AI: Local Server, Cloud or API? A Complete Guide for Your Project
Introduction
In a context where artificial intelligence (AI) is emerging as a major driver of innovation, the question of hosting and deploying its models is becoming crucial. Companies have many options, whether it's leveraging an API offered by a service provider, opting for a cloud solution (AWS, Google Cloud, Azure, OVH, etc.), or hosting their models locally on their own servers.
The challenge isn't just technical: it involves assessing costs, internal skills, privacy and security, and the ability to scale resources based on demand. Working with a cloud provider can offer ease of deployment and scalability, but at a cost and with a form of dependency. Relying on a local server allows you to maintain full control of the infrastructure, but requires significant hardware and human investments. As for using third-party APIs, it can meet immediate needs without requiring strong internal skills, while raising the question of data confidentiality.
This guide aims to clarify these different approaches and provide an overview of the key criteria that guide decision-making. The goal is to enable stakeholders across all sectors—startups, SMEs, and large corporations—to identify the solution best suited to their constraints, whether related to cost, performance, security, or regulatory compliance.
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Understanding the different approaches
To effectively choose between API, Cloud or local server, it is necessary to clearly distinguish the specificities of each of these options.
1) AI model via API
Using an API involves leveraging an existing service, already trained and maintained by a service provider. You access the AI by sending your requests to an external access point; the service provider handles the calculations and returns the response. This method is characterized by ease of integration and pay-as-you-go pricing, but can limit customization and raise data sovereignty issues.
2) Hosting on a Cloud platform
Deploying an AI model on AWS, Google Cloud, Microsoft Azure, or OVH provides highly scalable infrastructure, managed services (databases, monitoring, backups), and advanced flexibility. The cloud frees the company from hardware management and greatly simplifies the deployment of new instances. However, it can lead to high recurring costs in the event of high demand and impose technological dependency on a provider.
3) Local or on-premise server
By installing and running its AI models in its own data center or on dedicated servers, the company retains complete control over its data and infrastructure. This option offers guaranteed confidentiality and customization, but requires an investment in hardware, a skilled team (DevOps, MLOps), and ongoing maintenance. Initial costs can be high, while computing capacity must be planned for to handle potential load peaks.
The main criteria for choosing and differentiating approaches
The choice between integrating an AI model via an API, a cloud platform, or a local server is based on several fundamental criteria that determine both the solution's performance and its sustainability. Understanding these criteria allows you to clearly distinguish the advantages and disadvantages of each of these approaches.
The first criterion concerns the nature of costs and their predictability . By opting for an API, the company benefits from a pay-per-use model: the initial investment remains low, but the bill can increase as the activity grows. The Cloud platform often adopts the same principle of billing per use, enhanced with automatic scalability options. This flexibility proves invaluable for absorbing occasional peaks in load or rapid growth. Conversely, hosting on local servers (on-premise) requires a significant initial hardware investment (CAPEX), but operational costs (OPEX) can stabilize over time, especially if the organization already has a suitable infrastructure.
The second criterion concerns the availability of internal skills . APIs are the most accessible solution for limited technical teams, as most of the complexity is managed by the service provider: training, maintenance, and updating the model. Cloud platforms require more know-how, particularly for configuring services (security, storage, scaling, monitoring). Finally, on-premise hosting requires solid expertise in system administration, GPU server management, and MLOps, as all tasks—from hardware provisioning to data security —rely on internal teams.
The third criterion involves confidentiality and regulatory compliance . In certain sectors (banking, healthcare, defense), the sensitivity of the data requires strict protection measures. APIs are not very suitable in these cases, since the data necessarily passes through external servers. The Cloud, although it offers advanced security and encryption tools, also raises issues of data sovereignty and data center location. On-premise hosting, thanks to its complete control over the environment, provides unparalleled control, but at the cost of increased operational complexity.
Finally, flexibility and performance are crucial differentiators. Cloud and API solutions can respond very quickly to increased demand without requiring hardware modifications. This responsiveness is a major asset for projects experiencing peak activity or rapid growth. On-premises servers, on the other hand, offer the ability to highly customize the execution environment (GPU settings, network configuration, etc.) and minimize latency if the infrastructure is located closer to users. However, this customization requires greater resources and planning, as computing needs must be anticipated and the machine pool properly sized.
Thus, to clearly distinguish between the approaches, it is necessary to position the cursor on the following elements: short- and long-term cost structure, the availability of internal skills, data sensitivity, and scalability. The challenge is not simply to choose the most modern solution, but to find the optimal fit between the company's operational constraints, budget management , and its strategic ambitions in terms of AI.
Summary table of major models and APIs
Here's a summary table of some major models and APIs, including costs (when publicly available), availability, and key features. Prices are approximate and subject to change by vendors.
| Supplier / Model | Kind | Availability / API | Price | Key Features |
|---|---|---|---|---|
| OpenAI - GPT-3.5 (Turbo) | Language model (NLP) | Public API (HTTP request) | - Entry : 0.0015 USD / 1,000 tokens - Output : 0.002 USD / 1,000 tokens |
- Excellent for text generation, conversation, summarization, translation, etc. - Large ecosystem of tools and libraries - Pay-per-use billing (tokens processed). |
| OpenAI - GPT-4 (8K context) | Language model (NLP) | Public API (paid access, waiting list or extended access depending on account) | - Entry : 0.03 USD / 1,000 tokens - Output : 0.06 USD / 1,000 tokens |
- Better contextual understanding and accuracy than GPT-3.5 - Ideal for applications requiring a high level of analysis (advanced chatbots, etc.) - Significantly higher costs than GPT-3.5. |
| OpenAI - GPT-4 (32K context) | Language model (NLP) | Public API (restricted access, similar to GPT-4 8K) | - Entry : 0.06 USD / 1,000 tokens - Output : 0.12 USD / 1,000 tokens |
- Extended context up to 32K tokens - Allows you to process or generate very long texts - Potentially significant bill for massive uses. |
| Anthropic - Claude 2 | Language model (NLP) | Public API (registration required, command line usage or via SDK) | - Prompt : 1.63 USD / million tokens (~0.00163 USD / 1,000 tokens) - Answer : 5.51 USD / million tokens (~0.00551 USD/1,000 tokens) |
- Highly efficient in text comprehension and generation - “Conversational assistant” oriented - Good prices for moderate use, may increase if the text output is large. |
| Google - PaLM 2 (Text-Bison) | Language model (NLP) | Via Google Cloud Vertex AI (paid API), or use through the Vertex AI UI | - Entry : 0.0005 USD / 1,000 characters (~0.002 USD / 1,000 tokens) - Output : 0.0010 USD / 1,000 characters (~0.004 USD / 1,000 tokens) |
- Integrated into the Google Cloud (GCP) ecosystem - Good for text generation, contextual analysis, translation, etc. - Billing per request based on character volume (approx. 1 token ≈ 4 characters). |
| Mistral AI (Mistral 7B) | Open source model (NLP) | Downloadable template (GitHub, Hugging Face); no official proprietary API at launch (Oct 2023) | - Free if self-hosted (no license cost) - Infrastructure costs (GPU, Cloud) to be expected if you host it yourself |
- Open source 7 billion parameter model geared towards text generation and understanding - Can be deployed on local server or Cloud (e.g. Docker container) - Full customization possible, but requires in-house skills for fine-tuning and inference. |
| Meta - Llama 2 | Open source model (NLP) | Downloadable (GitHub, Hugging Face) or accessed via third-party solutions (Hugging Face Inference, Azure, etc.) | - Free for research use or under license conditions - Some providers (Hugging Face, Azure) offer paid hosting |
- High-performance open source model (different sizes: 7B, 13B, 70B) - Special license for large-scale commercial use - Large community and support on GitHub, Hugging Face forum |
Comparison table of major Cloud offerings
Here is a comparison table of major cloud offerings (based on an Nvidia T4 GPU or equivalent) for hosting AI models. Prices are approximate and may vary by region , contract (on-demand, reserved, spot), and options (storage, bandwidth, etc.). The amounts indicated are based on standard "on-demand" usage (no commitment) and converted to US dollars, for information purposes only.
| Supplier | Instance / Range | GPU | vCPU / RAM | Estimated hourly rate (USD/h) | Monthly cost (~720 h) in USD | Comments |
|---|---|---|---|---|---|---|
| AWS | g4dn.xlarge (example) | 1×Nvidia T4 | 4 vCPU / 16 GB RAM | ~0.52 USD/h | ~375 USD/month | - Includes 125GB of local SSD storage - Ideal for inference or moderately sized AI workloads |
| GCP | n1-standard-8 + 1×T4 | 1×Nvidia T4 | 8 vCPU / 30 GB RAM | ~1.30 USD/h | ~935 USD/month | - Combination of VM cost + GPU cost - Separate billing for persistent storage and network traffic |
| Azure | NV T4 v3 (example) | 1×Nvidia T4 | 4 vCPU / 28 GB RAM | ~1.00–1.20 USD/h | ~720–865 USD/month | - Price range depending on Azure region - Possibility to reduce the cost with 1 or 3 year reservations |
| OVHcloud | GPU T4-60 (Public Cloud) | 1×Nvidia T4 | 8 vCPU / 60 GB RAM | ~1.20–1.40 USD/h | ~865–1,000 USD/month | - Dedicated AI offering with large memory capacities - Interesting for moderate-scale deep learning |
Investment for a local GPU server
Here's a table that illustrates the key elements to consider when purchasing and operating an on-premises server for AI projects. The figures are provided for informational purposes only and may vary depending on the vendor, region, and market fluctuations (GPU prices, etc.). The goal is to provide an idea of the investment and recurring costs.
| Level / Use | Typical specifications | Acquisition cost (USD) | Estimated recurring costs | Benefits | Constraints |
|---|---|---|---|---|---|
| 1) Small config / Workstation | - 1× consumer or semi-pro GPU (e.g. Nvidia RTX 3080/3090 or RTX A4000) - CPU: 8 to 16 cores - RAM: 32 to 64 GB - SSD: 1 TB - Power supply: ~750 W |
~3,000 to 6,000 USD | - Electricity: ~30 to 50 USD/month (moderate use) - “Artisan” maintenance (manufacturer’s warranty) |
- Low initial cost - Sufficient for prototyping or inferencing medium-sized models - Space-saving, can be integrated into an office |
- Limited training capacity for complex networks - Difficult scalability (little space to add other GPUs) - Sometimes noisy heat dissipation |
| 2) Average config / 1-2 GPU rack server | - 1 to 2× Nvidia T4 or RTX A5000 GPU - CPU: 16 to 32 cores (Intel Xeon / AMD EPYC) - RAM: 64 to 128 GB - SSD storage: 2 to 4 TB - 1U or 2U rack + suitable cooling |
~8,000 to 15,000 USD | - Electricity: ~50 to 100 USD/month (continuous use) - Maintenance: IT team, replacement of parts |
- Good compromise for training reasonably sized models - Easy to set up in a small data center or server room - Better reliability than a workstation |
- Larger initial investment - Remains limited in GPU if you want to quickly train very large models - Need for continuous air conditioning in the room or premises |
| 3) Advanced Config / Multi-GPU Server (2-4 GPUs) | - 2 to 4× Nvidia A100 / RTX 6000 / T4 GPU - CPU: 32 to 64 cores - RAM: 128 to 512 GB - Storage: 4 to 8 TB (NVMe SSD) - 2U or 4U rack, redundant power supply |
~25,000 to 60,000 USD | - Electricity: 150 to 300 USD/month - Maintenance contracts: 5 to 10% of the price/year |
- Good power for training deep models (vision, NLP, etc.) - Robust, scalable infrastructure (additional GPU slots, RAM, etc.) - Full control over data |
- High entry cost - Requires a server room environment (cooling, inverters, etc.) - More complex maintenance (firmware, drivers, etc.) - Need for a competent internal team |
| 4) AI/Data Center Cluster (4+ GPUs per node) | - Multiple nodes with 4 to 8× Nvidia A100 / H100 GPUs each - CPU: 64+ cores per node - RAM: 512 GB to 1 TB - High-speed network (Infiniband or 25/40/100 GbE) - SAN / NAS storage bays |
> USD 100,000 (can climb to USD 500,000 and more, depending on the number of nodes) | - Electricity: several hundred to thousands USD/month - Dedicated staff (administration, security, etc.) - Premium support contracts to be expected |
- Massive computing capacity for large-scale deep learning - Possibility of distributing training loads - High resistance to failures via redundancy and virtualization |
- Very high initial and operational costs - Demanding infrastructure (air conditioning, electrical redundancy, dedicated space) - Requires a high level of expertise (MLOps, clusters, containers, orchestration) |
Key points
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Investment (CAPEX) vs. operational costs (OPEX)
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On a small server (or workstation), the initial cost remains moderate (<10,000 USD), but the computing capacity is limited.
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As soon as you aim for larger configurations (multi-GPU, cluster), the bill quickly climbs (from tens to several hundreds of thousands of euros/dollars).
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Electricity and cooling
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GPUs consume a lot of power (up to 300W or more per GPU).
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The monthly cost of electricity and air conditioning can become significant, especially if the server is running 24/7.
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Maintenance and upgrades
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Regular replacement of parts (fans, disks, power supply).
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Software updates (GPU drivers, firmware, OS) and fault management (faulty RAM, overheating GPU, etc.).
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Internal skills
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An IT/MLOps team must manage the installation, configuration of frameworks (PyTorch, TensorFlow) and security.
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On large clusters, you also need to manage orchestration (Kubernetes, Slurm, etc.), monitoring and optimizations (GPU profiling).
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Depreciation and scalability
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To make an on-premise investment profitable, we aim for amortization over 3 to 5 years .
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Scalability can be tricky: you can add GPUs within certain limits (PCIe slots, sufficient power supplies, cooling), at the risk of having to quickly buy another complete server.
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Steps to making the right decision
1. Clearly define objectives
The first step is to identify the purpose of the AI project, whether it's image processing, text analysis, or prediction . It's crucial to clarify whether the solution is intended for sensitive use (personal data, regulated sector) or whether it simply needs to accelerate an existing functionality. This clarification already allows you to discern the confidentiality and compliance imperatives likely to guide the choice towards a local infrastructure or, on the contrary, to favor a Cloud solution.
2. Understanding the volume of data
Next, you need to estimate the amount of data to be processed, both for training and inference. The larger the volume, the more likely it is that your cloud bill will balloon. Conversely, a local server can quickly become saturated if the hardware resources (GPU, CPU, storage) are not properly sized. The volume and speed of data growth therefore directly influence the financial and technical viability of the chosen platform.
3. Analyze costs (CAPEX, OPEX)
Comparing initial investments (CAPEX) and operational costs (OPEX) over at least three to five years is an essential step. The cloud is attractive for avoiding large upfront costs, but can lead to high recurring expenses if the business grows. Conversely, an on-premise server requires a substantial budget upfront, the amortization of which can, however, prove advantageous in the event of intensive and long-term use.
4. Assess internal skills
Every solution requires a minimum level of expertise, but the scope of skills varies greatly. A cloud platform eliminates the need for hardware management, while an on-premises deployment requires a technically savvy team in system administration, MLOps, and security. In some cases, a lack of human resources naturally leads to the cloud or a third-party managed API.
5. Anticipate scalability
Before making a decision, it's essential to anticipate potential increases in traffic, data, or computing needs. The cloud simplifies scaling by allocating additional resources on demand. In contrast, a local server requires greater investment in physical infrastructure, including cooling and hosting space, to accommodate potential medium-term growth.
6. Carry out a proof of concept (POC)
A small-scale proof of concept helps assess the reliability, performance, and real-world cost of the proposed solution. Testing a pilot project on a public cloud or a smaller hardware configuration allows you to gather concrete data (latency, throughput, costs), and then adjust your implementation strategy accordingly.
7. Consider a hybrid model
When needs are complex, a tradeoff can arise between on-premises and cloud hosting. Sensitive data can remain on-premises, while peak computing or non-critical functionality migrates to an outsourced infrastructure. This approach requires fine-grained orchestration to synchronize environments, but it can optimize both costs and privacy.
8. Finalize the deployment strategy
Once the tests and trade-offs have been completed, it is possible to establish a detailed deployment plan: hardware configuration, selection of the Cloud provider, security measures, monitoring, etc. This roadmap must also anticipate future developments, whether it involves adding GPUs to a local cluster or reserving new instances in the Cloud, in order to maintain flexibility in the face of unforeseen events.
