An application that worked at 100 users breaks at 1,000. Fixing it requires finding the right bottleneck.
Performance problems have specific causes: the N+1 database query that executes 200 queries per page load, the unindexed column on a table with 2 million rows, the API endpoint that loads a 10MB JSON blob for a 5-row display. We find the bottleneck, fix it, and build the infrastructure to prevent the next one.
Application that worked fine for early users but degrades visibly as user count grows — slow page loads, timeouts, and errors under load
Performance problems at scale are almost always database problems. The UI framework (React, Next.js) rarely causes the slowness users experience — the bottleneck is almost always in how the application queries and retrieves data. The most common patterns:
N+1 queries: The application loads a list of 50 records, then executes one additional query per record to fetch related data — resulting in 51 database queries where 2 would suffice. This pattern is invisible at low data volumes and catastrophic at scale.
Missing indexes: A query that runs in 5ms on a table with 10,000 rows takes 45 seconds on a table with 5 million rows if the query's filter column isn't indexed. This is the most common surprise for applications that grew faster than expected.
Over-fetching: API endpoints that return entire objects when only 3 fields are displayed. A user list endpoint that returns every column on the users table, including the full profile bio, avatar URL, and all metadata, for a display that shows name and email.
No caching: Identical expensive queries executed on every page load for data that changes at most once per hour (navigation menus, category trees, configuration data).
Synchronous operations in request handlers: PDF generation, email sending, image processing, or external API calls executed synchronously in the HTTP request handler — holding the connection open while the slow operation completes.
Application that performs under the user load you actually have, with the monitoring to catch the next bottleneck before users notice it
Query analysis
Postgres query plan analysis to identify missing indexes and inefficient query patterns. N+1 detection and refactoring to joined queries or batch loading. Slow query log review.
Index optimization
Index creation for all filter, sort, and join columns on high-traffic queries. Composite index design for multi-column queries. Index monitoring to prevent over-indexing.
Caching implementation
Redis or in-memory caching for expensive queries on slowly changing data. Cache invalidation strategy that keeps data fresh without over-hitting the database.
Background job migration
Moving slow operations (email, PDF generation, external API calls) out of request handlers into background queues with Convex scheduled functions or similar.
Load testing and monitoring
Post-fix load testing to validate performance at 5–10× current traffic. Sentry performance monitoring to catch regressions before users notice them.
One honest number to start.
Fixed-scope, fixed-price. The number below is the starting point — final scope is built from your brief.
Application that performs under the user load you actually have, with the monitoring to catch the next bottleneck before users notice it
Three steps, every time.
The same repeatable engagement on every project. No surprises, no mystery, no billable ambiguity.
Brief & discovery.
We send you questions, then get on a call. Output: a written scope with every step, feature, and integration listed.
Build & ship.
Fixed schedule, weekly reviews. No scope creep unless you change the scope — and if you do, we reprice it transparently.
Warranty & retainer.
30-day warranty on every launch. Most clients stay on a monthly retainer for ongoing features and maintenance.
Why Fixed-Price Matters Here
Performance projects with open-ended scope run indefinitely. Fixed scope: diagnosis, specific fixes, and validated improvement.
Questions, answered.
Postgres query logs and application APM tools (Vercel Analytics, Sentry) provide enough data to diagnose the most common bottlenecks from read access to production metrics. For diagnosis that requires live query analysis, read-only database access via a read replica is sufficient.
The diagnosis phase identifies whether the problem is fixable with database optimization and caching (80% of cases) or requires architectural changes. Architectural changes (e.g., moving to a CQRS pattern for read performance) are a larger scope and priced as a separate engagement.
Performance diagnosis and remediation: from $12k for focused database optimization. Full performance overhaul: from $25k. Fixed-price.
Tell Ryel about your project.
Describe what you’re building and what outcome you need. You’ll have a written, fixed-price scope within the week.