The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type bool to null
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2059, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2059, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2143, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2005, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type bool to nullNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
AI Pricing Observatory
An open, longitudinal dataset of how AI-native companies price and package their products — snapshots, change events, and econometrics-ready panels.
Maintained by Ananya Pradhan · Canonical source: github.com/ananyapradhan02/ai-pricing-observatory · Data licensed CC-BY-4.0
Why this exists
The most consequential GTM debate in AI — seats vs. usage vs. outcomes — is argued almost entirely without open data. Model token prices are tracked publicly; product pricing and packaging is not. Top SaaS/AI companies reportedly made 1,800+ pricing changes in 2025 alone (~3.6 per company), and nobody keeps the log. This dataset is the public record: versioned, source-cited, and structured for analysis.
Current coverage (2026-07-22)
25 companies · 94 SKUs · 25 usage meters · 25 documented change events across 8 categories: coding agents, foundation labs, support agents, sales agents, agent platforms, creative AI, voice AI, and GTM tooling. Companies include OpenAI, Anthropic, Google, Cursor, Replit, GitHub Copilot, ElevenLabs, Midjourney, Perplexity, Clay, Intercom (Fin), Sierra, Decagon, and more.
Files
data/companies/*.json— source of truth: one record per company, validated againstdata/schema.json. Each snapshot captures pricing models, plans (with quota/"unlimited" definitions and throttle behavior), usage meters, credit dictionaries (incl. whether failed actions consume credits), outcome-pricing terms, bill-shock protections, and enterprise signals — every claim with a source URL and access date.data/companies/[company].json → change_events— first-class log of pricing changes: effective date, silent vs. announced, notice period, grandfathering terms, documented user reaction.data/panels/sku_panel.csv— tidy long format: one row per company × SKU × snapshot date.data/panels/meter_panel.csv— one row per company × usage meter × date, with rates normalized to canonical units (usd_per_1m_tokens,usd_per_resolution,usd_per_credit,usd_per_minute) for cross-vendor comparison.data/panels/events_panel.csv— one row per pricing change event.
Methodology (v0.x, under open review)
Manual collection from primary pricing pages first; confidence recorded per snapshot (public_page > reported > inferred); historical snapshots immutable; no editorializing in data — analysis lives in the quarterly State of AI Pricing report. Full methodology and contribution guide in the GitHub repo. Methodology critiques welcome — open an issue there.
Example questions this data can answer
- What does a "resolution" cost across AI support vendors, and does outcome pricing stick?
- Do pricing changes with notice periods and grandfathering produce less user backlash?
- How opaque are credit systems, and is opacity increasing over time?
- What does "unlimited" actually mean, per vendor, in enforceable terms?
Citation
Pradhan, A. (2026). AI Pricing Observatory [Data set]. https://github.com/ananyapradhan02/ai-pricing-observatory
License
Dataset: CC-BY-4.0 (attribution: "AI Pricing Observatory, Ananya Pradhan"). Code (validator, exporters): MIT.
- Downloads last month
- 163