It works on your laptop. That was never the hard part.
Taking an ML or LLM prototype to production: a real deployed API, monitored, cost-controlled and load-tested before real users find the gaps.
Three signs a prototype isn't ready for users
- The notebook works, but nobody has wrapped it in an API a frontend can actually call.
- Nobody knows what it costs per request until the first real invoice arrives.
- It has never been tested with more than one request at a time.
How a prototype becomes a production service
Wrap it in a real API
A documented endpoint with proper request and error handling, not a script that happens to print the right answer.
Add monitoring before launch, not after an incident
Latency, error rate and cost visible from day one, so a problem is caught by a dashboard instead of a user complaint.
Model and estimate cost per request
Inference, hosting and any per-call third-party cost projected against expected volume before it ships, not discovered on the first bill.
Choose hosting for the actual workload
GPU inference, batch jobs and real-time endpoints have different hosting needs — the choice is made for the workload, not defaulted to whatever is familiar.
Load-test before real users arrive
Concurrent requests are tested deliberately, so the first spike in traffic isn't also the first time the system has seen one.
Two systems shipped, deployed and operating
Both case studies on this site are deployed, not demoed: a real-time voice pipeline running on Fly.io with Pipecat Cloud, and a multi-agent LLM system with cost tracked per run. The write-ups on measuring latency honestly and on WebRTC network paths cover the deployment decisions in detail.
Concrete deliverables
A deployed, documented API
Not a script — a real endpoint your team's frontend or other services can call.
Monitoring and cost dashboards
Latency, error rate and per-request cost visible without asking someone to check logs.
A load-tested concurrency profile
Verified behaviour under multiple simultaneous requests, documented before launch.
Hosting matched to the workload
A deployment choice justified by the actual traffic pattern, not a default.
The two questions every team asks
"Our data scientist already built this — why do we need an engineer?"
A model that works in a notebook and a service that survives production traffic are different disciplines. This work sits alongside a data science team, not in competition with it — turning what they've validated into something deployed, monitored and load-tested.
"How do we know what this will cost to run?"
Cost per request is modelled against expected volume before launch, using real provider pricing, not a rough guess. The cost estimator on the demos page shows the same kind of modelling used on a real project.
A deployment build starts at $6,000
The Build tier covers taking a prototype to a deployed, monitored production service across 4–8 weeks. Smaller deployment or audit work fits the Sprint tier or hourly advisory. Full tiers and payment terms are on the pricing page.