When it fits.
Choose infrastructure engineering when your AI application experiences high API costs, unacceptable latency, strict data privacy mandates requiring on-premise or VPC deployment, or unmonitored production regressions.
What we build together.
- High-throughput private model serving with vLLM, TensorRT-LLM, and autoscaling GPU instances.
- Vector database architecture and index tuning (pgvector HNSW, Qdrant, Pinecone).
- Continuous LLM evaluation pipelines (Ragas, DeepEval) tracking retrieval and generation quality.
- Observability dashboards measuring token throughput, p99 latency, failure rates, and unit costs.
Architecture & trade-offs.
| Operational Criterion | Engineered LLMOps (Nxtr Standard) | Unmanaged Direct API Calls |
|---|---|---|
| Latency Optimization | p99 latency bounded via streaming, semantic caching & vLLM | High unpredictable latency spikes under provider load |
| Cost & Token Governance | Dynamic model routing, prompt compression, rate limits | Runaway monthly API billing with zero spend visibility |
| Regression Detection | Automated evaluation suite on every prompt/code commit | Bugs and hallucinations discovered by end users |
| Data Sovereignty & Privacy | VPC / On-premise private deployment options | Data transmitted to third-party multi-tenant endpoints |
Start with the right questions.
We profile current API expenditures, latency percentiles, concurrency peaks, and compliance constraints. We determine whether self-hosting open models or optimizing commercial API routing produces superior ROI.
Engineering for the real world.
Self-hosting open-weights models introduces GPU reservation costs and operational maintenance. We recommend self-hosting only when throughput, latency, or compliance requirements justify the infrastructure footprint.
Questions before we begin.
When should a company self-host open models instead of using commercial APIs?
Self-hosting becomes cost-effective at high continuous request volumes (millions of tokens daily) or when data privacy regulations strictly forbid third-party API transmission.
How do you optimize pgvector search latency at scale?
We configure HNSW indexing parameters (m, ef_construction), partition embedding tables, optimize query vector caching, and tune work_mem settings.
How do you catch regressions when models or prompts change?
We implement automated CI test suites running golden evaluation datasets, scoring faithfulness, relevance, and semantic drift before deployments.
Explore the thinking. See the work.
How to vet production engineering capabilities in AI teams
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What are you looking to solve?
Tell us about the workflow, users, and constraints. We’ll define a practical next step together.