AI Development Cost in 2026 — LLM, RAG and Agent Pricing

AI feature in existing product

$15,000 – $30,000

6 – 10 weeks

Best for: Adding a chatbot, summariser, classifier or AI search to a product you already ship

Scope:

• Provider integration (OpenAI / Anthropic / Gemini)
• Prompt design + versioning
• Streaming UI
• Basic evaluation harness
• Cost monitoring and rate limiting
• Guardrails and safety checks

Production RAG system

$30,000 – $70,000

10 – 14 weeks

Best for: Q&A over private documents — knowledge base, support, research, legal

Scope:

• Document ingestion pipeline
• Embeddings + vector store (pgvector / Pinecone / Weaviate)
• Hybrid retrieval (vector + keyword)
• LLM generation with grounded citations
• Evaluation harness with golden questions
• Admin tooling for content updates
• Cost / latency / quality observability

AI agent / workflow product

$70,000 – $120,000

14 – 20 weeks

Best for: Multi-step AI agents — research, drafting, customer support automation

Scope:

• Multi-step agent orchestration (LangGraph / custom)
• Tool calling + external API integrations
• Memory and conversation persistence
• Human-in-the-loop review workflows
• Evaluation harness with success metrics
• Cost-controlled fallback routing
• Production observability (LangSmith / custom)

Provider choice

OpenAI / Anthropic / Gemini pricing varies. Self-hosted open models (Llama, Mistral) shift cost from inference to infrastructure.

Evaluation depth

Light eval harness: 1 week. Production-grade eval with golden datasets and regression tests: 3–4 weeks.

Retrieval complexity

Pure vector retrieval is fastest. Hybrid retrieval + reranking adds 2–3 weeks but typically improves answer quality 20–30%.

Document ingestion scope

PDFs + simple text: 1 week. Scanned docs with OCR + structured extraction: 3–5 weeks.

Latency requirements

Sub-2-second responses with streaming: standard. Sub-500ms requires careful provider, model and caching choices.

Which LLM should we use?

Depends on cost, latency and task. We help you choose between OpenAI GPT, Anthropic Claude, Google Gemini and open-weights models like Llama / Mistral — and how to fail over between them.

Do you fine-tune models?

Where it's actually useful, yes. For most products, well-designed prompts + RAG outperform fine-tuning on cost and maintenance. We push back on fine-tuning when it's a vanity ask.

What's the ongoing cost?

Two components: provider inference cost (depends on usage) and a retainer for prompt / model / eval maintenance (typically $4,000–$10,000 / month).

Do you handle safety and hallucinations?

Yes. Guardrails for safety, citation requirements for RAG, evaluator harnesses with regression tests, and cost monitoring — production AI is engineering, not prompting.