LLM application development services: from prototype to production

Which model should your LLM application use?

Start with a hosted API model and switch only when you have a measured reason. GPT, Claude and Gemini models give the best quality per engineering hour, support structured outputs and tool calling out of the box, and let you ship a first version without running inference infrastructure. Open-weight models such as Llama, Qwen and Mistral, served with vLLM or a managed endpoint, make sense when data residency rules forbid sending text to a third party, when volume is high enough that per-token pricing dominates, or when you need a LoRA or QLoRA fine-tune for a narrow, high-volume task.

The second decision is how the model gets your knowledge. For most products that is retrieval, not training: according to Menlo Ventures, November 2024, 51 percent of enterprise production deployments used retrieval-augmented generation while only 9 percent of production models were fine-tuned. Fine-tuning earns its place for style, format and latency at scale, after a retrieval baseline exists. Our guide to RAG vs fine-tuning walks through the decision, and fine-tuning specialists are available when training is the right call.

What does a production-ready LLM architecture look like?

A production LLM application is a thin model call wrapped in a lot of ordinary engineering. The pieces we deliver together: a retrieval layer over your data where grounding is needed (pgvector, Qdrant, Pinecone or Weaviate, with hybrid search and a reranker); an orchestration layer in LangGraph, LlamaIndex, the OpenAI Agents SDK or plain Python against the Responses API or the Anthropic Messages API; tool access through MCP servers or typed function schemas; an evaluation harness that scores every change against a fixed set of real examples; tracing and cost dashboards with Langfuse, LangSmith or OpenTelemetry; and guardrails covering input validation, output schema checks, PII redaction and a deterministic fallback when the model fails.

• Cost and latency controls: prompt caching, model routing (small model first, large model on escalation), streaming, batch endpoints for offline work and hard token budgets per request.
• Quality gates: no change reaches production until the eval suite passes and a human has reviewed regressions on the hardest cases.
• Operations: versioned prompts, rollback for models and prompts alike, and alerts on error rate, latency percentiles and spend.

Generative AI features inside an existing SaaS product

When the LLM capability is one feature in a product you already sell, the architecture above is scoped down to what the feature needs and wired into your existing auth, tenancy and billing. That is covered in detail under AI integration services; the full production checklist is in our guide to deploying LLM apps to production.

How do you engage: one LLM engineer or a blended team?

A single senior LLM engineer is the right shape for one feature in an existing product: an assistant inside a SaaS tool, an extraction step in a pipeline, a summariser in a support workflow. They work inside your repository, your CI and your sprint rituals, with your backend team handling the surrounding plumbing. A blended team is the right shape when the LLM capability is the product itself: an LLM engineer, a backend engineer who owns APIs and data, and an MLOps engineer who owns serving, evaluation and cost. Hyparz offers both as individual experts, blended teams or full remote teams. If you need a whole pod with a lead engineer, see dedicated AI team.

What drives the cost of an LLM application?

Build cost is driven mostly by the quality bar, not the model. A demo that is right most of the time takes days; a feature that is right reliably enough to put in front of customers needs an evaluation set, failure handling and iteration, and that is where the engineering hours go. Other drivers: how messy and scattered the source data is, whether retrieval is needed, how many tools the model must call, compliance requirements such as PII handling and audit logs, and how deep the integration into existing systems runs.

Running cost is driven by tokens per request, requests per day, the model tier and how much of each request can be served from cache or routed to a smaller model. We scope both before the trial starts so there are no surprises. For market rates on the people themselves, see our guide to what it costs to hire AI engineers.