AI Agent Memory Cost Calculator
Estimate the monthly cost of writing memories to an embedding model and retaining the resulting vectors. Enter your own workload and vendor prices; no data leaves this page.
Estimate Memory Cost
Adjust the workload, embedding, and storage assumptions. Results update immediately.
Monthly memory cost
$0.311
Cost per agent run
$0.00
New memories per month
40,000
4,800,000 embedding tokens
Retained vector storage
0.86 GB
$0.096 embedding + $0.215 storage
Estimate excludes model tokens used to retrieve and reason over memory, network requests, replicas, and vendor minimum charges.
How the Calculator Works
Agent memory has at least two direct infrastructure costs. First, text is converted into vectors by an embedding model. Second, those vectors and their metadata are retained in a database. The calculator estimates both and reports a steady-state monthly total.
It deliberately excludes the model tokens used when retrieved memories are inserted into an agent prompt. That cost belongs in the AI agent token budget calculator.
Memory Cost Formula
Monthly embedding cost equals new memories multiplied by average tokens per memory and the embedding price per million tokens. Estimated vector bytes equal dimensions multiplied by four bytes, plus the metadata bytes stored with each memory.
Retained storage assumes the entered number of months has reached a steady state. If you are launching a new system, actual storage ramps up during the first retention window.
Choose Realistic Assumptions
- Count failed and retried runs if they can write memory before failing.
- Measure memories per run from traces instead of using only successful examples.
- Include IDs, timestamps, source text, and index overhead in metadata.
- Use the effective vendor bill, including replicas and minimum capacity, when you compare the estimate with production.
How to Reduce Memory Cost
Write fewer, better memories. Extract durable facts instead of storing every message, deduplicate near-identical records, and expire memories that no longer affect decisions. Smaller vectors reduce storage, but retrieval quality should be evaluated before changing embedding models.
Track the write decision as an event so you can connect each stored memory to an agent run. The AI agent cost tracking guideexplains the event schema and rollups.
Frequently Asked Questions
Does AI agent memory cost include LLM tokens?
This calculator includes tokens sent to the embedding model. It does not include retrieved memories added to a reasoning-model prompt, which should be tracked as input tokens for that model call.
Why estimate four bytes per vector dimension?
A common uncompressed representation stores each dimension as a 32-bit floating-point number. Quantization and database indexes can change the real number, so use the result as a planning estimate.
Is vector storage usually the largest agent cost?
Often it is not. Repeated LLM calls usually dominate, but memory cost becomes meaningful at high write volumes, long retention periods, or when the database has minimum capacity and replica charges.
Next Steps
Read how AI agent memory fits into the complete loop, then instrument real runs with the cost tracking guide.
Understand AI agent memory and feedback →ai agent memory cost calculator — return to the complete AI agent architecture guide.
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