What does “AI-assisted email automation” cost?

Many companies are curious about using large language models (LLMs) in customer support, but are unsure what a realistic, practical setup actually looks like. There is often a gap between abstract promises (“AI answers your emails”) and systems that can be integrated safely i nto existing workflows, with humans still in control.

To make this concrete, the following example describes a common, production-ready support automation pattern that focuses on efficiency rather than full automation. It shows how LLMs are typically used as an assistive layer, while established mail or ticket systems remain the authoritative source of truth.

A common support-automation setup looks like this: 300–500 incoming emails/tickets per day are automatically read, classified (e.g., billing / technical / sales / spam), and a draft reply is prepared for a human to approve and send. The language model runs via an API (e.g., OpenAI), while your existing mail/ticket system remains the source of truth.

Two cost buckets: implementation vs. usage

• One-time / project cost: integration with your mail/ticket system, routing rules, guardrails, evaluation on your historical tickets, logging/audit, and rollout.
• Ongoing cost: mainly the model’s token usage (plus a small amount for hosting and monitoring).

Token-based usage cost (quick, realistic estimate)

Model providers charge per token (roughly “pieces of text”). The total per ticket depends on: how long the incoming message is, how much context you include (policy, knowledge base), and how long the draft reply should be.

Assumptions for a “simple but useful” workflow

• Tickets/day: 300–500 (we’ll use 400 as a middle value)
• Input tokens per ticket: 600–1,200 (email + minimal instructions + a little context)
• Output tokens per ticket: 120–300 (classification + short draft reply)

Example monthly token volume

Using 400 tickets/day and a mid-case of 900 input + 250 output tokens:

• Daily input: 400 × 900 = 360,000 tokens
• Daily output: 400 × 250 = 100,000 tokens
• Monthly (30 days): 10.8M input + 3.0M output tokens

What this costs with OpenAI (typical choice for this use case)

If we use gpt-4o-mini (fast, cost-effective for classification + drafting), OpenAI’s current standard pricing is $0.15 / 1M input and $0.60 / 1M output tokens. :contentReference[oaicite:0]{index=0}

Scenario	Tokens / ticket (in + out)	Monthly model cost (approx.)
Lean prompts	600 + 150	~$1–$2
Mid-case (example above)	900 + 250	~$3–$5
More context / longer drafts	1,200 + 300	~$5–$8

The surprising takeaway: for “drafting + classification” workloads, token fees are often not the main expense. The main work is usually integration, reliability, and making sure the drafts are consistently safe and on-brand.

What about AWS (Bedrock) instead?

AWS Bedrock offers multiple models/vendors under one AWS umbrella (good for procurement and governance). Pricing is still token-based, but varies by model. For example, Bedrock’s on-demand pricing page lists (among others) Meta Llama 2 Chat 70B at $0.00195 / 1K input and $0.00256 / 1K output tokens (≈ $1.95 / 1M input and $2.56 / 1M output). :contentReference[oaicite:1]{index=1}

Using the same mid-case token volume (10.8M in / 3.0M out), that example would land around: ~$21 input + ~$8 output ≈ ~$29/month for that specific model pricing. (Other Bedrock models can be cheaper or more expensive; the “right” choice depends on quality needs, latency, and compliance.)

How to keep costs predictable (and quality high)

• Split the workflow: cheap model for triage/classification, stronger model only for complex tickets.
• Use “just enough context”: include only the relevant policy/KB snippets, not the whole handbook.
• Cap draft length: short drafts reduce output tokens and keep replies readable.
• Cache stable instructions: repeated boilerplate can be billed as cached input where supported. :contentReference[oaicite:2]{index=2}

Next step

If you tell me (1) your average email length, (2) how many categories you need, and (3) whether replies must cite internal policy/knowledge base, I can give you a tighter estimate and propose a few architecture options: OpenAI API, AWS Bedrock, or a hybrid setup with routing and human approval. Want the simplest “human-in-the-loop drafts only”, or a more automated flow with suggested actions (refund, reship, escalate)?