Serverless vs. Self-hosted LLM inference
When building applications with LLMs, you typically have two main infrastructure choices: serverless (managed) services or self-hosted solutions. Each offers distinct advantages and trade-offs in terms of ease of use, customization, scalability, and compliance.
Serverless LLM inference
Serverless inference services, provided by companies like OpenAI, Anthropic, and other hosted API providers, simplify application development significantly. Key advantages include:
- Ease of use: You can get started quickly with minimal setup — just use an API key and a few lines of code. There is no need to manage hardware, software environments, or complex scaling logic.
- Rapid prototyping: It is perfect for testing ideas quickly, building demos, or internal tooling without infrastructure overhead.
- Hardware abstraction: Self-hosting LLMs at scale usually requires high-end GPUs (such as NVIDIA A100 or H100). Serverless APIs abstract these hardware complexities, allowing you to avoid GPU shortages, quota limits, and provisioning delays.
Self-hosted LLM inference
Self-hosted LLM inference means deploying and managing your own LLM infrastructure. It provides significant control and flexibility, critical for enterprises to build long-term competitive advantage.
Key benefits of self-hosting include:
- Data privacy and compliance: LLMs are widely used in modern applications like RAG and AI agents. These systems often require frequent access to sensitive data (e.g., customer details, medical records, financial information). This is often not an acceptable option for organizations in regulated industries with compliance and privacy requirements. Self-hosting LLMs makes sure your data always stays within your secure environment.
- Advanced customization and optimization: With self-hosting, you can tailor your inference process to meet specific needs, such as:
- Adjusting latency and throughput trade-offs precisely.
- Implementing advanced optimizations like prefill-decode disaggregation, prefix caching, KV cache-aware routing.
- Optimizing for long contexts or batch-processing scenarios.
- Enforcing structured decoding to ensure outputs follow strict schemas
- Fine-tuning models using proprietary data to achieve competitive advantages.
- Predictable performance and control: When you self-host your LLMs, you have complete control over how your system behaves and performs. You’re not at the mercy of external API rate limits or sudden policy changes that might impact your application’s performance and availability.
Comparison summary
Choosing between serverless and self-hosted LLM inference depends on your specific needs regarding ease of use, data privacy, performance optimization, and control.
| Item | Serverless APIs | Self-hosted inference |
|---|---|---|
| Ease of Use | ✅ High (simple API calls) | ⚠️ Lower (requires LLM deployment and maintenance) |
| Data Privacy & Compliance | ⚠️ Limited | ✅ Full control |
| Customization | ⚠️ Limited | ✅ Full flexibility |
| Cost at Scale | ⚠️ Higher (usage-based, may rise significantly) | ✅ Potentially lower (predictable, optimized infrastructure) |
| Hardware Management | ✅ Abstracted away | ⚠️ Requires GPU setup & maintenance |
Compared with self-hosted inference, serverless model APIs make it hard to get fine-grained control over performance tuning and cost optimization. You are just calling the same API as everyone else. To ship a production system with competitive edges, you need to own your inference layer.
For more information, see the blog post Serverless vs. Dedicated LLM Deployments: A Cost-Benefit Analysis.
At Bento, we work to help enterprises self-host any open and custom LLMs with flexible distributed architecture and tailored inference optimization. With Bento Inference Platform, you can achieve up to 6x lower cost than serverless APIs.