AI Inference Service | Geodd AI
Inference as a Service

Production
Inference Stack

End-to-end inference with automatic scaling, streaming tokens, and real-time monitoring.

Infrastructure

Deployment Modes

Choose the optimal runtime for your workload. From elastic API endpoints to bare-metal isolated instances.

Serverless
AUTO_SCALING

Serverless
Inferencing

Serverless endpoints are optimized for rapid deployment and elastic workloads. Fully abstracted infrastructure with automatic scaling.

Execution
Multi-tenant optimized runtime
Infrastructure
Fully abstracted
Control
Parameter-level tuning
Scaling
Automatic, workload-driven
Use Case
Dynamic workloads, API products
Dedicated
ISOLATED_RUNTIME

Dedicated
Deployment

Used when workload predictability, isolation, or sustained throughput becomes critical. Single-tenant GPU allocation.

Execution
Single-tenant isolated runtime
Infrastructure
Dedicated GPU allocation
Control
Infra + runtime control
Scaling
Cluster-level scaling
Use Case
Stable high-throughput systems
Available Models

Optimized Models with Predictable Pricing

Production-ready inference endpoints (not raw weights), optimized for:

  • Stable concurrency
  • Consistent latency
  • Efficient memory usage
Explore Full Library
Technical Stack

Architecture Layers

A vertically integrated stack designed for maximum throughput and deterministic latency.

Runtime Behavior Under Load

Performance is defined by stability under concurrency, not single-request benchmarks. Token generation remains consistent across sessions due to scheduler and execution-layer control.

  • STABLE PERFORMANCE AT 32+ CONCURRENT REQUESTS
  • THROUGHPUT INCREASE: 25–50%
  • LATENCY REDUCTION: 20–30%
  • TIME-TO-FIRST-TOKEN REDUCTION: 30–50%
  • LATENCY DISTRIBUTION CONTROLLED AT P99 LEVEL

Controlled through:

Adaptive batchingLatency-aware schedulingMemory pre-allocation
geodd-cli — benchmark
$ geodd-cli benchmark --mode concurrency --requests 32
[INFO] Initializing benchmark environment...
[INFO] Warming up model cache...
[BENCHMARK] Starting concurrency stress test
[INFO] Request 1-8 ............... OK
[INFO] Request 9-16 .............. OK
[INFO] Request 17-24 ............. OK
[INFO] Request 25-32 ............. OK
Summary Statistics:
Throughput Increase: 25–50%
Latency Reduction: 20–30%
TTFT Reduction: 30–50%
P99 Latency: STABLE
[SUCCESS] Benchmark complete.
[ACHIEVED] Stable at 32+ concurrent requests
Session: perf-bench-001
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Infrastructure

Infrastructure Design and Failure Handling

Infrastructure is designed for continuous operation. System behavior is designed to remain stable under sustained load, not just peak benchmarks.

99.99%

Observed Uptime

System uptime across multi-location deployment with failover mechanisms.

Direct

Failure Response

Engineers alerted directly with immediate response. Infra + MLOps act together.

500+

Nvidia GPUs

High-performance GPU fleet dedicated to inference workloads across multiple regions.

99.99%

Observed Uptime

Tier III datacenter infrastructure with redundant power, network, and hardware.

Tier III Datacenter

Redundant power, network, and hardware with multi-location deployment and automated failover mechanisms.

III
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