Before / after

What changes when the model moves in-house.

Three representative migrations, side by side. These are illustrative scenarios built from typical workloads, not audited customer benchmarks — your numbers depend on your data.

Support assistant over 2,000 internal articles

RAG on an open 8B model

Illustrative scenario based on a typical SMB knowledge base. Before: a public chat API with pasted context. After: a private retrieval pipeline with citations.

Before

Answer grounded in sourceSometimes
p50 latency~1.9 s
Cost / 1M tokens€9.80
Data leaving the EUYes

After · Nevastack

Answer grounded in sourceiAlways cited
p50 latencyi~0.7 s
Cost / 1M tokensi€1.40
Data leaving the EUiNo

Domain classifier for regulated documents

LoRA fine-tune on 12k labelled samples

Illustrative scenario for a compliance team. Before: prompt engineering against a general model. After: a LoRA adapter trained on labelled history.

Before

Agreement with reviewersInconsistent
Prompt length~2,400 tokens
Training timen/a
Artifact ownershipNone

After · Nevastack

Agreement with reviewersiStable across runs
Prompt lengthi~180 tokens
Training timei~4 h on 1× A100
Artifact ownershipiExportable adapter

Migrating a live product from a hosted API

Dedicated inference endpoint

Illustrative scenario for a product team at steady traffic. Before: pay-as-you-go public API. After: reserved dedicated capacity in Frankfurt.

Before

Monthly compute billVariable
Rate limitsProvider-imposed
Cold startsOccasional
Model changesVendor-controlled

After · Nevastack

Monthly compute billiFixed reservation
Rate limitsiYour capacity
Cold startsiNone
Model changesiPinned by you

Your turn

Run the same comparison on your data

A pilot starts with your corpus, an agreed evaluation set, and a written target. If the target is missed, the setup fee is not charged.

Scope a pilot

Make room for your next idea.

Bring your data, models, and compute into one workspace. Start with a project and build from there.

Open the console Talk to the team