What does re-reading your data cost?
The same questions, the same answers — priced two ways. Adjust your seat mix and token assumptions to see the gap between querying a governed data layer and re-loading data into the model on every step.
saved per user / month, blended across your mix — 77% lower than the file-based approach
Modeled estimates of token flows, calibrated to a realistic analytic usage pattern — not customer telemetry. “File-based” prices a copilot-style, load-files-into-context usage pattern, not any vendor’s product or seat pricing. The direction holds regardless of any single input: query a governed graph, don’t re-load data into the prompt.
How the math works.
Every number traces back to the Fluree Token Economics Forecasting Model — with the load-bearing assumptions named, not buried.
Usage mix
Three modeled analyst archetypes — light, core, and power — defined by sessions per month, agentic loop depth, and data complexity. The default mix is 60/30/10 across a 50-seat team.
Prices
Published mid-2026 frontier API list rates: ≈$5 per million input tokens, ≈$25 per million output, cache reads at ≈10% of the input rate. Both inputs are editable above.
Price regimes
Regime 1 prices today’s subsidized rates. The repricing scenarios raise input and output rates (up to 3× and 4×) and shrink the cached-token discount toward 50% — the roll-off thesis, applied. At today’s rates held flat, a five-year 50→500-seat ramp still saves ~$2.4M (77%); under the repricing path it reaches ~$22M.
Deliberately conservative
Cache writes are modeled as free for both architectures, though real caches charge ~1.25× to write. The file-based pattern caches ~36× more tokens, so this simplification flatters the alternative — not Fluree.
The full model — session-level token flows, the ~22× token gap, and the five-year TCO ramp — is documented in The Tokenomics of Enterprise AI, and the memory architecture behind the flat line in Agentic Memory, Priced.
Questions CFOs ask.
The short answers — the whitepaper carries the receipts.
How much do AI agents cost per user per month?
At today’s published API rates, our modeled analytic-usage pattern lands at roughly $61–$983 per user per month on a file-based, load-everything-into-context approach, depending on usage intensity — versus $18–$195 when agents query a governed knowledge graph with write-back memory. Blended across a realistic seat mix, that’s about $222 versus $52: a 77% reduction.
What does “file-based” mean in this calculator?
It prices a copilot-style usage pattern — loading raw files, extracts, and a growing conversation transcript into the model’s context window and re-reading them on every step of an agentic loop. It is a pattern, not any vendor’s product or seat pricing. The alternative column prices the same questions answered by querying a governed knowledge graph server-side, with agentic memory resetting context each question.
Where do the token numbers come from?
From the Fluree Token Economics Forecasting Model: modeled estimates of a realistic analytic usage pattern (sessions per month, agentic loop depth, data rows pulled into context), calibrated to published mid-2026 frontier API list rates — not customer telemetry. Every load-bearing assumption is named in the accompanying whitepaper, and the model deliberately flatters the file-based side by treating cache writes as free.
What happens if token prices rise?
That’s what the price regimes model. Today’s rates are widely understood to be subsidized; the repricing scenarios raise input and output rates and shrink the cached-token discount. Because the file-based pattern leans hardest on long context and caching, it is repriced hardest — the gap widens from 77% today to roughly 89% under the high-repricing scenario.