It worked in the demo.
It buckles under real users.
Built for a screenshot, not for scale. The first real traffic spike surfaces N+1 queries, missing indexes, no caching, and a data model that was never meant to hold a business. We fix the fundamentals and, if the platform itself is the ceiling, migrate you onto a stack that actually holds.
The demo never had to survive real usage.
- Pages that loaded instantly with 10 users now take seconds to load with a few hundred.
- Database queries are timing out or the database bill jumped without a clear reason why.
- You're hitting hard row, request, or execution limits on the no-code platform you built on.
- The app has crashed or gone unresponsive during a launch, a press mention, or a marketing push.
- You have no caching layer and no idea what your app actually does under concurrent load.
Fix the data model first. Migrate only if you actually have to.
Performance & data model audit
Identify N+1 queries, missing indexes, and unbounded queries that work fine at low volume and fail as usage grows.
Infrastructure assessment
Determine whether the current platform, including no-code tools, has a hard ceiling or can be optimized in place.
Migration plan, if needed
If the platform is the actual ceiling, plan a migration onto owned infrastructure with minimal customer-facing downtime.
Indexing, caching & queries
Implement the specific fixes: proper indexes, a caching layer, and rewritten queries that scale with your data.
Load testing
Test against your actual traffic profile to confirm the app holds under real load, not a synthetic benchmark.
Observability
Logging, error tracking, and uptime alerts so failures are visible before your users complain about them.
A stack that holds, with proof it holds.
- An optimized data model with real indexes. Queries that scale with your data instead of degrading with it.
- A caching layer. Where it actually matters for your traffic pattern, not caching for its own sake.
- Migration off the platform ceiling, if needed. Onto infrastructure you own outright.
- Load-tested results against your real traffic. A real number for how much scale you have, not a guess.
- Observability and alerting. So the next problem is visible before your users find it first.
Optimization first. Migration only if the data says so.
Final scope is set after the diagnostic identifies the real ceiling.
Optimize the data model, add indexing and caching, and get the existing stack production-ready.
Migrate off a no-code platform ceiling onto owned infrastructure, with us as the ongoing engineering team.
Talk to usSee the full pricing breakdown for every service.
If any of this sounds like you.
Composite personas, written to describe who this is for. Not real clients, and not real messages.
"We got traction faster than the app can handle."
Growth is the good problem. We make sure the infrastructure can actually catch up to the traction you worked for.
"It went viral and then it fell over."
You built fast to test an idea. Now the idea is working and the app needs to actually survive its own success.
Exactly what is in scope, and exactly what is not.
The same spec block sits on every service page, so you can compare them line for line. What is excluded matters as much as what is included, because that is where scope disputes come from.
- A performance and data model audit driven by real slow-query logs and real traffic, not by guessing which query looks worst.
- Indexes added against the queries you actually run, and N+1 patterns and unbounded selects removed.
- A connection pooler in front of the database wherever serverless or edge functions are involved, because serverless concurrency has no ceiling and a Postgres connection pool does.
- A real caching layer on the expensive reads specifically, with a deliberate invalidation strategy rather than a short TTL and hope.
- Slow and third-party-dependent work moved off the request path into a queue with retries and dead-letter handling.
- A migration off a platform ceiling where one is genuinely the constraint, planned and executed rather than attempted in a panic.
- Load testing against a realistic concurrency target you name, with the results written down.
- Observability that survives us leaving: structured logging, tracing and alerting tied to user-facing impact.
- Scaling work you do not need yet. We will not sell you sharding, Kubernetes or a microservices split at a volume where a single well-indexed Postgres primary handles it comfortably.
- New feature development. This is throughput and reliability work, quoted as such.
- A full security audit, which is a separate engagement.
- A cost-to-serve audit. Making something faster and making it cheaper per user overlap, but they are different engagements with different evidence. The cost work has its own page.
- Ongoing capacity management after handover, unless you move to a retainer.
- Negotiating your infrastructure contracts on your behalf.
An optimized data model with the indexes it needed, a caching layer with a documented invalidation strategy, a completed migration where one was warranted, written load-test results against a named target, and observability and alerting you can keep running without us.
Set per engagement and stated as a dated milestone schedule in the proposal. Optimisation on an existing stack is materially faster than a platform migration, and we separate the two in the plan so you can approve them independently.
Senior engineers only, with production experience that predates the current generation of AI tooling. The engineer who reads your query logs is the engineer who changes the schema. No junior pool, no offshore handoff, no account manager in between.
- Whether a platform migration is genuinely required, which is the single largest variable in this service.
- Data volume and how much of it has to move without downtime.
- The scale target you are actually building for. A thousand users and a hundred thousand are different jobs.
- How much instrumentation already exists. A system with no query logging or APM has to be measured before it can be fixed.
- Data sensitivity, which sets the consequence tier the work is priced in.
Tell us where it hurts and roughly how many users it is hurting at. We read the real slow-query data and infrastructure before quoting, then propose a fixed scope split into optimisation and, only if warranted, migration. You approve each part separately.
What founders ask before scaling.
Do we need to leave our no-code platform entirely?
Not always. Some no-code and low-code platforms can be optimized in place with better data modeling, indexing, and caching. Others have a hard ceiling that genuinely requires migrating to owned infrastructure. We tell you which situation you're in before recommending a migration.
How much traffic can you get us to?
It depends on the starting architecture and your actual usage pattern, which is exactly what the diagnostic determines. We load-test against your real traffic profile, not a generic benchmark, so the number we give you is one you can rely on.
Will there be downtime during a migration?
We plan migrations to minimize or eliminate customer-facing downtime, using parallel infrastructure and a cutover window rather than taking the app offline to move it.
How do you decide between optimizing and rebuilding?
The diagnostic looks at your data model, query patterns, and platform limits. If the ceiling is structural, like a no-code platform's hard request or row limits, we recommend migration. If it's fixable with indexing, caching, and query optimization, we say so and charge accordingly.
What stack do you migrate to?
Whatever fits your product and team, typically a standard Postgres-backed backend with a modern framework. We pick the boring, well-supported option over anything trendy, because you have to run it after we leave.
Send us the repo and your traffic numbers.
A senior engineer reads your actual code and gives you a straight assessment of what it will take to scale. No sales engineer, no junior.