LLM routing can cost more than not routing at all

Unlike traditional inference, routing can lead to increased costs that may outweigh the benefits. During traffic spikes, capacity remains provisioned even when no longer needed, resulting in the payment for idle infrastructure built for peak demand. One simple API request can rapidly expand into routing, retries, caching, observability, rate limits, and cost controls.

This creates a complex internal inference stack where model APIs, despite appearing interchangeable, often have different schemas, latency profiles, safety layers, and pricing behavior. Switching providers isn't just a matter of swapping endpoints; it involves reworking routing, evaluation, and application logic. Payment is based on per-token usage, with no GPU contracts or minimums.

Forecasting inference costs should be similar to forecasting AWS bills. To optimize costs, consider batching at around half of real-time usage and leveraging off-peak dynamic pricing for MiniMax M2.5 and Kimi K2.5. According to recent statistics, 80% of AI customer ARR now comes from inference and core cloud services, rather than bare metal.

Serverless Inference supports an array of models, delivers 230 tok/sec on DeepSeek V3.2 (3.9× faster than AWS Bedrock), and processes over 180M patient interactions per day at 400ms in production. DigitalOcean offers open-weight models, including DeepSeek, Qwen, Llama, Mixtral, Phi, and gpt-oss, and provides features like LoRA on Serverless, full BYOM on Dedicated, and no proprietary lock-in.

The platform includes five integrated layers: compute, network, storage, data, and AI, and remains open at each layer. DigitalOcean's API supports message handling for agentic workflows compatible with Claude Code, and provides drop-in OpenAI and Anthropic schemas for seamless migration. Additionally, it offers multimodal capabilities, including image (Stable Diffusion 3.5), video (Wan 2.2), speech (Qwen3 TTS), and vision-language (Nemotron and Kimi), all under the same API and billing structure.

For healthcare AI, DigitalOcean prioritizes reliability, access to the latest hardware, and efficient scaling to ensure patient experience remains unaffected. Serverless Inference automatically scales capacity based on demand, eliminating the need for manual GPU management or peak traffic planning. Key features include built-in observability and control primitives to manage production workloads without external tooling, such as metrics for latency, tokens, errors, and spend, as well as built-in controls for rate limits and usage tracking.

Teams can switch or experiment with models without altering application logic or rewriting integrations. DigitalOcean provides a unified API for signing up with an email or GitHub account, generating an API key in one click, and setting up compatible SDKs within minutes. The platform supports pay-per-token pricing, which is applied once the included usage is exceeded.