Infrastructure & hosting

We don't just build it. We run it.

Your AI systems run in production around the clock - deployed on infrastructure you own, monitored, cost-capped, and yours to operate or hand to your team.

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Ops console18,420requests today

Deploy

Railway · Vercel · CF

running

Monitor

Health · logs · alerts

queued

Scale

Autoscale · budgets

queued

Complete

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09:41:00health.ok · 12ms

09:40:59autoscale → 4 workers

09:40:58secret.rotated ✓

09:40:57queue.drained · 0 backlog

↳ Built on the stack that ships

Claude CodeAgent SDKn8nRailwayVercelSupabase

[ 000 ]Trusted by operators

[ 01 ]What it is

The capability, defined.

An AI system that only runs on someone's laptop isn't a system. This is the part most firms skip: real deployment, real uptime, real observability. We put your agents and workflows on infrastructure you own, with the monitoring and guardrails to run them like production software - because that's what they are.

Not that · ✓ this

Not a prototype handed off with a 'good luck.' Not a rented agent platform that owns your logic and your data. It's real deployment on standard infrastructure in your accounts - Railway, Vercel, Cloudflare, Modal, or your own cloud - operated like the production software it is.

[ 02 ]The status quo

What this costs you today.

The build works on someone's laptop, and that's exactly where most agencies leave it - which means it isn't a system yet.

An AI system that only runs in a notebook can't be a system - there's no uptime, no recovery, no real users.

It breaks at 2am and nobody knows until morning, because there's no monitoring or alerting.

An autonomous agent with no budget guardrails can quietly run up a wild bill on a runaway loop.

You're trapped inside a rented platform - your logic in their UI, your data through their cloud, the bill on their terms.

[ 03 ]What we build

The anatomy of the system.

Deployment is the part most firms skip, and it's where production reliability actually lives. We put your systems on standard infra in your accounts, with the operational layers that keep them up and keep them affordable.

Containerized deploy

Your agents and workflows ship as containers (or V8 isolates on Cloudflare) to Railway, Vercel, Modal, or your own cloud - reproducible, versioned, and rolled out cleanly, into your accounts under your keys.

Secrets + access control

API keys and credentials live in a real secrets manager with rotation and least-privilege access - never hardcoded, never in a vendor's database you don't control.

Monitoring + traces

Health checks, structured logs, and OpenTelemetry traces on every system - you see what ran, what it decided, and what it cost, and a failure pages a human instead of rotting silently.

Queues + schedules

Durable job queues and cron handle async and recurring work, with sandboxed compute for agent code - so spikes get absorbed and long tasks survive a restart.

Autoscale

Workers scale to real traffic and back down when it's quiet - so a spike doesn't fall over and an idle system doesn't burn money holding capacity it isn't using.

Cost guardrails

Per-run token and spend budgets, rate limits, and alerts - so an autonomous agent on a bad loop hits a ceiling instead of a surprise invoice.

[ 04 ]How it works

Engineered, not prompted.

We deploy and operate on Claude Code, the Claude Agent SDK, n8n, Railway, Vercel, Cloudflare, and Supabase - the same infrastructure we'd run for ourselves.

Deploy

Your agents and workflows ship to real infrastructure - Railway, Vercel, Cloudflare, or your own cloud - containerized, with secrets, queues, and schedules wired up. Into your accounts, under your keys.

Monitor

Health checks, logs, traces, and alerting on every system. You see what ran, what it cost, and why it decided what it did - and if something breaks at 2am, it pages a human instead of rotting silently.

Scale

Autoscaling, rate limits, and budget guardrails tuned to your real traffic - so the system handles a spike without falling over and an autonomous agent never runs up a surprise bill.

How we engineer it

Your infra or ours

We deploy to your cloud accounts so you own everything, or run it on ours and hand it over cleanly later. Either way, no proprietary platform you can't leave.

Built to stay up

Health checks, retries, fallbacks, and alerting so an agent failing at 2am pages someone instead of silently breaking your business.

Observable by default

Logs, traces, and dashboards on every system - you can see what ran, what it cost, and why it did what it did.

Cost under control

We set the guardrails that keep an autonomous system from running up a surprise bill - rate limits, budgets, and the runtime tuned to your usage.

[ 05 ]Example builds

What this looks like in the wild.

Agents in production

Your agents deployed with sandboxed compute, durable queues, and schedules - running reliably around the clock with health checks and alerting, not in a notebook.

Self-hosted + private

Sensitive workloads run entirely on your own infrastructure, so your data never leaves your perimeter and nothing touches a public model.

Migration off rented platforms

Stuck inside a no-code or proprietary agent platform? We rebuild it on standard infrastructure you actually own - so there's nothing left to be locked into.

Edge + global deploy

Latency-sensitive workloads pushed to Cloudflare's edge with sub-5ms isolate cold starts - close to your users, on infra you don't have to babysit.

[ 06 ]By the numbers

The reliability that ships.

< 5ms

Cold start for a Cloudflare Workers V8 isolate - roughly 100x faster than a traditional container - so edge-deployed agent code spins up instantly instead of stalling on first request.

~15%

Share of GenAI deployments that instrument observability today (Gartner) - the operational gap that separates a system you can run from one that breaks blind. We close it.

Your accounts

Where everything runs - Railway, Vercel, Cloudflare, Modal, or your own cloud - so there's no proprietary platform to escape and you already hold the keys.

↳ Industry benchmarks and engineering standards, not Anfloy client metrics - we report your real numbers once you're live.

[ 07 ]The stack

Named tools, and why.

The model is fungible - the system is the moat. Here's what we build it on, and the reason each earns its place.

Railway

Container hosting in your account with health checks, queues, and logs out of the box - the fast path to production for agents and workflows.

Vercel

Push-to-deploy hosting and edge/serverless functions for AI front ends and APIs, with previews on every change - in your account, not ours.

Cloudflare Workers + Sandboxes

V8 isolates for sub-5ms edge execution and GA Sandboxes for persistent, isolated agent environments - cheap, fast, and globally distributed.

Modal

Serverless GPU and Python-first sandboxed compute with gVisor isolation - elastic capacity for model and agent workloads with no reserved spend.

Secrets manager (Doppler / cloud KMS)

Centralized, rotated, least-privilege credentials - so keys live in a vault you control, never hardcoded or trapped in a vendor UI.

OpenTelemetry + Grafana

Vendor-neutral traces, metrics, and dashboards on every system - full cost and behavior visibility you can swap tools under without re-instrumenting.

Durable queues (Inngest / Temporal)

At-least-once delivery, retries, and state checkpointing - so async and long-running agent work survives crashes and resumes where it left off.

[ 08 ]The architectural difference

Why not just a rented agent platform?

Rented agent platforms get you a demo fast, then own the result. Your logic lives in their UI, your data flows through their cloud, and the bill scales with usage on terms you don't set. We deploy the same systems on standard infrastructure in your accounts - so when you want to leave, there's nothing to leave. You already hold the keys.

· Dimension

· Rented platform

· Anfloy custom

Where it runs

Their cloud, their UI, their terms.

Your accounts - Railway, Vercel, Cloudflare, or your cloud.

Your data

Flows through a vendor you don't control.

Stays in your perimeter; self-hosted when it's sensitive.

Observability

Whatever dashboard they choose to show you.

Full logs, traces, and cost visibility on every run.

Cost control

A usage bill on their pricing, their changes.

Your own rate limits and budgets, tuned to your traffic.

Lock-in

Leaving means rebuilding from scratch.

Standard infra - nothing proprietary to escape.

The keys

They hold them.

You hold them. Operate it, or hand it to your team.

Where it runs

Rented platformTheir cloud, their UI, their terms.

Anfloy customYour accounts - Railway, Vercel, Cloudflare, or your cloud.

Your data

Rented platformFlows through a vendor you don't control.

Anfloy customStays in your perimeter; self-hosted when it's sensitive.

Observability

Rented platformWhatever dashboard they choose to show you.

Anfloy customFull logs, traces, and cost visibility on every run.

Cost control

Rented platformA usage bill on their pricing, their changes.

Anfloy customYour own rate limits and budgets, tuned to your traffic.

Lock-in

Rented platformLeaving means rebuilding from scratch.

Anfloy customStandard infra - nothing proprietary to escape.

The keys

Rented platformThey hold them.

Anfloy customYou hold them. Operate it, or hand it to your team.

[ 09 ]Who it's for

The honest fit check.

Build this if

Teams that have an AI system - built by us or already in hand - and need it deployed, monitored, and operated like production software, on infrastructure they own and can take over anytime.

Skip it if

If you have a mature platform team and standardized internal infra, you likely just need the code and the runbook - we'll hand those over and step back. And if it's a one-off script run by hand a few times, full production hosting is overkill we won't sell you.

[ 10 ]Questions

The honest answers.

Q01

Why does hosting matter - can't we just run it ourselves?

You can, and we set you up to. The point is that a real AI system needs deployment, secrets management, monitoring, autoscaling, and cost controls to run reliably - and that's engineering, not a checkbox. We do it so the system is production-grade on infrastructure you own, then you can operate it yourself, have us run it, or hand it to your team. The choice stays yours because nothing is locked to us.

Q02

Do you lock us into your platform?

No - the exact opposite is the point. Everything runs on standard infrastructure - Railway, Vercel, Cloudflare, Modal, or your own cloud - in your accounts, under your keys. There's no Anfloy platform to be trapped in and nothing proprietary to escape. When you want to leave, there's nothing to leave: you already hold the keys and the code is already in your repo. That's the whole differentiator from a rented agent platform.

Q03

What happens when something breaks at 2am?

It pages a human instead of rotting silently - that's the entire reason this layer exists. We wire health checks, retries, and alerting into every system, with dead-letter queues for work that keeps failing and traces that show exactly what broke and why. We can operate it on call for you, or set your team up with the dashboards and runbooks to respond. Either way the failure is loud and diagnosable, not a quiet outage you discover from a customer.

Q04

Can you keep our data fully private?

Yes. For sensitive or regulated workloads we self-host the entire system on your own infrastructure, so data never leaves your perimeter and nothing trains a public model. Secrets live in your vault, traffic stays in your network, and access is least-privilege. We'll architect to your compliance requirements - and tell you honestly where a managed service is fine versus where self-hosting is worth the operational cost.

Q05

How do you stop an autonomous agent from running up a huge bill?

Cost guardrails are part of the deploy, not an afterthought. We set per-run token and spend budgets, cap loop iterations, add rate limits, and alert on spend before it becomes an invoice - so a runaway loop hits a ceiling instead of your credit card. You get cost attribution per run through the traces, so you can see what each system actually costs and tune it. Autoscaling also means you're not paying for idle capacity between spikes.

Q06

How long does it take to get our system into production?

Deploying an existing build to real infrastructure with monitoring and guardrails is typically days, not weeks - the longer pole is hardening, load-testing, and tuning autoscale and budgets to your real traffic. We ship a production deploy first, then layer in the observability and cost controls, so you're live quickly and getting steadily more robust rather than waiting for a perfect setup before anything runs.

[ 11 ]Keep going up the ladder

RunMaintenance & EvolutionSystems that get sharper every month, not stale.AutomateWorkflow AutomationDeterministic workflows with LLM steps - wired into your stack.BuildAutonomous AgentsAutonomous agents that decide and act, not just answer.

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