Designed for stable inference under sustained load, with controlled latency, efficient GPU usage, and direct engineering ownership.
The performance layer for
production-grade AI
Eliminate the inference bottleneck with our custom-tuned hardware-software stack. Precision engineered for the most demanding workloads.
50% Higher Throughput
Proprietary scheduling algorithms maximize GPU utilization across distributed clusters without overhead.
Faster Decoding
Optimized KV caching and continuous batching tailored for long-context generation tasks.
Stable p99 Latency
Isolate production workloads with dedicated compute paths and jitter-free inference pipelines.
Custom Models
Full support for LoRA adapters, quantization, and custom kernels at orchestrator level.
Accelerated
Execution
Custom CUDA plugins optimised to minimize memory bottlenecks and maximize FLOPs.
Precision Tuning
Intelligent FP8/FP4 weight quantization.
KV Cache Router
Routes requests by evaluating their computational costs across different workers
Disaggregated Serving
Where prefill and decode are handled by separate worker pools, boosting overall throughput.
Bare Metal Scale
Zero-virtualization overhead for GPU communication.
KV Cache Aware Routing
You get lower latency and higher throughput because you’re reusing cached attention states
Automated Fallback
Instantly handles worker failures gracefully during LLM text generation.
Serverless Inference
Multi-Regional
Deploy across 3 US regions, with 2 more continents coming soon.
SOC2 Type II Compliant
Enterprise-grade security and data isolation for all workloads.
Unified API
One SDK for both serverless inference and dedicated compute.
Built for Scale
Deploying high-density compute clusters across strategic global locations to eliminate the inference bottleneck.
Latest Updates
Developer-First Control
Fully compatible with the OpenAI SDK. Switch providers with a single line of code. No migration headaches, just immediate performance gains.
- Direct OpenAI SDK compatibility
- Real-time token usage and observability
- Privacy first with Zero Data Retention (ZDR) and logging policy
from openai import OpenAI # Switch to Geodd AI by changing base_url client = OpenAI( api_key="GEODD_API_KEY", base_url="https://api.geodd.ai/v1" ) completion = client.chat.completions.create( model="openai/gpt-oss-120b", messages=[ {"role": "user", "content": "What is machine learning?"} ] ) print(completion.choices[0].message)
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