Data residency, vendor risk, latency floors, and per-token economics all push AI workloads back into your own VPC or data center. We help you decide when self-hosting is genuinely the right call, then build the serving stack, evaluation harness, and operating practices to run it reliably.
Adoption pillars covered
Discover
Primary focus
Build
Primary focus
Enable
Supporting
Govern
Supporting
Delivery models supported
Project-based
Best fit
Embedded engineering
Available
Workshop-based
Available
Step 01 — decide
This is the question the engagement opens with, and it has a real answer. Some constraints make self-hosting close to mandatory. Others make it an expensive way to solve a problem a managed API already handles. We weigh both sides against your actual workload.
Signals that push work in-house
The stronger these are, the more the operational cost of self-hosting starts to pay for itself.
Data cannot leave your boundary
Regulatory residency, contractual restrictions, or classified material make sending prompts to a third party a non-starter rather than a preference.
Sustained, predictable volume
You run enough inference around the clock to keep GPUs busy. Idle accelerators are the fastest way to make self-hosting uneconomic.
A hard latency floor
Network round trips to a provider blow your budget before the model even starts generating, and co-locating inference is the only fix.
Model stability matters more than frontier capability
You need the same weights to behave the same way for years, without a provider deprecating or silently updating the model underneath you.
Signals that say stay managed
When these dominate, we will tell you so — and the engagement can end at the assessment.
Spiky or still-unknown demand
Usage that swings by an order of magnitude wants elastic capacity you pay for by the token, not a fixed fleet sized for the peak.
You need frontier-level capability
If the task genuinely requires the strongest available model, open weights will cost you quality that no amount of infrastructure work recovers.
No appetite for GPU on-call
Self-hosting adds driver issues, capacity incidents, and upgrade cycles to a rotation that has to be staffed by someone.
A VPC deployment already satisfies compliance
Several providers will run inside your cloud account or under a zero-retention agreement, which often clears the actual requirement.
We are happy for the answer to be no.
The assessment is deliberately cheap relative to a GPU commitment. If the evidence points at managed APIs for your workload, that is the recommendation you get, in writing, along with the specific conditions that would change it later.
Assessment length
2–3 weeks
Deliverable
Written recommendation
Outcomes we will recommend
Both
Step 02 — model it
Managed APIs cost roughly nothing to start and rise with every token. Self-hosting starts at the price of the smallest sensible GPU footprint and barely moves until you outgrow it. Where those two lines cross is the whole economic argument.
Cost against inference volume
The step in the self-hosted line is the point where another node gets added; the flat stretches are capacity you have already paid for.
Managed API
Self-hosted
Illustrative shape, not your numbers. The assessment builds this same curve from your measured volume, concurrency, and target model — including the operational staffing cost that most build-versus-buy comparisons quietly leave out.
Three things move the crossover point
GPU utilization
The single biggest factor. A fleet running at 30% utilization pushes the crossover far to the right, because you are paying for capacity nobody is using.
Low utilization → crossover moves right
Model size and quantization
A quantized mid-size model that fits on fewer accelerators lowers the fixed floor substantially, often without a meaningful quality cost on narrow tasks.
Smaller footprint → crossover moves left
Concurrency profile
Steady concurrency batches efficiently and keeps throughput high. Bursty traffic forces you to size for the peak and idle through the trough.
Steady load → crossover moves left
Step 03 — build it
Downloading open weights takes an afternoon. Everything below is the part that decides whether the thing survives production — and every layer of it is handed over with the runbook your team needs to own it.
Your data boundary
Applications & agents
The products calling inference, plus the retrieval and tool layers feeding them context.
We build
Integration and SDK layer
You operate
Product roadmap
Inference gateway
One entry point for every model call: authentication, quotas, routing, and hybrid fallback to a managed provider when you allow it.
We build
Gateway, policy, and routing
You operate
Quotas and access policy
Serving layer
The inference server itself — continuous batching, KV cache management, tensor parallelism, and the throughput tuning that makes GPUs pay.
We build
Serving config and tuning
You operate
Model rollouts
Model registry & evaluation
Versioned weights with provenance and licensing recorded, and the regression suite that gates every upgrade.
We build
Registry and eval harness
You operate
Release gates
GPU scheduling & capacity
Node pools, accelerator scheduling, autoscaling behaviour, and the headroom policy that keeps p95 latency inside its target.
We build
Scheduling and autoscaling
You operate
Capacity planning
Observability & on-call
Throughput, latency, utilization, and quality tracing, wired into the alerting and incident process your team already uses.
We build
Dashboards, alerts, runbooks
You operate
The rotation
Prompts, retrieved context, and generated output stay inside this line. Nothing crosses it to a third-party model provider, which is usually the reason the project exists.
What we watch in production
Tokens per second
Under real load
p95 latency
Against target
GPU utilization
Fleet-wide
Quality vs baseline
Per release
Where this sits
This is a Discover-and-Build service with a serious operational tail. The assessment decides whether self-hosting is justified at all; the build stands up a real inference platform; and enablement and governance make sure it survives contact with your on-call rotation and your auditors.
Discover
Uncover real value
Before any hardware is bought, we model what self-hosting would actually cost and require — and we will recommend against it when managed APIs are the better answer.
Workload and concurrency profile
TCO model versus managed APIs
Residency and compliance driver map
A written go / no-go recommendation
Build
Take ideas to production
We stand up the full serving path: model selection and benchmarking, GPU capacity, inference server, gateway, autoscaling, and the evaluation harness that proves output quality.
Benchmarked model shortlist for your tasks
Inference serving stack on your infrastructure
Gateway with auth, quotas, and hybrid fallback
Evaluation and regression suites
Enable
AI competency for every team
GPU infrastructure fails differently from the rest of your platform. We train your engineers on the failure modes, upgrade paths, and capacity decisions they will own.
Operating runbooks and upgrade process
On-call and incident drills
Capacity planning guidance
Architecture documentation
Govern
Scale without losing control
Self-hosting is often chosen for control, so we make that control provable: data boundaries, model provenance, access policy, and audit trails.
Data boundary and isolation design
Model provenance and licensing record
Access policy and audit logging
Monitoring and alerting coverage
How we deliver it
Standing up private inference is a defined outcome, so most teams start with a scoped project. What follows depends on whether you want to own GPU operations in-house or keep senior capacity close while the platform matures.
Project-based
A scoped outcome, shipped
A fixed-scope engagement: assessment, model selection, serving stack, evaluation harness, production rollout, and handover.
Engagement shape
Typical length
6–12 weeks
Starts with
Go / no-go assessment
Ends with
Production handover
What it includes
TCO and feasibility assessment before the build commits
Benchmarked model selection against your real tasks
Serving stack, gateway, and autoscaling in your environment
Runbooks, monitoring, and a handover session at the boundary
Embedded engineering
Senior engineers inside your team
Senior AI and platform engineers inside your team, owning inference capacity and model upgrades alongside your own roadmap.
Engagement shape
Typical length
3–12 months
Renewal
Monthly
Priorities
Your roadmap
What it includes
Capacity planning as usage and model sizes change
Model upgrades evaluated and rolled out safely
GPU cost and utilization tuned continuously
Patterns transferred through pairing and code review
Workshop-based
Hands-on teaching on your stack
A hands-on cohort that teaches your platform engineers to operate GPU inference on your own clusters.
Engagement shape
Typical length
1–3 weeks
Format
Cohort blocks
Run on
Your clusters
What it includes
Serving, batching, and GPU scheduling taught hands-on
Quantization and sizing trade-offs with real measurements
Incident drills against realistic inference failures
Upgrade and rollback practice in a safe lane
The assessment can end the engagement
If the numbers say managed APIs win for your workload, we will say so and stop there. A short assessment that prevents an unjustified GPU commitment is a better outcome than a build we could have talked you out of.
What changes
Success is not a running container. It is predictable throughput under real load, measured output quality, a clear upgrade path, and a team that can handle a 3am page without calling us.
Control
Data stays inside your boundary
Inference runs in your VPC or data center with provable isolation, audit trails, and no prompt or output leaving your control.
Performance
Throughput that holds under load
Batching, GPU scheduling, and autoscaling tuned against your real concurrency rather than a single-user benchmark.
Quality
Output measured, not assumed
Evaluation suites compare your self-hosted model against the baseline it replaced, so the trade-off is a number rather than a hunch.
Economics
Inference cost you can forecast
GPU utilization, capacity headroom, and cost per million tokens tracked so the economics stay defensible as usage grows.
Signals we help your team move
We make the invisible platform work visible enough to prioritize, fund, and improve.
Bring us the constraint that started this
Tell us what is pushing the workload in-house — residency, cost, latency, or vendor risk. We will model it honestly and tell you what the real commitment looks like before anyone buys a GPU.
Feasibility assessment
Model the total cost, capacity, and operational load of self-hosting against managed APIs.
Private inference build
Stand up the serving stack, gateway, evaluations, and monitoring inside your own boundary.
GPU operations support
Keep senior engineers close while capacity, models, and usage patterns keep changing.