Reliance Test & Technology, LLC
Federal Contractor · Rank #386
Spending by Agency
Recent Awards
| Description | Agency | Start Date | Amount |
|---|---|---|---|
| IGF::OT::IGF EGLIN - OPERATION&MAINTENANCE SERVICES (E-OMS) | Department of Defense | Apr 1, 2016 | $1.3B |
| ATLANTIC RANGES AND TARGETS DEPARTMENT SERVICES CONTRACT AWARD | Department of Defense | Sep 1, 2020 | $305.8M |
All federal spending data sourced from USAspending.gov, the official source for federal award data as mandated by the Government Accountability Office.
Reliance Test & Technology, LLC Federal Contracts FAQ
Reliance Test & Technology, LLC has received $203,282,408 in total federal obligations across 2 awards and 2 contracts.
Reliance Test & Technology, LLC works with 1 federal agencies, including Department of Defense. The largest agency relationships are based on total dollar obligations.
Reliance Test & Technology, LLC has received federal awards in multiple states.
Reliance Test & Technology, LLC is ranked #386 among the largest federal contractors by total obligation amount. Rankings are based on USASpending.gov data for FY2024.
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Spending figures represent total federal obligations for FY2024, as reported through USASpending.gov. Rankings are based on aggregate obligation amounts across all contract, grant, and direct payment awards.
The this entity record above pulls directly from USASpending.gov federal awards data. What follows is the per-entity context — how this entity sits in the broader U.S. federal government spending distribution and which underlying factors drive the headline numbers.
Every number on this page links back to USASpending.gov federal awards data; the methodology page describes the inputs, refresh cadence, and known limitations of the underlying data product.
Practical use of this page is in combination with the comparison and ranking pages elsewhere on the site, which surface the same data for this entity’s peers within U.S. federal contracts, grants, and awards. A single-entity reading without peer context can be misleading when an entity is an outlier on one axis but typical on another.