How Much Does AI Memory Add to API Costs

Cost Breakdown

Embedding Costs

Every memory needs to be converted to a vector embedding for storage, and every retrieval query needs to be embedded for search. Using OpenAI's text-embedding-3-small, embedding costs roughly $0.02 per million tokens. A typical memory is 50-200 tokens, so embedding 10,000 memories costs about $0.02-0.04. Query embeddings add a similar per-query cost. At 1,000 queries per day, the daily embedding cost for retrieval is under $0.01. Embedding costs are negligible compared to other components.

Storage Costs

Vector storage costs depend on the database and the number of memories. Self-hosted pgvector costs whatever your PostgreSQL instance costs (no per-vector pricing). Managed services like Pinecone charge by usage tier, starting around $25-70 per month for small workloads. For 10,000-100,000 memories with 1536-dimension vectors, storage typically runs $20-50 per month on managed services.

Context Injection Costs

This is the largest cost component. When memories are injected into the prompt, they consume input tokens that the LLM charges for. Five retrieved memories, each around 100 tokens, add 500 tokens to every request. With GPT-4o at $2.50 per million input tokens, this adds $0.00125 per request. At 1,000 requests per day, that is $1.25 per day or roughly $38 per month. This is the memory cost that scales directly with usage volume.

Extraction Costs

If you use an LLM for memory extraction (which produces the highest quality results), each extraction call costs based on the conversation length. Extracting memories from a 2,000-token conversation using GPT-4o costs about $0.005 per extraction. If you extract after every conversation (not every message), this adds up to $5-15 per month for moderate usage.

Total Cost Estimates

For a small application (1,000 memories, 100 queries per day): $5-15 per month for memory infrastructure, adding approximately 5% to your existing LLM costs.

For a medium application (50,000 memories, 1,000 queries per day): $50-150 per month, adding approximately 10% to LLM costs.

For a large application (500,000 memories, 10,000 queries per day): $200-500 per month, adding approximately 8-12% to LLM costs (economies of scale on storage, but injection costs stay proportional).

How to Reduce Costs

Memory consolidation is the most effective cost reduction strategy. By merging redundant memories, archiving stale ones, and compacting related information, consolidation typically reduces the active memory count by 40-60%. Fewer memories mean lower storage costs, faster retrieval, and fewer tokens consumed by injected context.

Selective injection also reduces costs. Instead of injecting memories on every request, only inject when the current query is likely to benefit from historical context. Many queries ("what time is it," "format this as JSON") do not need memory context. A relevance threshold on the retrieval results can skip injection when no memories are sufficiently relevant.

Adaptive Recall's cognitive scoring helps here by returning only high-relevance memories. Instead of injecting the top-5 most similar memories regardless of actual relevance, cognitive scoring ensures that low-relevance memories are not wasting token budget.