AIaaS
Buying an AI capability as an API moves the hard part rather than removing it. What decides whether it works is integration surface, latency under your real load, what happens when the provider has a bad day, and whether your data is allowed to leave your infrastructure at all.
The costs that surface after integration
The model is rarely the expensive part. The expensive parts are the integration nobody scoped, the absence of a versioning contract so a provider-side model update silently changes behaviour in your product, per-request pricing that was fine in pilot and unpleasant at volume, and the discovery during a security review that user content cannot lawfully be sent to a third party. Each of these is cheap to design for at the start and expensive to retrofit once the capability is embedded in a product.
The three calls that decide the outcome.
Made explicitly, with the trade-off written down, before anything gets built.
Managed API or self-hosted service
Managed APIs give you capability immediately with no infrastructure to run, and they require your data to leave your boundary. Self-hosting removes that question entirely and puts operations back on you. The decision is usually made by regulation rather than preference — and it is worth checking the assumption that self-hosting demands GPUs, because a meaningful share of production models serve well on CPU. The moderation service we built runs as a stateless CPU-optimised container specifically so it can be self-hosted cheaply when user messages cannot leave the customer's infrastructure.
Synchronous or asynchronous contract
A synchronous API is simpler to consume and ties your caller's latency to the slowest component in the chain. Asynchronous submit-and-callback decouples them, survives provider slowdowns without cascading, and costs you a more complex client. Anything with a variable or long tail of processing time — document handling, video, multi-step reasoning — should be asynchronous from the start, because retrofitting it means changing every consumer.
What the versioning contract guarantees
Behaviour changing under a stable endpoint is the failure mode that erodes trust fastest, because it presents as your product regressing for no visible reason. Pinned model versions, a deprecation window, and an evaluation set you can re-run against a new version before adopting it are what make an AI dependency operable. Without them you have a capability you cannot reason about, and no way to tell a provider change from your own bug.
The capabilities we bring on day one.
Each engagement assembles from this menu, sized to your scope, paced to your calendar.
Clean API contract
Versioned endpoints, pinned model versions and a deprecation window, so behaviour does not shift underneath you.
Self-hostable by design
Stateless containers that run without a GPU per node where data cannot leave your infrastructure.
Predictable latency
Async contracts, caching and load-tested budgets rather than a best-case number from a quiet afternoon.
Cost that scales
Routing, caching and right-sized models so unit economics hold at volume, not just in pilot.
Where the data goes, contractually
Before any personal data reaches a third-party model, you need a lawful basis and a signed processor agreement — a provider without a standard data processing agreement ready is a straightforward procurement red flag. Beyond that, the questions worth asking are specific: which region processes the request rather than merely stores the result, whether inputs are retained or used for training and for how long, and what the sub-processor chain looks like. Where the answers do not clear your regulatory position, self-hosting stops being a preference and becomes the design.
From kickoff to live in four phases.
Each phase has named deliverables, named owners and a named gate, and every one respects the systems you already have in place.
Discover
Integration points, real load profile and latency budget, and the regulatory boundary that decides where inference is allowed to run.
Design
API contract and versioning policy, deployment model, failure and fallback behaviour, and the unit-economics model at projected volume.
Build
The service, client libraries, load testing against your real traffic shape, and the evaluation set used to qualify version changes.
Run & hand off
SLA monitoring, cost per request tracking, and a runbook covering provider degradation and version migration.
What a good fit looks like.
Stated up front, so neither of us spends a call finding out this was the wrong conversation.
Engagement shape
A scoped sprint of roughly eight weeks to a production-ready service behind a versioned API, load-tested against your real traffic shape.
From your side
Your expected load profile and latency budget, the consuming application team, and a clear answer on where data is permitted to be processed.
When not to hire us
If a commodity API already solves your problem and your data may lawfully leave your infrastructure, integrate it directly. You do not need us between you and a well-documented endpoint.
AIaaS questions, answered.
Yes, and it is often the deciding requirement rather than a preference. It is worth testing the assumption that self-hosting means GPUs — a meaningful share of production models serve well on CPU. We built the moderation service as a stateless CPU-optimised container precisely so it self-hosts cheaply where user messages cannot leave the customer's environment.
That is a design decision made before launch, not an incident to improvise through. Depending on the use case it means provider failover behind a gateway, a degraded-but-useful fallback path, or queueing with an async contract so the caller is not blocked. What matters is that the behaviour is defined and tested rather than discovered in production.
By routing work to the cheapest model that answers it correctly, caching context that repeats across requests, and keeping prompts to what the request actually needs. We model unit economics at projected volume during design, because pricing that is comfortable in pilot and painful at scale is the most common unpleasant surprise.
Pin model versions, keep a deprecation window, and hold an evaluation set you can re-run against a candidate version before adopting it. Without those you cannot distinguish a provider-side change from a bug in your own code, which is the failure mode that erodes trust in an AI dependency fastest.