's the tool already connected — Postgres can do a lot, but every workload you bolt on competes with your transactional traffic.
Ask them to walk through how they'd diagnose a slow application, and listen for measurement before prescription: pg_stat_statements, execution plans, and actual timings — not an immediate pitch for caching, replicas, or a different database. Anyone who proposes solutions before seeing your workload statistics is guessing at your expense.
Ask how they'd deploy a schema change to a table with a hundred million live rows. The answer must include lock behavior, batched backfill, and rollback strategy; someone who says 'run the ALTER in a maintenance window' hasn't done this at scale where windows don't exist. Ask what they'd document when leaving — a strong consultant leaves runbooks, migration scripts in CI, and reasoning, not just a faster database and an invoice. And check they'll say 'Postgres is fine, your queries aren't' when true, because it usually is.
If your database is under 10GB and your traffic is modest, you almost certainly don't need a consultant — you need the slowest three queries from pg_stat_statements and an index or two, which your own team can do in an afternoon with any decent guide. I'd rather tell you that in one call than bill you for it.
You also don't need a Postgres specialist to pick a database for a prototype: choose managed Postgres from any major provider and move on; the differences that matter show up later. And be skeptical of anyone — including me — recommending a migration off Postgres for performance. Genuine outgrowths exist, but they're rare; most 'Postgres can't handle it' stories are missing indexes, connection mismanagement, or one table doing a job a queue should. The database that needs replacing is usually the schema, not the engine.
You approve milestones and a price before any build starts — no open-ended hourly surprises.
You see running software each week, not status reports, so you can course-correct early.
The person who scopes the work is the person who builds it — no junior layers, no agency markup.
Top Rated on Upwork with public client reviews and $100K+ earned, plus contributions to Expensify. Check the receipts before you commit.
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A focused performance engagement — profiling the live workload, fixing the top slow queries, correcting indexes and connection pooling — typically runs $10K–$20K and takes two to three weeks. Full engagements involving schema redesign or online migrations of large production tables run $30K–$60K. Most tuning work pays for itself quickly in smaller instance sizes and in the engineering time your team stops spending on firefighting.
A zero-downtime restructuring of core production tables typically takes three to six weeks end to end: a week of planning and rehearsal against a copy of production, one to two weeks building dual-write paths and batched backfills, then verification and a cutover measured in seconds. The calendar time is dominated by careful verification, not the cutover itself — which is exactly how you want it.
Almost certainly not yet. Postgres comfortably handles terabytes of data and tens of thousands of transactions per second when the schema, indexes, and connection pooling are right, and it now covers JSON documents and vector search natively. Most teams considering a switch have a fixable query problem, not a database problem. Genuine reasons to add another store exist — but audit the workload first; it's a two-week check versus a six-month migration.
Projects typically fall in the $10K–$60K range depending on scope, integrations, and timeline. I provide a fixed-scope proposal after a 30-minute scoping call.
MVPs often ship in 8–12 weeks. Production systems with AI backends or RAG may run 12–20 weeks. Rescue and audit engagements can start within days.
Yes. I work with founders, CTOs, product teams, and agencies worldwide — US, UK, EU, and APAC time zones with async updates and weekly demos.
Yes. I specialize in React Native + Python (FastAPI) + AI (RAG, agents, OpenAI/Claude) under one senior owner — fewer handoffs, faster shipping.
Book a free 30-minute scoping call on this site, hire through Upwork, or email dhairyasenjaliya@gmail.com with your brief and timeline.
30-minute scoping call · Clear milestones · Senior engineer ownership