Cloud & Operations
Serverless Development Services
Serverless is excellent for spiky, event-driven, low-baseline work. You pay nothing when it is idle. It is a poor default for steady high-traffic services. We build it where it fits and tell you when it does not.
What Serverless Development means in practice
Who it’s for: Teams with event-driven, spiky or low-baseline workloads who want to run no servers and pay nothing at idle, and who want a senior engineer to tell them honestly when serverless is the right architecture and when a container would be cheaper and simpler.
Serverless is a way of running software where you never provision, patch or scale a server yourself. You write a function, hand it to the cloud, and the provider runs it on demand (one copy or ten thousand), and bills you for the milliseconds it actually executed. When nothing is happening, you pay nothing, because there is nothing running. Around those functions sits a set of managed services that follow the same model: an API gateway that terminates requests, a queue that buffers work, an event bus that routes messages between components, and a database that scales its own storage and throughput without you sizing an instance. Composed well, this is a system with no machines to keep alive, no capacity to guess at, and no idle cost, and for the right workload that is a genuinely different economics and operational burden than running containers or virtual machines.
We build these systems across the major providers on their native primitives. AWS Lambda with API Gateway, DynamoDB, SQS and EventBridge; Azure Functions with the same supporting cast; Google Cloud Functions and Cloud Run where a container that still scales to zero is the better fit. The work is less about writing functions, which is the easy part, and more about designing the event flow between them: what triggers what, what happens when a step fails halfway, how a message that cannot be processed is caught rather than lost, and how you observe a request that has hopped across five managed services owned by nobody on your team. That distributed, event-driven design is where serverless is won or lost, and it is where senior experience earns its place.
We are also going to be blunt about something the marketing around serverless works hard to bury: it is not a default for everything, and choosing it wrongly is expensive. Serverless shines for event-driven work, for traffic that is spiky or unpredictable, and for services with a low baseline where paying nothing at idle beats paying for a machine that mostly sits waiting. It is a poor fit for a steady, high-traffic service running flat out around the clock: at sustained high volume the pay-per-request model can cost several times what an equivalent container would, and you inherit cold starts, harder local development and a deep dependency on one cloud’s services in the bargain. This service exists to build serverless where it genuinely wins and to say so plainly when a boring container would serve you better. If you want the broader picture across all of your infrastructure, that is our Cloud Engineering service; this one is specifically about the serverless, event-driven architecture within it.
What you get
- An honest architecture decision up front. A written case for whether this workload actually belongs on serverless, containers, or a mix, with the cost and latency trade-offs shown rather than assumed
- Functions kept small and single-purpose, each with its own least-privilege role granting access to exactly the resources it touches and nothing else, because a shared, over-permissioned role is the most common serverless security failure
- Event-driven flow designed deliberately (the triggers, queues and event bus wiring), with dead-letter queues, retries and idempotency so a failed message is caught and reprocessed, never silently dropped
- Managed data and messaging chosen and modelled for the access patterns. DynamoDB single-table design where it fits, a serverless relational database where it does not, rather than defaulting to whichever store is fashionable
- A cold-start strategy sized to the latency your users will actually tolerate: right-sized memory, provisioned or minimum-instance concurrency where a user is waiting, and a clear statement of where cold starts are simply acceptable
- Cost modelled against realistic traffic before we build, and monitored per-function after, with the point named at which growing volume would make containers the cheaper choice
- Local development, testing and observability that make a distributed serverless system debuggable, structured logs, distributed tracing across the managed services, and a way to run and test functions without deploying to reproduce every change
What Serverless Development does for you
Idle costs nothing, and scale is automatic
The defining advantage of serverless is that when no work is happening there is nothing running and nothing to pay for, and when a burst arrives the provider spins up as many concurrent executions as it takes without you planning capacity. For event-driven, spiky or low-baseline workloads this is a genuinely better economics than a server sized for the peak and idle the rest of the time. We design to capture that advantage where it exists, and, just as importantly, we tell you when your traffic is steady enough that the advantage disappears and a container would win.
Almost nothing to operate
There are no servers to provision, patch, secure at the OS level, or scale. The provider owns the machines, the runtime and the availability of the platform, which removes a whole category of undifferentiated operational work (the patching, the capacity planning, the auto-scaling groups), that a container or VM estate demands. That is real time your team gets back. The trade is that you give up control and take on a deep dependency on the provider’s services, which is a cost we account for honestly rather than pretend away.
Composed from managed building blocks
A serverless system is assembled from managed services that each scale and heal themselves (an API gateway, a queue, an event bus, a serverless database), so you build behaviour by wiring proven components together rather than operating each one. Done well this is fast to build and cheap to run for the right workload. The discipline it demands is designing the event flow between those components correctly, because a distributed system composed of managed services fails in distributed ways, and that is exactly the part we treat as the core engineering rather than an afterthought.
Why teams choose us for Serverless Development
- You want an engineer who will tell you the truth about fit, including talking you out of serverless when your workload is steady and high-traffic and a container would be cheaper and simpler, rather than a shop that sells serverless because it is what they build and bills you for the premium.
- You need the distributed, event-driven parts done properly: dead-letter queues, retries, idempotency and tracing designed in, so a failed message is caught and replayable and a slow request can actually be diagnosed across five managed services, instead of discovered as a silent gap by a customer.
- You care about the real cost trajectory, not the launch-day bill. You want the per-request economics modelled against your actual traffic, the point named at which growth flips serverless from cheaper to more expensive than containers, and a design that can move that boundary if it comes.
- You want least-privilege security taken seriously in an architecture where it is easy to get lazy: one tightly-scoped role per function, secrets in a managed store, and the larger managed-service attack surface understood and configured rather than left on defaults.
What Serverless Development includes
The concrete pieces of work this covers, scoped to what your problem actually needs.
Functions-as-a-service across the major clouds
We build on AWS Lambda, Azure Functions and Google Cloud Functions, and reach for Cloud Run or its equivalents when a containerised workload that still scales to zero is the better fit than a raw function. We size memory deliberately, because on most platforms memory also buys CPU, and the fastest configuration is often not the cheapest, so it is a decision to measure rather than default. Functions are kept small and single-purpose so each has a narrow blast radius, a tight permission set, and a cold start you can reason about, rather than a monolith crammed into a function where none of those things are true.
Event-driven architecture and orchestration
The heart of the work is deciding what triggers what and how components talk. We connect functions through queues (SQS and equivalents), an event bus (EventBridge and equivalents) and, where a multi-step workflow needs explicit coordination, a managed state machine such as Step Functions: choosing between choreography, where components react to events independently, and orchestration, where a coordinator drives the steps, on the shape of the process rather than by habit. The aim is loosely-coupled components that fail independently and recover cleanly, not a brittle synchronous chain where one slow step takes the whole request down.
API Gateway and serverless HTTP
For request-driven serverless we put an API gateway in front of the functions to terminate HTTP, handle authentication and authorisation, apply throttling and rate limits, and shape requests before they reach your code. We design this deliberately, where authorisation lives, how throttling protects downstream managed services from a stampede, how the gateway’s own latency and cost factor in, because the gateway is both the front door and one more managed service in the path whose behaviour and pricing you need to understand rather than accept on defaults.
Managed serverless data and messaging
We model data around access patterns rather than defaulting to a store. DynamoDB and other serverless key-value or document databases are excellent when the access patterns are known and suit them, and single-table design done properly is fast and cheap, but they punish ad-hoc querying and relational shapes, so where the data is genuinely relational we use a serverless relational database (such as Aurora Serverless or its equivalents) instead of forcing a fit. For messaging we choose between queues for buffered point-to-point work and an event bus for fan-out and routing, and we design the throughput, partitioning and cost of each rather than discovering the limits under load.
Cold starts, concurrency and performance
A cold start. The extra latency the first time a function runs on a fresh execution environment while the runtime initialises: is a real cost, and we treat it as an engineering decision rather than something to hope users do not notice. For asynchronous, event-driven work it usually does not matter. For a user waiting on a response it can, and we address it with right-sized memory, lean deployment packages and lighter runtimes, and provisioned or minimum-instance concurrency where a warm function genuinely earns its keep. We are also clear about where a cold start is simply acceptable, because paying to keep functions warm around the clock can quietly erase the cost advantage you came to serverless for.
Observability, local development and testing
A distributed system stitched from managed services is hard to debug precisely because no single machine has the whole story, so we ship the tooling that makes it tractable: structured logs, per-function metrics, and distributed tracing that follows one request as it hops across the gateway, the functions, the queues and the database. We also invest in the developer experience the model makes harder: running and testing functions locally, emulating the managed services where it is worth it, and testing the event wiring, so an engineer is not deploying to the cloud to find out whether a change works. This is the part of serverless that is genuinely harder than servers, and pretending otherwise is how projects stall.
Where it fits
Event and stream processing
A file lands in storage and needs transforming, a stream of events needs enriching and routing, a message arrives on a queue and triggers downstream work. This is serverless at its best: the work is inherently event-driven, the volume is often bursty, and paying only for actual execution beats keeping a worker fleet alive to wait for events that arrive unevenly. We build the triggers, the processing functions and the failure handling (dead-letter queues and idempotent handlers), so a poison message is caught and replayable rather than lost or endlessly retried.
Spiky or unpredictable APIs and backends
A backend whose traffic is genuinely irregular. A launch you cannot size for, a service with occasional bursts against long quiet stretches, an internal tool used hard for an hour a day. Here scaling from zero to thousands of concurrent executions without provisioning for a peak that mostly is not there is worth real money and real operational calm. We build the API gateway, the functions and the managed data behind them, and we are explicit that if the traffic later flattens into a steady high-volume stream, the economics change and we should revisit whether part of it belongs on a container.
Scheduled jobs, automation and glue
The scheduled and event-driven plumbing that keeps systems in sync. A nightly export, a periodic reconciliation, a webhook handler, a piece of automation that reacts to a change in one system and updates another. This work is a poor fit for a permanently-running server: it is idle almost all the time, so a function that wakes on a schedule or an event and costs nothing in between is exactly right. We build these with proper retries, idempotency and visibility, because glue code that fails silently is how a sync quietly drifts until a customer notices for you.
A hybrid where only the right parts are serverless
The most honest architecture is often not all-serverless. A steady, latency-sensitive core runs on a container where the economics and the cold-start-free responsiveness suit it, while the bursty, asynchronous, event-driven work around it (the background processing, the spiky endpoints, the scheduled jobs), runs on functions that scale to zero. We design that boundary deliberately, on cost and latency evidence, so each workload runs where it is actually cheapest and simplest, rather than forcing everything into one model to keep the architecture diagram tidy.
How we approach Serverless Development
We start with the workload’s shape, not with a decision to use serverless. The questions that matter are how the traffic behaves: is it steady or spiky, does it have a real idle baseline, is it triggered by events or by a constant stream of users, and how sensitive it is to the tens or hundreds of milliseconds a cold start can add. Those answers decide whether serverless is the right tool at all, and for which parts. It is common for the honest recommendation to be a mix: functions for the asynchronous, event-driven, bursty work where scaling to zero is worth real money, and a container for the steady, latency-sensitive core that would be more expensive and more awkward as a fleet of functions. We would rather draw that line correctly than sell you an all-serverless architecture that costs more and debugs worse.
Once the fit is settled, the real work is the event flow. We design each function to do one thing, connect them through queues and an event bus rather than long synchronous chains that fail as a unit, and treat failure as the normal case: every asynchronous step gets a retry policy and a dead-letter queue so a message that cannot be processed lands somewhere you can see it and replay it, rather than vanishing. Because the same engineers operate what they build, the parts that only hurt in production. The cold-start budget, the per-function permissions, the tracing that lets you follow one request across five services, are designed in from the first function, not bolted on after the first incident that nobody could diagnose.
How the engagement runs
We open with the fit decision, because it is the one that everything else depends on and the one a serverless-only shop skips. We map the workload’s traffic shape, its latency tolerance and its baseline, model the per-request cost against realistic volume, and put in writing which parts belong on functions, which belong on containers, and where the honest answer is a mix. If serverless is the wrong tool for what you are building, this is where we say so: before you have paid to build it. Assuming it is the right tool, we settle the event architecture on paper first: the triggers, the queues and event bus, the data model against its access patterns, and the failure handling, so the shape of the distributed system is agreed before any of it is wired together.
From there we build in thin slices that run in the real cloud, not in an emulator that hides the parts that only bite in production. Each slice is a real flow end to end: a trigger, a function with its own least-privilege role, the managed services it touches, its retries and dead-letter handling, and the tracing that lets you follow it, so you are steering off working software and real cost figures rather than a plan. Because the engineers who design it operate it, the cold-start budget, the per-function permissions and the observability are present from the first slice, and the per-function cost is watched as the system grows so the point where volume might flip the economics is seen coming, not discovered on an invoice.
How we architect it
We design for loose coupling and independent failure, because that is what the serverless model is good at and what makes it operable. Functions are small and single-purpose, connected through queues and an event bus rather than long synchronous call chains, so a slow or failing component degrades one path instead of collapsing the whole request. We choose choreography (components reacting to events independently), for flows that suit it, and explicit orchestration through a managed state machine for multi-step workflows that need coordination and visibility, rather than applying one pattern everywhere. Every asynchronous step is built on the assumption that it will sometimes fail: retries with backoff, dead-letter queues so a message that cannot be processed is captured rather than lost, and idempotent handlers so a message delivered or retried more than once (which, in these systems, it will be), has the same effect as one delivered once.
On data, we model around access patterns before choosing a store, because the biggest architectural mistake in serverless data is forcing a relational shape into a key-value store or scattering ad-hoc queries across a database built for known patterns. We use serverless key-value and document stores where the patterns are known and suit them, a serverless relational database where the data is genuinely relational, and we design throughput and partitioning rather than discovering the limits under load. Throughout, we keep the coupling to the provider visible and deliberate: serverless means building deeply on one cloud’s specific services, and that lock-in is real. We do not pretend a system like this is portable, but we isolate business logic from provider-specific glue where it is cheap to, so the parts worth moving later are not tangled into the parts that never will be.
Security
Serverless changes the security picture rather than removing it. You no longer patch an operating system, which genuinely removes one class of risk, but you take on a larger managed-service attack surface: every function, gateway, queue, bus and table is a resource with its own permissions and its own configuration to get right, and the most common failure is laziness with those permissions. The right model is one tightly-scoped role per function, granting access to exactly the resources that function touches and no more, so a compromised or buggy function cannot reach the rest of the system. The lazy alternative (a single broad role shared across every function because it is easier), turns any one weakness into access to everything, and it is precisely the pattern we are called in to unwind. Per-function least privilege is more work up front and it is not negotiable.
Around that we treat every input crossing a boundary as hostile and validate it, keep secrets in a managed secret store rather than in environment variables committed to a repository, and configure the managed services themselves rather than trusting defaults: a queue’s access policy, a table’s encryption, a gateway’s authorisation and throttling are all part of the security posture, not platform details someone else handles. We are also honest that the provider owns the platform’s security and you inherit their model whether you like it or not, and that the sheer number of small, individually-configured resources is itself a risk: it is easy for one table or one bus to be left open. So configuration is declared as code and reviewed, not clicked together, because in a system made of dozens of managed pieces the security is only as good as the least-careful resource in it.
Signs it’s time
- You have event-driven work. A file lands and needs processing, a webhook arrives, a message hits a queue, a scheduled job runs, and you want it to run on demand and cost nothing between events rather than keeping a server alive to wait for them
- Your traffic is spiky or genuinely unpredictable (occasional bursts, seasonal spikes, a launch you cannot size for), and you want automatic scaling from zero to thousands of concurrent executions without provisioning for a peak that mostly is not there
- You have a low-baseline service where a machine sized to handle the busy moments would sit mostly idle and mostly wasted, and paying only for actual execution would be dramatically cheaper
- You want to run as little infrastructure as possible (no servers to patch, no capacity to plan, no scaling to configure), and you are willing to accept the trade-offs of the managed, event-driven model to get there
Our working method
The organising principle is that serverless is a specific tool with a specific sweet spot, not a default, so the first thing we get right is whether it belongs here at all, and we would rather lose the all-serverless version of a project than build you something that costs more and debugs worse than the container you should have had. Where it does fit, we treat the distributed, event-driven design as the real engineering: the failure handling, the idempotency and the tracing are part of writing a flow, not a follow-up, because in a system stitched from managed services owned by nobody on your team, the failure path and the debuggability are not edge cases, they are the job. We measure cost per function and per request against real traffic rather than trusting the launch-day bill, because the serverless economics are excellent at low and spiky volume and can quietly turn against you at steady high volume, and knowing where that line is for your workload is part of building it responsibly.
We build in thin slices in the real cloud, and the engineers who design the system operate it, which is why the cold-start budget, the least-privilege roles and the observability are present from the first function rather than added after the first incident nobody could trace. And because the trade-offs of serverless are real and specific, part of the method is naming them plainly: the cold starts, the lock-in, the harder local development, the cost that can flip at scale. We would rather you choose serverless with those trade-offs in full view (and choose containers for the parts that do not suit it), than sell you an architecture that photographs well and costs you for years.
Technologies we build it with
Chosen per problem, not per fashion. This is the stack we most often reach for on this work.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves, including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Want a straight answer on Serverless Development?
A short call with a senior engineer, before you write a brief. If Serverless Development is the wrong answer for your situation, we will say so and tell you what we think is right.
What changes
Nothing paid at idle
A system that scales to zero when no work is happening, so a spiky or low-baseline workload costs you only the execution it actually uses instead of a server kept alive to wait.
Scales itself, silently
Automatic scaling from zero to thousands of concurrent executions with no capacity planning and no servers to patch. The operational load of running it is close to nothing.
The right tool, honestly chosen
A clear line between the parts that belong on serverless and the parts that belong on containers, drawn on cost and latency evidence, so you are not paying a premium to run a steady workload the wrong way.
Industries we serve
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
How pricing works
- A fit-and-architecture assessment for teams deciding whether a workload belongs on serverless at all. The traffic-shape analysis, the per-request cost model against your real volume, and a written recommendation of what runs on functions, what runs on containers, and where the boundary is, priced as a focused engagement, because getting this decision right is worth far more than the code that follows it.
- A fixed-scope build for a well-defined serverless system. An event-processing pipeline, a spiky API, a set of scheduled and event-driven jobs, quoted once the event flow, the data model and the failure handling are understood, so you are not paying for scope nobody has pinned down.
- A monthly senior engagement for evolving serverless work, where the system grows with the product and you want continuity, ongoing cost monitoring as volume changes, and someone genuinely on call for a distributed system rather than a one-off deliverable handed over and forgotten.
- A focused review or rescue for an existing serverless estate. A per-function permissions and security audit, a cost investigation for a bill that has grown past expectation, or a diagnosis of a system whose event flow fails in ways nobody can trace, priced by the assessment.
Typical timeline
- 01
Fit and architecture
One to two weeks analysing the workload’s traffic shape and latency tolerance, modelling per-request cost against realistic volume, and settling which parts belong on serverless and which on containers, captured as a design and a cost model your team can review before anything is built.
- 02
First flow in the cloud
Two to three weeks delivering a real end-to-end flow. A trigger, functions with their own least-privilege roles, the managed data and messaging they touch, dead-letter handling, idempotency and distributed tracing in place, proving the architecture and the real cost figures against genuine traffic.
- 03
Iterative build-out
Flow-by-flow delivery of the rest of the system in short cycles, each slice shippable, with per-function cost watched as the system grows so the point where volume might flip the economics is seen coming rather than discovered on an invoice.
- 04
Hardening and handover
A security review focused on per-function permissions and managed-service configuration, load testing that exercises cold starts and concurrency limits, observability and runbooks in place, and documentation so your team can operate, be on call for, and extend the system without us.
What working with us actually means
We will tell you not to use it
The most valuable thing we bring to a serverless engagement is the willingness to say your workload does not suit it. A steady, high-traffic service is usually cheaper and simpler on containers, and we would rather draw that line for you honestly than sell you the premium version of the wrong architecture. When serverless is right (event-driven, spiky, low-baseline work), we say that just as plainly, and we build it well.
We operate what we build
We run the serverless systems we ship, so the parts that only hurt in production. The dead-letter queues, the idempotency, the cold-start budget, the distributed tracing, the least-privilege roles: go in from the first function rather than after the first incident nobody could diagnose. A distributed system stitched from managed services is genuinely harder to operate than a server, and we build for that reality instead of pretending it away.
Cost modelled, not assumed
Serverless economics are excellent at low and spiky volume and can quietly flip against you at steady high volume. We model the per-request cost against your real traffic before we build, monitor it per function after, and name the point at which growth would make containers cheaper, so the bill holds no surprises and the architecture can move the boundary if it needs to.
Senior engineers on the hard parts
Writing a function is easy; designing the event flow, the failure handling and the least-privilege security across a dozen managed services is not, and it is exactly where inexperience shows up later as a silent data loss, a runaway bill, or a breach through an over-permissioned role. The people building yours have operated systems where those mistakes had consequences, and they design to avoid them.
How to engage us
Three ways to work with us on this, chosen to fit the problem, not our margin.
- Dedicated team A standing team that works only on your product, in your rituals and your tooling. Best when the roadmap outlives the project. Ongoing product development
- Staff augmentation Named senior engineers embedded into your existing team, reporting into your leads. Best when you know what to build and need capacity. Filling a capability gap
- Software outsourcing A defined outcome delivered end-to-end by an accountable team. Best when you want the result owned, not just the hours filled. Outcome-owned delivery
Related services
Part of Cloud Engineering. Other work we do alongside this.
Common questions
How is this different from your Cloud Engineering service?
Cloud Engineering is the broad discipline, architecting, migrating and operating your infrastructure across AWS, Azure or Google Cloud, whether that runs on virtual machines, containers, serverless, or a mix, with the cost, resilience and observability that go with all of it. Serverless Development is specifically the functions-as-a-service and managed-service architecture within that: building event-driven systems on Lambda or its equivalents, API gateways, managed queues and event buses, and serverless databases. If you want your whole cloud estate designed and run, that is Cloud Engineering. If you have an event-driven, spiky or low-baseline workload and want it built well on serverless (or want an honest answer on whether it should be), that is this service. Many engagements involve both, and the fit decision often lands us in a hybrid.
Is serverless always cheaper than running servers?
No, and anyone who tells you it is has not modelled your traffic. Serverless is dramatically cheaper for event-driven, spiky or low-baseline workloads, because you pay nothing when nothing is happening rather than paying for a machine that sits mostly idle. But the pay-per-request model does not have the economies of scale a busy server does, so for a steady, high-traffic service running flat out around the clock, serverless can cost several times what an equivalent container would. The honest answer depends entirely on your traffic shape, which is why we model the per-request cost against your realistic volume before recommending it, and name the point at which growing traffic would flip the decision toward containers.
What are cold starts and will they be a problem for us?
A cold start is the extra latency the first time a function runs on a fresh execution environment, while the provider initialises the runtime and your code before it can handle the request, typically tens to hundreds of milliseconds depending on the language, the package size and the memory configured. Whether it matters depends entirely on the workload. For asynchronous, event-driven work (processing a file, handling a queued message, running a scheduled job), it almost never does, because nobody is waiting. For a user waiting on an HTTP response it can, and we address it with right-sized memory, leaner packages and lighter runtimes, and provisioned or minimum-instance concurrency where a warm function genuinely earns its cost. We are also clear about where a cold start is simply acceptable, because keeping functions warm around the clock can erase the very cost saving you came to serverless for.
Does serverless lock us into one cloud provider?
Yes, and it is one of the real trade-offs we will not pretend away. A serverless system is built deeply on one cloud’s specific services (its function runtime, its event bus, its managed database, its permission model), and those do not map cleanly onto another provider’s, so moving is a genuine rewrite rather than a redeploy. The lock-in is the price of the low operational burden and the pay-per-use economics; you are trading portability for the provider running everything for you. We do not sell false portability, but we do isolate your business logic from the provider-specific glue where it is cheap to, so the parts that would be worth moving later are not needlessly tangled into the parts that never will be. If avoiding lock-in is a hard requirement, that pushes toward containers, and we will say so.
Is serverless harder to develop and debug than normal servers?
In some genuine ways, yes, and this is the trade-off the marketing tends to skip. Running and testing a distributed system of managed services on your own laptop is harder than running a single server locally, because much of the behaviour lives in cloud services that are awkward to emulate faithfully. And debugging a request that has hopped across a gateway, several functions, a queue and a database (none of them a machine you can log into), is harder than reading one server’s logs, because no single place has the whole story. We take this seriously rather than wishing it away: we invest in local development and testing that let engineers work without deploying for every change, and we ship distributed tracing and structured logging that follow one request across every managed service it touches. Pretending this part is easy is exactly how serverless projects stall, so we build for it from the start.
Thinking about Serverless Development?
Tell us the problem in your own words, not in requirements. A senior engineer reads it and comes back with a straight view on whether Serverless Development is the right answer here, or what would be.
- 01A senior engineer reads it. Not a form queue, and not an account manager.
- 02We reply either with questions or with a straight answer that we are not the right fit.
- 03If it looks like a fit, a technical call with the person who would actually run the delivery.
- 04Then scope, effort and risk in writing, before anyone signs anything.