Same affordable, developer-first spirit — but point-in-time correct and auditable to the filing.
Financial Modeling Prep is a popular low-cost financial-data API with broad coverage (fundamentals, ratios, prices, transcripts, 13F) and one of the widest MCP tool surfaces in the category. Valuein covers a narrower domain — SEC fundamentals — but with point-in-time vintaging, a survivorship-free universe, and per-figure traceability FMP doesn't offer.
The short version, including where Financial Modeling Prep (FMP) is the better pick.
FMP wins on raw breadth-per-dollar. Valuein wins where it matters for backtests and AI agents: every figure is point-in-time correct, the universe includes delisted names, and every figure the tools return carries a fact_id that resolves to the exact SEC filing — so a restated or hallucinated value in your agent's answer has no fact_id to show and stands out.
Backtest that survives out-of-sample: Point-in-time vintage + every delisted name.
FMP covers far more ground per dollar — global markets, transcripts, ETF/fund holdings, prices
Quants and AI builders who need SEC fundamentals that are point-in-time correct and auditable — and are willing to use FMP or another source for prices and global breadth.
What you get done, not just a feature checklist.
Grouped by what you are trying to do. Scroll sideways on a phone.
| Feature | Valuein | Financial Modeling Prep (FMP) |
|---|---|---|
| Data integrity and audit | ||
| Point-in-Time | accepted_at vintage — no look-ahead | No advertised PIT vintaging |
| Survivorship Bias | All delisted companies included | Not addressed |
| Per-fact Audit Trail | fact_id → SEC filing on every figure | No per-fact filing lineage |
| Published Accuracy | CI-gated consistency baseline, public | Accuracy complaints reported in dev forums |
| Agents and workflow | ||
| AI Agent Access | MCP + agent-safety guardrails | Official MCP — broad, no governance layer |
| Coverage and depth | ||
| History Depth | 1993 → present | Up to ~30 years (top tier) |
| Access and pricing | ||
| Format | Columnar Parquet — DuckDB-native | REST/JSON |
Financial Modeling Prep (FMP) details and pricing are based on publicly available information as of June 2026 and may have changed; verify on their site. Where a competitor's figure is vendor-reported or estimated, we say so.
Install the SDK and read a company with no token and no card: the free sample runs the same code your plan will. Add a key later and nothing else changes.
from valuein_sdk import ValueinClient with ValueinClient() as client: # What a ticker or CIK resolves to. A CIK also reaches delisted companies. print(client.resolve(["AAPL", 320193])) # One company's rows from a table, with the point-in-time filter applied. facts = client.read_table( "fact", tickers="AAPL", columns=["standard_concept", "period_end", "fiscal_period", "numeric_value"], limit=10, ) print(facts) # Named SQL templates. Pass parameters as keyword arguments. print(client.list_templates()[:5]) df = client.run_template("fundamentals_by_ticker", ticker="AAPL") print(df.head()) # Links to the SEC filings behind the numbers. links = client.filing_links("AAPL", form_types=["10-K"], limit=3) print(links[["form_type", "filing_date", "document_url"]]) # Restatement Radar: figures a later filing changed (every plan). changed = client.restatements(ticker="AAPL", limit=5) print(changed[["standard_concept", "period_end", "first_value", "current_value", "disclosure_class"]])Yes, if you need point-in-time-correct, survivorship-free SEC fundamentals you can audit to the filing — especially for backtesting or AI agents. FMP is the better pick if you want the widest cheap coverage across global markets, transcripts, and prices in one key.
FMP does not advertise point-in-time vintaging or restatement handling. Valuein stores an accepted_at vintage so a backtest sees only what was known on each date, with no look-ahead or survivorship bias.
FMP's MCP is a broad data connector (250+ tools). Valuein's MCP adds an agent-safety layer — temperature-0 runs, a human-in-the-loop approval ledger, and a fact_id on every figure the tools return, so the figures in the agent's output are auditable to the source filing.
Start with the free sample. No card, no sales call; pricing is public.