Category: Cost-Effective Enterprise AI

40-article series on cost-effective AI implementation in enterprise

Context Window Economics — Managing the Fade Problem

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19102793  59stabilfr·wdophcgmx
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The expansion of LLM context windows — from 4K tokens in 2022 to 1M+ in 2025 — has created a tempting illusion: that enterprise applications can simply load all relevant information into a single prompt and expect reliable retrieval. Empirical research consistently contradicts this assumption. Context windows are not uniform attention surfaces; they exhibit systematic biases in which informatio...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19102793 59stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources25%○≥80% from editorially reviewed sources
[t]Trusted63%○≥80% from verified, high-quality sources
[a]DOI63%○≥80% have a Digital Object Identifier
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[h]Freshness [REQ]29%✗≥60% of references from 2025–2026. Current: 29%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
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Local LLM Deployment — Hardware Requirements and True Costs

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19097902  48stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (45 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The decision between cloud-hosted API inference and local LLM deployment represents one of the most consequential infrastructure choices enterprises face in 2026. While API providers offer simplicity and elastic scaling, local deployment promises data sovereignty, predictable costs, and elimination of per-token pricing. This article provides a rigorous analysis of hardware requirements across d...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19097902 48stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (45 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Pricing Deep Dive: Token Economics Across Major Providers

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19087980  48stabilfr·wdophcgmx
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (45 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The cost of large language model (LLM) inference has become the dominant line item in enterprise AI budgets, with inference now accounting for approximately 85% of total AI spending. Yet token pricing structures remain opaque, inconsistent across providers, and poorly understood by the engineers who design systems around them. This article dissects the token economics of major LLM providers as ...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19087980 48stabilfr·wdophcgmx
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[t]Trusted47%○≥80% from verified, high-quality sources
[a]DOI37%○≥80% have a Digital Object Identifier
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[g]Code—○Source code available on GitHub
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Score = Ref Trust (45 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Caching and Context Management — Reducing Token Costs by 80%

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19076627  51stabilfr·wdophcgmx
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (60 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Token costs are the largest variable expense in production AI systems. For enterprises running thousands of daily API calls, optimising how context is stored, reused, and compressed is not an architectural nicety — it is the difference between a viable product and an unscalable one. This article provides a practitioner's map of the three caching layers now available to enterprise AI teams — KV-...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19076627 51stabilfr·wdophcgmx
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[t]Trusted60%○≥80% from verified, high-quality sources
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (60 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Deterministic Guardrails for Enterprise Agents — Compliance Without Killing Autonomy

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19053079  41stabilfr·wdophcgmx
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The enterprise AI agent landscape in 2026 faces a paradox: organizations deploy autonomous agents to reduce costs and increase throughput, yet every autonomous action introduces compliance risk. The EU AI Act reaches full enforcement on August 2, 2026, NIST has launched its AI Agent Standards Initiative, and enterprises face penalties of up to 7% of global turnover for non-compliance. This arti...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19053079 41stabilfr·wdophcgmx
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[t]Trusted55%○≥80% from verified, high-quality sources
[a]DOI30%○≥80% have a Digital Object Identifier
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[l]Academic30%○≥80% from journals/conferences/preprints
[f]Free Access55%○≥80% are freely accessible
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[w]Words [REQ]889✗Minimum 2,000 words for a full research article. Current: 889
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19053079
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]47%✗≥60% of references from 2025–2026. Current: 47%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (44 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Container Orchestration for AI — Kubernetes Cost Optimization

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19043029  37stabilfr·wdophcgmx
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[a]DOI23%○≥80% have a Digital Object Identifier
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[l]Academic23%○≥80% from journals/conferences/preprints
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[r]References22 refs✓Minimum 10 references required
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]55%✗≥60% of references from 2025–2026. Current: 55%
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Container orchestration for AI workloads presents a unique economic challenge: the intersection of expensive hardware (GPUs), bursty demand patterns (training vs. inference), and the operational complexity of multi-tenant scheduling. This article provides a systematic analysis of Kubernetes cost optimization strategies for AI — from GPU partitioning and spot instance economics to autoscaling po...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19043029 37stabilfr·wdophcgmx
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[t]Trusted23%○≥80% from verified, high-quality sources
[a]DOI23%○≥80% have a Digital Object Identifier
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[l]Academic23%○≥80% from journals/conferences/preprints
[f]Free Access73%○≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]2,429✓Minimum 2,000 words for a full research article. Current: 2,429
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19043029
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]55%✗≥60% of references from 2025–2026. Current: 55%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (28 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Enterprise AI Agents as the New Insider Threat: A Cost-Effectiveness Analysis of Autonomous Risk

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19019216  41stabilfr·wdophcgmx
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[t]Trusted32%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
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[l]Academic13%○≥80% from journals/conferences/preprints
[f]Free Access26%○≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]3,124✓Minimum 2,000 words for a full research article. Current: 3,124
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19019216
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]71%✓≥60% of references from 2025–2026. Current: 71%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (24 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The rapid deployment of autonomous AI agents across enterprise environments has introduced a novel category of insider threat that traditional cybersecurity frameworks are ill-equipped to address. According to the Thales 2026 Data Threat Report, 61% of organizations now cite AI as their top data security concern, while only 34% maintain visibility into where all their data resides. This article...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19019216 41stabilfr·wdophcgmx
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[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted32%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
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[l]Academic13%○≥80% from journals/conferences/preprints
[f]Free Access26%○≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]3,124✓Minimum 2,000 words for a full research article. Current: 3,124
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19019216
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]71%✓≥60% of references from 2025–2026. Current: 71%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (24 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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Buy vs Build in 2026: Why CIOs Are Choosing Integrated Agentic Ecosystems

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19005352  47stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted42%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed42%○≥80% have metadata indexed
[l]Academic25%○≥80% from journals/conferences/preprints
[f]Free Access33%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,029✓Minimum 2,000 words for a full research article. Current: 2,029
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19005352
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]78%✓≥60% of references from 2025–2026. Current: 78%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (34 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The classic "build vs buy" dilemma in enterprise software has been resolved for most AI deployments in 2026 — not by a clear winner, but by a third option that renders the original question obsolete. As Gartner projects worldwide AI spending at $2.5 trillion in 2026, enterprises are abandoning bespoke AI moonshots in favour of orchestrated integration across incumbent vendor ecosystems. This ar...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19005352 47stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted42%○≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed42%○≥80% have metadata indexed
[l]Academic25%○≥80% from journals/conferences/preprints
[f]Free Access33%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,029✓Minimum 2,000 words for a full research article. Current: 2,029
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19005352
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]78%✓≥60% of references from 2025–2026. Current: 78%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (34 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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Why Companies Don’t Want You to Know the Real Cost of AI

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18944159  43stabilfr·wdophcgmx
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18944159
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Score = Ref Trust (37 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The current landscape of artificial intelligence pricing operates on a fundamental deception: what consumers pay bears almost no relationship to what the technology actually costs. This paper explores the economic mechanics behind platform subsidisation, the strategic motivations for concealing true costs, and the implications for enterprises building AI-powered products. Drawing on platform ec...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18944159 43stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted44%○≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed38%○≥80% have metadata indexed
[l]Academic38%○≥80% from journals/conferences/preprints
[f]Free Access44%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,792✓Minimum 2,000 words for a full research article. Current: 2,792
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18944159
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (37 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Subsidised Intelligence Illusion: What AI Really Costs When the Platform Isn’t Paying

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18943388  34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted29%○≥80% from verified, high-quality sources
[a]DOI21%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed43%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access36%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,738✗Minimum 2,000 words for a full research article. Current: 1,738
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18943388
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (33 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Enterprise AI adoption has accelerated dramatically, yet fundamental cost misperceptions persist. This paper demonstrates that consumer subscription plans for frontier AI models (Claude Max at $100/month, ChatGPT Plus at $20/month) represent heavily platform-subsidised pricing that bears no relation to actual inference economics. Through detailed token consumption analysis and API pricing calcu...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18943388 34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted29%○≥80% from verified, high-quality sources
[a]DOI21%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed43%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access36%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,738✗Minimum 2,000 words for a full research article. Current: 1,738
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18943388
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]31%✗≥60% of references from 2025–2026. Current: 31%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (33 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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