Neural Network Software Market Size and Share
Market Overview
| Study Period | 2019 - 2030 |
|---|---|
| Market Size (2025) | USD 34.76 Billion |
| Market Size (2030) | USD 139.86 Billion |
| Growth Rate (2025 - 2030) | 32.10% CAGR |
| Fastest Growing Market | Asia Pacific |
| Largest Market | North America |
| Market Concentration | Medium |
Major Players
*Disclaimer: Major Players sorted in no particular order Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0. |
|
Neural Network Software Market Analysis by Mordor Intelligence
The Neural Network Software Market size is estimated at USD 34.76 billion in 2025, and is expected to reach USD 139.86 billion by 2030, at a CAGR of 32.10% during the forecast period (2025-2030). Expansion is accelerating as enterprises move from proofs of concept to full-scale rollouts, supported by sovereign-AI programs, foundation-model ecosystems, and cloud platforms that lower adoption barriers. OpenAI’s revenue jump from USD 5.5 billion in December 2024 to USD 10 billion in June 2025, illustrating rising commercial demand for large-scale neural network deployments. Asia-Pacific is the fastest-growing geography because China, Japan, India, and South Korea are localizing large language models and building national AI clouds. Component trends show software tools retaining the majority share, yet services are expanding faster as enterprises seek integration and optimization expertise. Competition continues to intensify, with cloud hyperscalers, enterprise software vendors, and specialist AI firms racing to differentiate on model efficiency, governance, and vertical solutions.
Key Report Takeaways
- By component, software tools held 54.4% of 2024 revenue, while services are projected to expand at a 35.4% CAGR through 2030.
- By deployment mode, cloud solutions commanded 61.3% of the neural network software market share in 2024, whereas hybrid architectures are forecast to grow at a 34.8% CAGR to 2030.
- By type, data mining and archiving led with 38.7% revenue share in 2024; optimization software is expected to advance at a 34.2% CAGR through 2030.
- By application, fraud detection accounted for 24.2% of 2024 revenue; predictive maintenance is projected to record a 35.6% CAGR through 2030.
- By end-user vertical, BFSI represented 23.4% share of the neural network software market size in 2024, while manufacturing is anticipated to expand at a 34.6% CAGR through 2030.
- By geography, North America captured 38.06% revenue in 2024; Asia-Pacific is forecast to post the fastest 35.7% CAGR through 2030.
Global Neural Network Software Market Trends and Insights
Drivers Impact Analysis
| Driver | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
|---|---|---|---|
| Cloud-based AI platforms democratize neural networks | +8.2% | Global, stronger adoption in North America and Europe | Medium term (2-4 years) |
| Rising enterprise demand for predictive analytics | +7.5% | Global, led by manufacturing hubs in APAC and North America | Short term (≤2 years) |
| Growing availability of big data and GPUs | +6.8% | North America and APAC core, tempered by supply constraints | Medium term (2-4 years) |
| Foundation models create new toolchain demand | +5.9% | Global, concentrated in technology-forward regions | Long term (≥4 years) |
| Open-source model marketplaces accelerate adoption | +4.1% | Global, particularly strong in developer communities | Short term (≤2 years) |
| Sovereign-AI initiatives need local neural-network stacks | +3.7% | Europe, APAC, and select emerging markets | Long term (≥4 years) |
| Source: Mordor Intelligence | |||
Cloud-based AI Platforms Democratize Access
Enterprise generative-AI spending is rising 30% in 2025 as mid-market firms adopt managed platforms that remove capital barriers. Red Hat’s purchase of Neural Magic adds optimized inference libraries to its hybrid cloud suite, enabling efficient deployments within private clusters. [1]Red Hat, “Red Hat Announces Definitive Agreement to Acquire Neural Magic,” redhat.com Rackspace’s AI Anywhere service packages pre-built models with predictable subscription pricing, making complex neural network architectures attainable for firms lacking in-house expertise. [2]Rackspace Technology, “Enhance AI Performance in Private Cloud With Rackspace AI,” rackspace.com Google’s Gemini family extends democratization by embedding text-to-image and video generation APIs inside standard cloud consoles, letting developers test multimodal inference without bespoke infrastructure. These platform moves reduce time-to-value and expand the neural network software market across new corporate adopters.
Rising Enterprise Demand for Predictive Analytics
Manufacturers are shifting from reactive to proactive maintenance as neural networks reach 94% accuracy in fault prediction. BMW’s Regensburg plant prevents over 500 minutes of annual assembly disruption by analyzing existing component data, confirming strong ROI in industrial contexts. [4]BMW Group, “Smart Maintenance Using Artificial Intelligence,” press.bmwgroup.com General Motors cut unexpected downtime by 15% and saved USD 20 million yearly after linking IoT sensors with AI-driven scheduling engines. Financial institutions see parallel benefits, with hybrid deep-learning models catching 98.7% of fraudulent payments. Such clear economic gains accelerate software procurement cycles and raise expectations for rapid deployment support from vendors.
Growing Availability of Big Data and GPUs
Global AI compute capacity is projected to grow tenfold by 2027, aided by chip-node advances and advanced packaging, yet supply remains tight because NVIDIA controls 88% of discrete GPU volume and depends on limited CoWoS lines. Scarcity creates a two-tier hardware market where resource-rich firms pursue frontier models while others rely on smaller architectures. Intel’s Arc GPUs, paired with PyTorch, lower entry costs, broaden hardware choice. The net result is continued capacity expansion, but also heightened interest in efficient model compression that keeps performance high on limited resources, sustaining neural network software market momentum.
Foundation Models Create New Toolchain Demand
Databricks’ DBRX shows how open foundation models let enterprises fine-tune on proprietary data while retaining ownership, cutting vendor lock-in expenses. TorchTitan achieves 65% faster training across 128 GPUs, highlighting the need for distributed training orchestration. Governance layers mature in parallel; IBM watsonx.governance automates EU AI Act compliance checkpoints, ensuring models meet transparency mandates. [3]IBM Staff, “IBM watsonx.governance,” IBM, ibm.com These specialized toolchains create new revenue pools across MLOps, observability, and policy engines, broadening the neural network software market footprint.
Restraints Impact Analysis
| Restraint | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
|---|---|---|---|
| Shortage of deep-learning MLOps talent | -4.8% | Global, most acute in Europe and North America | Medium term (2-4 years) |
| Data-privacy and governance burdens | -3.2% | Europe (GDPR) with expanding global influence | Long term (≥4 years) |
| GPU supply-chain volatility inflates costs | -2.9% | Global, concentrated impact on compute-intensive applications | Short term (≤2 years) |
| Energy and ESG scrutiny of training workloads | -1.7% | Developed markets enforcing sustainability mandates | Long term (≥4 years) |
| Source: Mordor Intelligence | |||
Shortage of Deep-Learning MLOps Talent
Only 28% of AI adopters employ dedicated MLOps engineers, and 75% of European employers struggled to fill AI roles in 2024, spotlighting a persistent skills gap. Tech giants now deliver certification curricula to accelerate reskilling, yet curricula cannot match rapid framework changes. Without sufficient practitioners to operationalize models, deployment timelines lengthen and service revenues climb, capping short-term neural network software market gains even as demand grows.
Data-Privacy and Governance Burdens
The EU AI Act introduces mandatory risk assessments and disclosure, increasing compliance overhead. Financial institutions in Asia avoid AI for AML tasks because legacy systems cannot satisfy data lineage tests. GDPR further compels privacy-preserving inference, prompting investment in model monitoring and synthetic-data techniques. Smaller firms face higher proportional costs, discouraging early adoption despite strong interest, and thereby tempering neural network software market expansion.
Segment Analysis
By Component: Software Stability and Services Upswing
Software frameworks, libraries, and AutoML suites delivered 54.4% of 2024 revenue, underscoring their role as the structural backbone of the neural network software market. Core development kits such as TensorFlow, PyTorch, and JAX remain essential, yet buyers increasingly demand turnkey modules that shorten experimentation cycles. Services, including professional consulting and managed operations, are rising at 35.4% CAGR as firms outsource integration, tuning, and lifecycle management.
Managed services captured incremental gains equal to 35.4% of the neural network software market size in 2024 as cloud providers embedded AI specialists within subscription packages to accelerate time-to-production. Professional service teams respond to sector-specific needs—e.g., healthcare imaging compliance—further boosting service share. Over the forecast window, vendor differentiation will hinge on domain depth and outcome-based pricing rather than licensing alone.
Note: Segment shares of all individual segments available upon report purchase
By Deployment Mode: Hybrid Flexibility Underpins Sovereign AI
Public cloud retained 61.3% of the neural network software market share in 2024 because hyperscalers offer elastic compute for training and inference. Enterprises leverage GPU clusters on demand, avoiding up-front capital outlays. Yet sovereignty, latency, and regulatory requirements are shifting growth toward hybrid deployments, forecast at 34.8% CAGR to 2030.
Hybrid architectures let data reside on-premise or in private clouds while model training happens in scalable public environments. Financial services and healthcare operators adopt this topology to protect sensitive data while exploiting cloud scale. The growing use of confidential computing and federated learning will amplify hybrid demand, reshaping resource planning for vendors.
By Type: Optimization Engines Gain Momentum
Data mining and archiving applications controlled 38.7% revenue in 2024, reflecting entrenched usage for pattern discovery across large datasets. Visualization and analytical dashboards translate neural network outputs into actionable insights for business users, cementing their place within analytics stacks.
Optimization software is rising fastest at 34.2% CAGR, targeting supply-chain routing, production scheduling, and resource allocation. Early adoption in automotive assembly lines shows predictive algorithms reducing changeover time and scrap rates, driving direct cost savings. As lean manufacturing and ESG targets converge, demand for optimization modules will add fresh layers to the neural network software market.
By Application: Predictive Maintenance Takes Flight
Fraud detection dominated with a 24.2% share in 2024, boosted by BFSI's focus on transaction monitoring. Accuracy above 98% is now table stakes, pushing vendors toward explainable-AI add-ons.
Predictive maintenance accounts for just a fraction today, but adds the highest incremental weight to the neural network software market size, growing at 35.6% CAGR. Industrial equipment makers and process manufacturers embed neural networks into edge gateways to anticipate faults days ahead, curbing downtime and inventory costs. Successful pilots across automotive, chemicals, and mining spark enterprise-wide rollouts, ensuring robust future demand.
Note: Segment shares of all individual segments available upon report purchase
By End-user Vertical: Manufacturing Rises, BFSI Holds Ground
BFSI kept 23.4% of revenue in 2024 through broad adoption in fraud, credit scoring, and algo-trading. Regulatory reporting obligations keep spending steady.
Manufacturing is projected to post 34.6% CAGR as Industry 4.0 projects converge with IoT sensor rollouts. The segment captured 34.6% of new neural network software market size between 2024 and 2025, driven by condition monitoring suites that deliver measurable yield gains. The transition from proof-of-concept to plant-wide deployment fuels multi-year subscription commitments, consolidating vendor relationships.
Geography Analysis
North America held 38.06% revenue in 2024 due to an established venture-capital ecosystem, advanced cloud infrastructure, and dense talent pools. OpenAI doubling annual recurring revenue to USD 10 billion highlights commercial maturity, while hyperscalers continually widen managed-AI portfolios. Canada leverages academic clusters in Montreal and Toronto, yet chip fabrication dependence on Asia limits sovereign compute ambitions. Mexico leverages nearshoring to integrate neural network solutions in logistics and automotive production, strengthening regional supply chains.
Asia-Pacific is forecast to grow at 35.7% CAGR, with the neural network software market size jumping to USD 300 billion by 2030 as China, Japan, India, and South Korea implement national AI clouds. China leads 37 of 44 critical R&D disciplines, channelling state financing toward industrial AI upgrades. Japan hosts OpenAI’s first Indo-Pacific office, confirming local demand for enterprise GPT solutions that respect linguistic nuance and data-residency laws. India nurtures start-ups through government sandboxes, while Australia and Singapore invest in safety and governance research, creating diversified regional opportunities.
Europe pursues technological autonomy through sovereign-AI projects. NVIDIA is supplying over 3,000 exaflops of Blackwell clusters to European data-center partners, forming a continental spine for regulated AI workloads. Germany’s industrial AI cloud and France’s telco-led model-hosting hubs add depth. However, talent shortages persist, with 75% of employers unable to staff AI roles, driving wage inflation and cross-border migration. Strict GDPR and forthcoming AI-Act requirements favor vendors offering governance tooling, shaping procurement priorities.
Competitive Landscape
The neural network software market remains moderately fragmented. Cloud hyperscalers leverage integrated stacks, bundling compute, frameworks, and managed services under consumption-based pricing. Enterprise application vendors target sector requirements; for example, SAP embeds neural networks into S/4HANA manufacturing modules. Pure-play AI firms such as DataRobot command premium valuations, reflecting investor appetite for domain-agnostic AutoML and MLOps suites.
Strategic mergers are rising. Red Hat’s acquisition of Neural Magic secures sparse-matrix inference technology that slashes model latency on off-the-shelf CPUs, differentiating hybrid cloud performance. IBM integrates watsonx.governance with mainstay data catalog products, positioning governance as a cross-sell catalyst. Partnerships also matter: NVIDIA aligns with European governments to embed Blackwell systems inside sovereign data centers, while Databricks and Hugging Face co-develop optimized transformer pipelines for regulated industries.
Technology differentiation is shifting from raw benchmark scores to efficiency and governance. DeepSeek’s mixture-of-experts model achieved near-frontier performance with only USD 5.6 million in training expenditure, proving cost-effective innovation possible and intensifying competitive pressure on compute-heavy incumbents. Vendors now emphasize quantization, pruning, and distillation toolkits alongside observability dashboards to ensure responsible AI. Supply-chain constraints around GPUs elevate software that maximizes throughput on scarce hardware, creating a premium on efficiency algorithms.
Neural Network Software Industry Leaders
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DataRobot Inc.
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H2O.ai Inc.
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C3.ai Inc.
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Hugging Face Inc.
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DeepMind Technologies Ltd.
- *Disclaimer: Major Players sorted in no particular order
Recent Industry Developments
- June 2025: OpenAI achieved USD 10 billion in annual recurring revenue and pursued a USD 40 billion funding round led by SoftBank at a USD 300 billion valuation.
- March 2025: NVIDIA partnered with European nations to deploy over 3,000 exaflops of Blackwell systems for sovereign AI infrastructure.
- February 2025: DataRobot released generative-AI monitoring tools that allow real-time intervention to secure outcomes in enterprise environments.
- January 2025: DeepSeek launched an open-source chatbot with a 671-billion-parameter mixture-of-experts architecture, training for only USD 5.6 million.
- November 2024: Red Hat agreed to acquire Neural Magic to enhance generative AI inference across hybrid clouds.
- May 2024: DataRobot added AI observability functions with live rollback for misbehaving models.
Research Methodology Framework and Report Scope
Market Definitions and Key Coverage
Our study defines the neural network software market as revenues generated by purpose-built frameworks, libraries, AutoML suites, and cloud runtime platforms that create, train, and run artificial neural networks across public-cloud, on-premise, and hybrid environments.
Scope Exclusions: Hardware accelerators, generic analytics tools, and standalone professional services fall outside the study.
Segmentation Overview
- By Component
- Software Tools
- Frameworks and Libraries
- AutoML Platforms
- Platform (PaaS)
- Services
- Managed Services
- Professional Services
- Software Tools
- By Deployment Mode
- Cloud
- On-premise
- Hybrid
- By Type
- Data Mining and Archiving
- Analytical Software
- Optimization Software
- Visualization Software
- By Application
- Fraud Detection
- Hardware Diagnostics
- Financial Forecasting
- Image Optimization
- Predictive Maintenance
- Natural Language Processing
- Speech Recognition
- Others
- By End-user Vertical
- BFSI
- Healthcare
- Retail and E-Commerce
- Defense and Government
- Media and Entertainment
- Logistics and Transportation
- Energy and Utilities
- Manufacturing
- Other End-user Verticals
- By Geography
- North America
- United States
- Canada
- Mexico
- South America
- Brazil
- Argentina
- Chile
- Rest of South America
- Europe
- Germany
- United Kingdom
- France
- Italy
- Spain
- Russia
- Rest of Europe
- Asia-Pacific
- China
- India
- Japan
- South Korea
- Malaysia
- Singapore
- Australia
- Rest of Asia-Pacific
- Middle East and Africa
- Middle East
- United Arab Emirates
- Saudi Arabia
- Turkey
- Rest of Middle East
- Africa
- South Africa
- Nigeria
- Rest of Africa
- Middle East
- North America
Detailed Research Methodology and Data Validation
Primary Research
We interview enterprise AI architects, cloud procurement leads, and open-source maintainers across North America, Europe, and Asia-Pacific. Their input on license models, average seat prices, and adoption cadence lets us reconcile modeling assumptions and stress-test preliminary findings.
Desk Research
Mordor analysts gather macro and micro signals from respected, non-paywalled outlets such as the OECD AI Policy Observatory, NIST AI benchmark datasets, US Census ICT spending tables, IEEE digital-library proceedings, World Intellectual Property Organization patent counts, and regional trade filings. Public company 10-Ks, investor presentations, and disclosed cloud-provider usage metrics help us sense-check addressable spend. Paid resources, including D&B Hoovers and Dow Jones Factiva, contribute verified revenue prints that sharpen vendor splits. The sources named are illustrative only; many additional records inform data collection, validation, and clarification.
Market-Sizing & Forecasting
A top-down build begins with global enterprise software outlays, carving the share earmarked for neural-network workloads through indicators such as GPU instance spending, public-cloud AI billings, developer-community growth, model-training hours, and patent momentum. Select bottom-up checks, vendor revenue roll-ups and sampled average-selling-price x active-deployment counts, refine totals. Forecasts rely on multivariate regression blending enterprise IT budget trends, AI regulatory timelines, and silicon supply expansion, with coefficients reviewed by interviewed experts. Missing granular splits are bridged using analogous segment benchmarks and moving-average smoothing.
Data Validation & Update Cycle
Outputs face a two-step peer review, variance checks against external size signals, and anomaly resolution through re-contacts before sign-off. Reports refresh annually, and unexpected events such as new AI regulation trigger interim revisions. A final analyst sweep just before delivery ensures clients receive our latest calibrated view.
Why Mordor's Neural Network Software Baseline Inspires Confidence
Published estimates often diverge because firms apply different scopes, pricing assumptions, and update rhythms.
Key gap drivers include whether cloud-platform fees are folded in, if services are lumped with software, and the aggressiveness of forward CAGRs.
Benchmark comparison
| Market Size | Anonymized source | Primary gap driver |
|---|---|---|
| USD 34.76 B (2025) | Mordor Intelligence | - |
| USD 41.37 B (2025) | Global Consultancy A | Includes platform and managed-service revenue |
| USD 41.17 B (2025) | Trade Journal B | Bundles full deep-learning stacks at list prices |
| USD 26.02 B (2025) | Industry Study C | Extrapolates from 2016 base, omits cloud-native tools |
The comparison shows that Mordor's disciplined scope choices, yearly refresh, and balanced variable set deliver a transparent, repeatable baseline that decision-makers can trust.
Key Questions Answered in the Report
What is the neural network software market’s current value and growth outlook?
The market was valued at USD 34.76 billion in 2025 and is forecast to reach USD 139.86 billion by 2030, advancing at a 32.1% CAGR.
Which region is expected to grow the fastest over the forecast period?
Asia-Pacific is projected to post the highest 35.7% CAGR through 2030, driven by national AI-cloud programs in China, Japan, India, and South Korea.
Which application segment is expanding most rapidly?
Predictive maintenance is the fastest-growing use case, with a 35.6% CAGR as manufacturers adopt neural networks to cut downtime and extend equipment life.
Why are service revenues rising faster than software license sales?
Enterprises require integration, tuning, and ongoing MLOps support, so professional and managed services are growing at 35.4% CAGR while core toolkits remain essential.
What key challenges could restrain market expansion?
Acute shortages of deep-learning MLOps talent and stringent data-privacy mandates increase deployment costs and lengthen implementation timelines.
How are companies coping with limited GPU availability?
Firms optimize models through quantization and pruning, adopt alternative hardware such as Intel Arc GPUs, and prioritize hybrid cloud deployments that balance cost with compute access.
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