Self-hosting is usually chosen for control, and then lost on economics because the serving layer is left at defaults. Throughput depends on batching, memory layout and how requests arrive. We tune the serving stack to your actual traffic shape and publish the numbers, so the decision to self-host is made against measurements rather than a vendor comparison table.
01 / SELF_HOSTED
For teams who will not call an API. Serving, batching and autoscaling tuned to your traffic shape, with throughput and cost measured rather than promised.
Usually shipped with
Not a bundle to buy. Whichever you start from, the engagement covers what the build actually needs.
02 / Scope
03 / Outcomes
04 / Deliverables
05 / Stack
06 / Why us
Self-hosting is frequently the wrong call once operational cost is counted honestly. The comparison is run before the migration, and we have told clients to stay hosted.
Most self-hosted deployments run at a fraction of their hardware because batching and cache configuration were left at defaults. That is the work, not the model choice.
Requests arriving in bursts and requests arriving steadily need different configurations. Sizing on request volume alone is how capacity is bought and wasted.
Week 1
Current cost, latency and quality per request class. Self-hosting decisions made without this are made on vendor comparison tables.
Week 1-2
Throughput depends on how requests arrive, not on how many. Bursty and steady traffic want different serving configurations.
Week 2-4
Continuous batching, cache configuration and memory layout. This is where self-hosting is usually lost, because the defaults are conservative.
Week 4-6
Measured cost per thousand requests against the hosted alternative, including the operational overhead, reported honestly.
Admission control sits at the front: requests are queued, shed or refused before they reach a GPU, so load past the ceiling is refused rather than silently slowing every other caller. The pool batches work, reuses a KV cache and streams tokens back. Weights come from a registry tracking version and licence — the part teams discover late, usually during procurement. A canary sends a new version to a slice of traffic rather than to everyone, and health checks can roll back one version without a redeploy.
Production-proven
Built by engineers who've already shipped this in production.
Sometimes, and often not once engineering time and idle capacity are counted. We measure your case before recommending it, and the answer is genuinely sometimes no.
It follows from throughput and latency targets against your traffic shape. Sizing before measuring is how teams buy capacity they never use.
Your team, with runbooks. We build the system and write down how to run it; we are not an outsourced operations desk.
Keep the routing layer and it is a configuration change. That is one reason we build routing before migration rather than after.
Routing across models by task, for teams paying frontier prices for routine work.
View serviceFine-tuning and adapters, for behavior a prompt cannot hold.
View serviceCloud changes made safe to apply, for teams postponing the risky ones.
View serviceTell us where you are and what you're trying to ship. We'll come back with a concrete plan, the right engineers, and a path to production, not a generic pitch.