Skip to content

DIGITAL REALTY TRUST, INC.: operating income or loss

Operating income or loss for DIGITAL REALTY TRUST, INC. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All DIGITAL REALTY TRUST, INC. financial histories

What this measure means

Operating revenue less operating expenses for the reporting period. It excludes items outside the reported operating result and is not free cash flow.

Exact concept: us-gaap:OperatingIncomeLoss. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2009-01-01 to 2025-12-31. The SEC response was captured on 2026-09-20.

Selected filing history

Operating income or loss in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-31658,492,000USD2026-02-1310-K · 0001104659-26-015365
2024-01-012024-12-31471,864,000USD2026-02-1310-K · 0001104659-26-015365
2023-01-012023-12-31524,461,000USD2026-02-1310-K · 0001104659-26-015365
2022-01-012022-12-31589,968,000USD2025-02-2510-K · 0001558370-25-001424
2021-01-012021-12-31694,009,000USD2024-02-2310-K · 0001558370-24-001575
2020-01-012020-12-31557,526,000USD2023-02-2710-K · 0001558370-23-002087
2019-01-012019-12-31594,215,000USD2022-02-2510-K · 0001558370-22-002195
2018-01-012018-12-31549,787,000USD2021-03-0110-K · 0001558370-21-002191
2017-01-012017-12-31451,295,000USD2020-03-0210-K · 0001558370-20-001906
2016-01-012016-12-31497,286,000USD2019-02-2510-K · 0001297996-19-000032
2015-01-012015-12-31401,911,000USD2018-03-0110-K · 0001297996-18-000026
2014-01-012014-12-31258,666,000USD2017-03-0110-K · 0001297996-17-000020
2013-01-012013-12-31381,812,000USD2016-09-2810-K · 0001297996-16-000260
2012-01-012012-12-31366,078,000USD2015-03-0210-K · 0001297996-15-000010
2011-01-012011-12-31304,310,000USD2014-03-0310-K · 0001193125-14-078397
2010-01-012010-12-31242,306,000USD2013-02-2810-K · 0001193125-13-084894
2009-01-012009-12-31177,789,000USD2012-02-2810-K · 0001193125-12-082659

Related financial histories

Inspect the source

Entity
DIGITAL REALTY TRUST, INC. / CIK 0001297996
Captured
2026-09-20T07:50:32.953Z
SEC response SHA-256
1f5fcfd178c7d6e26d50f0d0419ed3fba35c68bffa838ed16ee33e436a47ed14

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0001297996.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))