Orchestrated Analytics Vs Direct upload to AI

Uploading a CSV into ChatGPT feels magical—but is it sustainable for serious analytics? If your team relies on data-driven decisions, this comparison might be worth a read.

Over the last year, many teams tried uploading CSVs or spreadsheets directly into AI tools and asking questions in plain English. It feels magical—but is it sustainable for serious analytics?

Platforms like Biznalyst use an LLM (Large Language Models)-as-orchestrator approach: the model decides which analysis tools to run, with what parameters, and composes the final narrative from ground-truth outputs and knowledge articles. Below we compare this approach with direct file uploads.

Quick Comparison

How the Two Approaches Work

Direct File Uploads

• The model ingests large files, attempts to infer business logic, and answers on the fly.

• Definitions (e.g., “active” or “churned” customer) can change with wording.

• Costs scale with file size and number of questions.

LLM Orchestration + Tools

• Business logic lives in vetted analysis scripts; the LLM is the decision layer.

• Knowledge articles define terms and policies; responses are grounded.

• Token use stays lean because raw data never enters the model.

Cost Example (100k-Row Dataset)

Assumptions (conservative):

• 100k rows × ~20 tokens/row ≈ 2,000,000 input tokens to represent the data.

• 10 questions in a session; direct uploads re-contextualize ~50% per question ≈ 1,000,000 tokens/question.

• LLM price example: $0.002 per 1k input tokens.

Scenario	Token Estimate	Illustrative Cost
Direct Upload	Initial 2M + (10 × 1M) ≈ 12,000,000 tokens	12,000 × $0.002 = $24.00
Orchestration	~2,000 tokens/query × 10 ≈ 20,000 tokens	20 × $0.002 = $0.04

Results: Orchestration can be ~600× cheaper per session, while improving consistency and auditability.

When to Use Which

• Direct uploads: quick, exploratory questions on small files.

• Orchestration: governed analytics, repeatable reports, lower costs, less risk.

Takeaway

If accuracy, repeatability, and cost efficiency matter, the orchestration approach wins. It lets non-technical teams ask high-level questions while ensuring results come from trusted tools and definitions—at a fraction of the token cost.