Self-Hosting vs API for LLMs: Full Deployment Comparison

Two Paths to LLM Inference

Every team building AI-powered products faces the same fundamental choice: call a managed API like OpenAI or Anthropic, or run an open-weight model on dedicated GPU hardware. The right answer depends on your volume, latency requirements, data sensitivity, and operational capacity. This guide provides a concrete framework for making that decision, with real numbers from production deployments.

The economics have shifted dramatically. Open-weight models like Llama 3, Mistral, and DeepSeek now match or exceed GPT-3.5-level quality for most tasks, and self-hosted LLM deployments on modern GPUs deliver tokens at a fraction of API pricing. But cost is only one factor. Let’s examine each dimension.

Cost Comparison: Per-Token Economics

The cost gap between API and self-hosted inference widens rapidly with volume. At low volumes, APIs win on simplicity. At production scale, dedicated hardware wins on unit economics.

Use our GPU vs API cost comparison tool to calculate your specific breakeven point. For a deeper dive into the unit economics, see our analysis of cost per million tokens on GPU vs OpenAI. The LLM cost calculator lets you model different scenarios based on your actual usage patterns.

Performance and Latency

Self-hosted inference gives you dedicated resources with no noisy-neighbour effects. You control the queue depth, batching strategy, and can guarantee latency SLAs that APIs cannot.

For real-time applications like chatbots and voice agents, consistent sub-200ms TTFT matters. Our tokens-per-second benchmark shows what to expect from different GPU and model combinations.

Privacy, Compliance, and Control

Data privacy is often the deciding factor for regulated industries. With private AI hosting, your data never leaves your server:

• Data residency: Choose server location to meet GDPR, HIPAA, or SOC 2 requirements
• No training on your data: Open-weight models guarantee your prompts and outputs stay private
• Audit trail: Full logging and observability under your control
• Model customisation: Fine-tune on proprietary data without uploading it to a third party
• No vendor lock-in: Switch models or frameworks without rewriting your integration

API providers offer data processing agreements, but the data still transits their infrastructure. For healthcare, legal, financial, or government workloads, that may not be acceptable.

Operational Complexity

Self-hosting requires more operational investment. You are responsible for the serving stack, monitoring, updates, and failover. However, modern tooling has reduced this burden substantially.

Frameworks like vLLM and Ollama provide production-ready serving with minimal configuration. A typical deployment involves installing the framework, downloading the model weights, and starting the server — a process documented in our complete self-hosting guide.

The operational overhead breaks down as follows:

• Initial setup: 1-4 hours for a single-model deployment
• Ongoing maintenance: Model updates, driver patches, monitoring checks
• Scaling: Adding GPUs or servers as demand grows
• Failover: Load balancing across multiple servers for high availability

GigaGPU handles the hardware layer, so you focus on the application stack rather than racking servers and managing data centre logistics.

Decision Framework: When to Self-Host

Self-host when:

• You process more than 50M tokens per month
• Data privacy or compliance requirements prohibit third-party API calls
• You need guaranteed latency SLAs for real-time applications
• You want to fine-tune models on proprietary data
• You need to run multiple models or specialised pipelines (e.g., DeepSeek for reasoning plus Whisper for transcription)

Use APIs when:

• Volume is low and unpredictable
• You need access to frontier models not available as open weights
• Your team lacks any ML infrastructure experience and volume doesn’t justify the learning curve
• You are prototyping and need to move fast before committing to infrastructure

For a detailed cost breakdown across different GPU tiers, our guide to the cheapest GPUs for AI inference helps identify the most cost-effective hardware for your workload.

The Hybrid Approach

Many production systems use a hybrid architecture: self-hosted models handle the bulk of traffic for predictable, cost-effective inference, while API calls to frontier models handle edge cases that require maximum capability. This pattern gives you the cost benefits of self-hosting at volume while maintaining access to the most powerful models when needed.

A common pattern is routing requests by complexity: simple classification and extraction tasks go to a self-hosted Mistral or Llama deployment, while complex multi-step reasoning routes to a frontier API. The self-hosting breakeven analysis can help model this kind of tiered architecture.

Whether you start with full self-hosting or a hybrid approach, dedicated GPU infrastructure gives you the flexibility to optimise as your needs evolve.