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mc.usage.tallies()                                                # all-time
mc.usage.tallies(start="2026-05-01", end="2026-05-31")            # bounded range

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Parameters

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start
str
ISO date ("YYYY-MM-DD"). Inclusive. Omit for no lower bound.
​
end
str
ISO date. Inclusive. Omit for no upper bound.
​
format
str
default:"\"models\""
"models" returns a typed UsageResponse; other values pass through to the underlying serializer.

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Response shape

res = mc.usage.tallies(start="2026-05-01", end="2026-05-31")
res.to_dicts()
# [
#   {"endpoint": "v2/enrich", "count": 1247},
#   {"endpoint": "v2/resolve", "count": 318},
#   ...
# ]

res.to_df()         # pandas DataFrame   (needs [pandas])
res.to_csv("usage.csv")
res.to_table()      # pretty terminal table   (needs [table])
The exact response shape is server-defined; the SDK surfaces whatever list-of-rows the server returns. Use to_dicts() for the raw shape, to_df() for analysis.

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Common patterns

Post-call audit — confirm a specific call actually billed: 
res = mc.usage.tallies(start="2026-06-02", end="2026-06-02")
for row in res.to_dicts():
    print(row)
Monitoring feed — periodically poll for use in dashboards / alerts: 
import time

while True:
    res = mc.usage.tallies(start=today_iso(), end=today_iso())
    send_to_monitoring(res.to_dicts())
    time.sleep(3600)