No server to patch
at 3 a.m. for a traffic spike
A serverless backend runs your code as functions that spin up on demand and scale down to nothing when there is no traffic, so a sudden spike does not page anyone and a quiet month does not cost you for an idle server. We use it for workloads that fit its shape: event-driven, bursty, or genuinely low-traffic, and say so plainly when a regular server is actually the better fit.
What it is
A serverless backend runs application code as individual functions triggered by events, an HTTP request, a scheduled timer, a message on a queue, a file uploaded to storage, rather than as a continuously running server process. The cloud provider handles scaling automatically: a quiet period runs zero instances and costs nothing, a traffic spike spins up as many instances as the load needs without anyone provisioning capacity in advance.
When you need it (and when you do not)
It earns its cost for workloads that are genuinely event-driven or irregular: webhooks from a payment provider, scheduled jobs that run a few times a day, an API that gets bursty traffic tied to a marketing campaign rather than a steady baseline. Backend infrastructure behind a digital goods operation processing over 25,000 marketplace listings used this shape well, since listing updates arrive in bursts tied to external marketplace events, not a constant stream.
It is the wrong tool for steady, high-volume, latency-sensitive traffic, where a well-sized traditional server or container setup is usually cheaper per request and avoids cold start latency entirely. It is also a poor fit for long-running processes, a function that needs to hold a connection open for minutes does not fit the short-lived execution model most serverless platforms are built around. We model your actual traffic pattern before recommending serverless over a regular server, since the wrong call here costs real money either way.
How we build it
We start with an honest workload assessment: which parts of your system are actually event-driven and bursty, and which are steady enough that a traditional server is the cheaper, simpler choice. Most real backends end up as a mix, serverless for webhooks, scheduled jobs and spiky endpoints, a regular server or container for anything with a constant baseline load, and we are explicit about which pieces go where rather than forcing everything into one model.
Each function is built around a single, discrete trigger, kept small and testable, since a sprawling function trying to handle many responsibilities loses most of the benefit of the serverless model. Cold starts, the latency penalty a function pays the first time it runs after being idle, get mitigated specifically where a user is waiting on the response, through provisioned concurrency or a keep-warm schedule, and left alone where nothing time-sensitive depends on the first response being instant.
Monitoring across a serverless system needs real attention, since a backend built from dozens of small functions produces logs scattered across just as many places; we set up structured, centralized logging and tracing so debugging a request across functions does not mean checking a dozen separate dashboards. Cost modeling is part of the deliverable, not an afterthought, since serverless billing scales with usage in ways that can surprise a team used to a fixed monthly server bill.
What to watch
Vendor lock-in is real with serverless platforms: provider-specific trigger formats and configuration syntax do not translate cleanly between AWS Lambda, Cloudflare Workers and others, so a migration later is a real rewrite, not a config change. We build with infrastructure-as-code so at least the deployment and configuration is documented and portable in principle, even if the function code itself is provider-specific. Cost can also surprise teams that scale past their estimate; we set budget alerts and model the cost curve against realistic traffic growth, not just current volume. And a system entirely built from small functions needs more deliberate observability than a single server’s logs, which we build in from day one rather than after the first hard-to-debug incident.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Focused functions | from $3,000 | Webhooks, scheduled jobs, or a handful of API endpoints | 3 to 4 weeks |
| Full serverless backend | from $8,000 | Multiple services, monitoring, CI/CD, cost modeling | 5 to 8 weeks |
Running cost scales with actual usage, typically $10 to $200 a month for moderate traffic, with a budget alert set before launch.
Related
This pairs with API-first backend with OpenAPI for the contract layer in front of the functions, and with monolith to modular refactor when splitting an existing backend into independent pieces. See the development service page for our full build process. For real examples, see digital goods marketplace automation and ProBay’s own agent-run infrastructure.
Not sure if your traffic pattern actually fits serverless? Get in touch and we will model it against your real numbers before recommending anything.
FAQ
How much does a serverless backend cost to build?
From $3,000 for a focused set of functions handling webhooks, scheduled jobs or API endpoints, 3 to 6 weeks. A full backend built serverless-first across many services runs $6,000 to $15,000 depending on the number of distinct workloads.
Is serverless actually cheaper?
For bursty or low, irregular traffic, often yes, since you pay per invocation instead of for an always-on server. For steady, high-volume traffic, a traditional server or container setup is frequently cheaper per request, because serverless pricing includes a premium for the elasticity you are not using if the load is constant. We model this with your actual traffic pattern before recommending either.
What about cold starts?
A function that has not run recently takes longer to respond the first time, which matters for user-facing APIs with tight latency needs and matters much less for background jobs or webhooks nobody is waiting on in real time. We mitigate it with provisioned concurrency or keep-warm strategies only where the latency actually affects a user, not everywhere by default.
Which provider do you use?
AWS Lambda most often, since it has the most mature ecosystem, alongside Cloudflare Workers for anything latency-sensitive at the edge. We pick based on your existing infrastructure and the specific workload, not a fixed default.
Who owns the infrastructure?
You. Functions deploy into your own cloud account under infrastructure-as-code we hand over, so your team can read, modify and redeploy without depending on us to make a change.