What does giving your agents memory actually save?

How this is calculated

Two Wisdom Layer–specific numbers do all the work in this calculator: a hallucination-reduction factor and a model-downgrade factor. Both come from our v1.0.1 DeepEval suite (N=3 mean ± stddev). Everything else is your input. No magic multipliers; the math is below.

• Hallucination reduction: 62% — v1.0 Beta groundedness run (Wisdom 0.916 vs Vanilla 0.346, 5/5 probes pass). Cross-validated by v1.0.1 domain runs: coding 2.61× lift (0.997 vs 0.382), sales 2.26× lift (0.979 vs 0.433). See methodology →
• Model-downgrade savings: 67% — small-vs-big benchmark shows Haiku 4.5 + Wisdom Layer scoring 2.27× higher on Groundedness than Sonnet 4.6 with no memory (0.660 vs 0.291), across six customer-support probes, GPT-4o judge. Cost ratio derived from current Anthropic published rates (Sonnet 4.6: $3/M input + $15/M output; Haiku 4.5: $1/M input + $5/M output). See methodology →
• Total monthly conversations = agents × conversations/day × 30
• Hallucinations avoided = total × current rate × 0.62
• Hallucination $ saved = hallucinations avoided × cost per incident
• Model-downgrade $ saved = (only applied if “Sonnet 4.6 / GPT-4o” is selected) total monthly conversations × cost per call × 0.67
• Annual savings = (hallucination $ + model-downgrade $ − Wisdom Layer cost) × 12

Tier suggestion is heuristic: 1–15 agents → Pro ($250/mo founder rate), 16+ agents or customer-facing/multi-tenant deployments → Enterprise (custom, starts $5k/mo). See /pricing for the full structure and confirm with us before sizing.

Honest disclosure: in our cross-memory benchmark, Sonnet 4.6 + mem0 (a competitor’s flagship config) scored 0.06–0.18 higher than Haiku 4.5 + Wisdom across the same probes. We’re publishing that loss because credible benchmarks include the contests you don’t lead. Cost-adjusted — Sonnet is ∼3× the input price — Wisdom still wins per dollar.