AI Inference Guide
Inference Providers
AI Inference Service Providers
Compare leading AI inference providers for cost, performance, and features. Choose the right provider based on your specific needs for latency, cost, and model availability.
Local / On-Device Providers
Run supported models on hardware you control to reduce third-party data transfer and support some offline workflows. Audit telemetry, storage, model licences, hardware requirements, and security before using local execution for sensitive data.
Key Features
Key Features
Key Features
Key Features
Cloud Inference Providers
Managed inference services can provide API access, capacity management, and operational tooling. Availability, regions, data handling, rate limits, model versions, and prices change frequently, so the cards link to live provider information instead of freezing a dated price snapshot here.
Key Features
Key Features
Google AI Studio
Gemini API tooling for multimodal applications, structured output, and function calling
Key Features
Key Features
Key Features
Key Features
Key Features
Key Features
Key Features
Key Features
Key Features
Key Features
Deployment Comparison
On Device Benefits
Can reduce third-party transfers when telemetry and remote fallbacks are disabled
No per-token API bill, but hardware, energy, support, and licence costs remain
Can work offline after required assets are cached and remote fallbacks are disabled
Cloud Benefits
Managed capacity and a provider-maintained model catalog
Can scale within quotas, regional capacity, and account limits
Provider manages serving hardware; you still own integration, evaluation, and incident planning
Benchmark providers on your workload
Vendor-to-vendor numbers are only comparable when model version, region, prompt/output length, concurrency, warm-up, retries, and measurement window are held constant. Record at least these dimensions in a time-stamped evaluation.
| Dimension | Measure | Why it matters |
|---|---|---|
| Responsiveness | TTFT p50/p95 and end-to-end latency | Averages conceal slow-tail user experiences |
| Throughput | Output tokens/second at fixed concurrency | Shows how performance changes under expected load |
| Quality | Task success on a versioned evaluation set | Fast output is not useful if it fails the task |
| Total cost | Input, output, search/tool, cache, and retry cost | Headline token prices omit material workload costs |
| Risk and operations | Retention, residency, SLA, quotas, fallbacks | Determines whether the deployment can meet real obligations |