Not everything belongs on-prem.
For most workloads, a cloud LLM is the faster and cheaper answer — and pretending otherwise costs clients money. We build on Claude, GPT, and Gemini with the same engineering discipline we bring to on-prem work: real retrieval, real evaluation, and governance your compliance team will sign off on.
What we do on cloud
- Pick the right deployment — local, cloud, or hybrid
- Cut API spend without cutting quality
- Ship RAG and agent systems that survive real users
- Put governance and audit controls around third-party APIs
Where we help
Choosing the deployment
A short, evidence-based answer to local vs cloud vs hybrid for your specific workload — data sensitivity, volume, latency floor, and budget. We deploy both, so the recommendation isn't a sales pitch. Often it saves a client the hardware purchase they came in asking for.
Cost optimization
Most cloud AI bills are 2–5× larger than they need to be. Prompt caching, batching, right-sizing the model to the task, and routing easy work to cheap models. We measure quality before and after, so savings don't quietly become regressions.
Building RAG & agents
Hybrid retrieval, reranking, tool-using agents, and the evaluation harness that tells you whether a change helped. The same architecture we run on-prem, built against cloud APIs — which means no GPU, and a working pilot in weeks.
Governance & compliance
PII detection and redaction before egress, data-retention and training opt-outs, region pinning, audit logging, and the BAAs and DPAs your legal team will ask about. The controls that turn "we can't use a third-party API" into a documented, defensible yes.
Local, cloud, or both?
The short version of a conversation we have most weeks. Your workload probably has rows pulling in both directions — that's normal, and it's usually an argument for a hybrid split.
| What you're weighing | On-premise | Cloud API |
|---|---|---|
| Sensitive or regulated data | Never leaves your network | Leaves, but can be redacted and contractually governed |
| Time to a working pilot | Weeks — hardware, deployment, tuning | Days |
| Cost at low-to-mid volume | Capex plus someone to run it | Usually cheaper — you pay for what you use |
| Cost at very high volume | Wins clearly once the box is saturated | Scales linearly with usage |
| Model quality ceiling | Best available open-weight models | Frontier models, updated without your involvement |
| Operational burden | Yours: drivers, upgrades, capacity | The provider's |
| Vendor and policy risk | None — your stack, your rules | Terms and deprecations are real; mitigate with abstraction |
When each one wins
Start on cloud when…
- No contract or regulation forbids third-party processing
- You want to learn what the workflow actually needs before buying hardware
- Volume is moderate and bursty — an idle GPU is pure waste
- You need frontier-model quality that no open-weight model matches yet
- There is nobody on the team who wants to own a serving stack
Go on-premise when…
- The data legally or contractually cannot leave your network
- Volume is high and sustained — classification, extraction, embeddings at scale
- You need a hard latency or availability floor with no external dependency
- A multi-year horizon makes vendor policy risk a real exposure
- Predictable fixed cost matters more to finance than a lower average
The answer is usually hybrid
In practice most teams land in the middle: the handful of workflows touching privileged or regulated data run on your own hardware, and everything else runs on a cloud API. Built well, that's one system with one interface — not two projects. Deciding the split is a large part of what we do.
Not sure which side you're on?
Book a 30-minute discovery call. Bring one workflow and we'll tell you where it belongs — even when the answer is "you don't need us for this yet."