Pricing

Compact contextFewer raw fetchesLower LLM spend

Estimate memory costs vs. ROI it delivers as you scale using AI agents.

Monthly usage model

5 agents handle 10,000 customers

Avg context5pages / customer

Model your agent usage

Use today's volume or your 12-month target.

How many records / month?Records added or refreshed each month, across all types.
10,000/ mo
How often do agents recall each record?How often agents access context for each record. If 50 agents look up the same record in a month, that is 50 recalls / record.
5/ rec / mo
How much context / record?A page = ~250 words. One email is ~0.5-1 page; search results, notes, or call transcripts are often ~5-10 pages.
5pages
Total memory recalls / month50,000/ mo
Estimated monthly cost
$103

Discounted usage pricing: $0.003 / 1K input tokens memorized + $0.001 / agent recall.

Up to 23x more work / dollar
Compared with agents querying raw context directly.
Memorization
$52.50/mo
Agent Recalls
$50.00/mo
Memorization savings - up to 65% vs raw LLM processing-$1,350/mo
Recall savings - up to 85% from compact context-$903/mo
Save up to $2,253/mokeep it or scale AI by 23x

Start free with $10 in credits. Startups and novel AI agent use cases can apply for up to $1,000 more. Terms apply.

Frequently asked

Honest answers about memory economics

Personize gives your AI agents durable memory. Instead of re-fetching the same customer, lead, deal, location, or asset context through Claude, ChatGPT, or another LLM every time, your agents recall compact, governed memory from Personize.

The result is a shared memory layer underneath your agents, workflows, apps, and CRMs, so every AI-driven interaction starts with the right context instead of starting from zero.

Need data residency or compliance control?

Deploy the full Personize stack into your own AWS account. Your data, your keys, your region. We handle the software.

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