End-to-end inference with automatic scaling, streaming tokens, and real-time monitoring.
Choose the optimal runtime for your workload. From elastic API endpoints to bare-metal isolated instances.
| Compare by | 01 / ServerlessServerless InferencingAUTO_SCALING | 02 / DedicatedDedicated DeploymentISOLATED_RUNTIME |
|---|---|---|
| Workload fit | Serverless endpoints are optimized for rapid deployment and elastic workloads. Fully abstracted infrastructure with automatic scaling. | Used when workload predictability, isolation, or sustained throughput becomes critical. Single-tenant GPU allocation. |
| Execution | Multi-tenant optimized runtime | Single-tenant isolated runtime |
| Infrastructure | Fully abstracted | Dedicated GPU allocation |
| Control | Parameter-level tuning | Infra + runtime control |
| Scaling | Automatic, workload-driven | Cluster-level scaling |
| Use Case | Dynamic workloads, API products | Stable high-throughput systems |
| Get started |
A fast, efficient multimodal AI model designed for coding, reasoning, AI agents, automation, and image/video understanding. Supports 262K tokens natively, extendable to 1M tokens with long-context configuration.
Qwen/Qwen3.8-Flash-NextA powerful reasoning model optimized for complex coding, autonomous AI agents, software engineering, and long-running workflows. Supports a 1M-token context window and up to 128K output tokens.
zai-org/GLM-5.3A powerful, efficient 27B-parameter multimodal model optimized for coding, complex reasoning, AI agents, research, and professional workflows. Supports text, image, and video understanding, function calling, and adjustable thinking modes.
Qwen/Qwen3.8-27BOur hardware-specific LLMs help agents write better kernels. We test the changes and return verified improvements to the inference service.
Runtime performance measurements show where execution can improve. Our optimization agents do not inspect customer prompts or completions.
Meridian, our NVIDIA-specific LLM, powers AI agents that write and refine CUDA kernels. The fully automated workflow tests correctness and execution performance, then deploys verified improvements to our inference service.
Verified improvements return to serving. Kernel code and measured results guide the next cycle, not customer content.
Regional GPU capacity, redundant infrastructure, and direct engineering ownership support production inference under sustained load.
Infrastructure and MLOps work together, from failure detection to recovery. Incidents go directly to the engineers responsible for the runtime.
Redundant power, network, and hardware underpin the serving infrastructure.
Failure detection and automated failover support continuous operation across locations.
Engineers receive incident alerts directly. Infrastructure and MLOps coordinate recovery and runtime tuning.
Deploy production-ready AI models without the compliance headache. Geodd provides high-performance, GDPR-ready inference designed to eliminate unnecessary data exposure for products serving the EU, EEA, and UK.
We never store your API prompts, completions, request bodies, or customer datasets.
Your data is strictly yours. It is never used for model training or human-in-the-loop screening.
Route and process your inference workloads entirely within EU-based data center infrastructure.
Hardened with encryption at rest and in transit (TLS), role-based access controls, and a comprehensive DPA framework.
We act strictly as a Data Processor for your API data, keeping your internal workflows, product logic, and customer messages safe by default.
Build on Geodd's hyper-optimized inference stack. Get instant API access to the world's most capable open-source models or talk to our team for custom deployments.