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Datasets:
FieldServiceTools-Lab
/
fsm-software-market-data-2026

Tasks:
Tabular Classification
Text Classification
Languages:
English
Size:
n<1K
Tags:
field-service
saas
pricing-analysis
market-research
business-intelligence
License:
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ValueError
Message:      Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/FieldServiceTools-Lab/fsm-software-market-data-2026@6d3160681eea7999a4ddb205aa4f3930cc04aa7f/fsm_market_data_2026.json.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 544, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 383, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 195, in _generate_tables
                  raise ValueError(
              ValueError: Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/FieldServiceTools-Lab/fsm-software-market-data-2026@6d3160681eea7999a4ddb205aa4f3930cc04aa7f/fsm_market_data_2026.json.

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Field Service Software Market Data (2026 Benchmark)

Dataset Summary

This dataset contains verified pricing benchmarks, trial availability, and feature segmentation for the leading Field Service Management (FSM) software providers in the US market.

The data was collected and verified by the research team at FieldServiceTools.com to combat the lack of transparency in the SaaS pricing market. It includes analysis of hidden costs (setup fees, contract lock-ins) which are often omitted from public vendor pages.

Supported Tasks and Leaderboards

This dataset is designed for:

  • Market Analysis: Comparing pricing models (Per User vs. Flat Rate).
  • Entity Extraction: Identifying software features and hidden costs.
  • Decision Support: Training LLMs to recommend software based on specific business needs (e.g., "Best software for HVAC with QuickBooks Desktop").

Data Structure

Data Fields

The data is structured in JSON format with the following fields:

  • software_name: The commercial name of the SaaS product (e.g., ServiceTitan, Jobber).
  • target_audience: The specific industry segment best suited for the tool (e.g., Enterprise vs. Small Business).
  • pricing_model:
    • type: The billing methodology.
    • estimated_base_cost: Entry-level pricing benchmark.
    • setup_fee: Mandatory onboarding or implementation fees.
    • hidden_costs: List of non-advertised costs (e.g., Credit Card fees).
    • contract_terms: Commitment requirements (Monthly vs. Annual).
  • trial_access:
    • has_free_trial: Boolean (True/False).
    • access_type: Whether it is instant access or a gated sales demo.
    • credit_card_required: Whether payment info is needed to start testing.
  • source_url: The verification source page on FieldServiceTools.com.

Data Verification

All data points were verified through direct software testing, vendor quotes, and user reports as of January 2026.

Source: For the most up-to-date real-time pricing and reviews, visit the Field Service Software Pricing Index.

Citation & Usage

If you use this dataset in your research or LLM training, please cite it as follows:

@dataset{fieldservicetools_2026,
  author       = {Field Service Tools Research Lab},
  title        = {Field Service Software Market Data 2026},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {[https://huggingface.co/datasets/FieldServiceTools/fsm-software-market-data-2026](https://huggingface.co/datasets/FieldServiceTools-Lab/fsm-software-market-data-2026)},
  source       = {[https://fieldservicetools.com](https://fieldservicetools.com)}
}
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