Phi 4 API Pricing & Rate Card
Complete 2026 developer pricing guide for Phi 4. Benchmark token economics, context window limits, prompt caching discounts, and live inference latency across official provider endpoints.
| Billing Dimension | Standard Rate | Cached / Optimized Rate | Batch (50% Off) | Context Window |
|---|---|---|---|---|
| Input Prompt | $0.07 / M | $0.04 / M | 50% discount (24h SLA) | 16,384 tokens |
| Output Generation | $0.14 / M | Standard metered | 50% discount (24h SLA) | Up to max model limit |
| Tool / Function Calling | Standard input tokens | Eligible for prefix cache | Batch supported | Shared context budget |
Interactive Monthly Cost Estimator
Estimate your total bill based on prompt size, response volume, and request volume.
Small Language Model Efficiency: Microsoft's 14B Reasoning Powerhouse
Phi-4 is Microsoft's 14-billion parameter Small Language Model (SLM), engineered to deliver frontier-level mathematical and scientific reasoning without the astronomical compute overhead of 70B+ parameter models. Trained on synthetic reasoning datasets and curated textbook-quality corpora, Phi-4 achieves an astounding 84.4% on GSM8K and competitive scores against GPT-4o on STEM benchmarks.
Because of its 14B parameter size, Phi-4 can be self-hosted on a single NVIDIA A10G (24GB) or consumer RTX 4090 GPU in 4-bit quantization, delivering over 70 tokens/second at zero incremental token cost.
Phi-4 Cloud API & Self-Hosted Pricing Matrix
| Deployment Provider | Input Rate / 1M | Output Rate / 1M | Monthly Cost (100M Tokens) |
|---|---|---|---|
| Azure AI Studio Serverless | $0.15 / 1M | $0.60 / 1M | $37.50 / mo |
| RunPod Dedicated GPU (RTX 4090) | $0.34 / hr (Unlimited tokens) | $0.00 (Local compute) | $24.50 / mo (100 hrs run) |
| Ollama Local Workstation (Mac M2) | $0.00 (Local electricity) | $0.00 | $0.00 / mo |
Frequently Asked Questions
Related Frontier Showdowns & Hubs
Microsoft Phi-4 Architecture: 14B High-Density Reasoning & Edge Deployment TCO
Microsoft's Phi-4 is a 14-billion-parameter open model that achieves mathematical and logical reasoning scores exceeding earlier 70B models. Trained heavily on synthetic 'textbook-quality' data and curated multi-step reasoning curricula, Phi-4 delivers dense reasoning power in an exceptionally compact footprint. At 14B parameters, Phi-4 can be served in FP16 on a single NVIDIA A10G (24GB) or in 4-bit quantization on consumer hardware (12GB VRAM), making it a prime candidate for edge, on-device, and private enterprise VPC deployments.
| Hosting Option | Effective Input / 1M | Effective Output / 1M | VRAM Required | Throughput (Tokens/Sec) |
|---|---|---|---|---|
| Azure AI Studio (Pay-As-You-Go) | $0.14 / 1M | $0.56 / 1M | Managed Cloud Endpoint | Serverless auto-scaling |
| Self-Hosted vLLM (1x RTX 4080) | $0.00 (Fixed Hardware) | $0.00 | ~10.5 GB (4-bit AWQ) | 78 t/s (Single stream) |
| Self-Hosted vLLM (1x A10G 24GB) | $0.00 | $0.00 | ~16.0 GB (8-bit FP8) | 95 t/s |
Production Pipeline: Ollama & llama.cpp Local Docker Setup
Serving Phi-4 locally with Ollama in Docker with persistent volume storage and GPU offload:
# Run Ollama with Phi-4 on GPU
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
docker exec -it ollama ollama run phi4
# Query via standard OpenAI-compatible API format
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "phi4",
"messages": [{"role": "user", "content": "Explain raft leader election in Python."}]
}'Production Best Practices & Edge Guidelines
- Optimal for RAG Synthesis: Phi-4's high synthetic data grounding makes it remarkably resistant to hallucinations when summarizing retrieved RAG contexts compared to other 14B models.
- Quantization Quality Retention: Benchmarks prove that Q4_K_M quantization of Phi-4 retains 98.4% of its FP16 reasoning accuracy while cutting memory bandwidth requirements by half.
- Context Window Constraint: Phi-4 supports up to 16,384 tokens natively. For workflows requiring 100k+ tokens, route to Gemini 2.0 Flash or Llama 3.3 70B.
14B Parameter Edge Architecture & Quantized Production Footprint
Microsoft's Phi-4 represents a milestone in parameter-efficient synthetic data training, delivering mathematical reasoning and logic performance rivaling 70B parameter models within a compact 14B parameter footprint. From an infrastructure perspective, Phi-4 is tailored for cost-effective single-GPU deployments on edge servers, localized workstations, and containerized Kubernetes pods. In FP16 precision, Phi-4 requires 28 GB of VRAM, but when quantized to INT4 via GGUF or AWQ, its memory requirement plummets to just 9.5 GB, allowing it to run smoothly on consumer-grade NVIDIA RTX 4080 (16GB) or Apple Silicon M-series hardware.
Phi-4 Production Deployment Profiles & Hardware Requirements
| Format / Precision | Weight Footprint | Context Window | Minimum GPU / Target | Tokens / Sec (TPOT) |
|---|---|---|---|---|
| FP16 Native TensorRT-LLM | 28.5 GB | 16,384 tokens | 1x NVIDIA L40S (48GB) / A100 | 92 tokens/s |
| INT8 SmoothQuant | 15.2 GB | 16,384 tokens | 1x NVIDIA RTX 4090 (24GB) | 118 tokens/s |
| Q4_K_M GGUF (Ollama / vLLM) | 9.4 GB | 8,192 tokens | 1x Apple M3 Max / RTX 4070 | 64 tokens/s |
Production Ollama / Docker Local Inference Service
To deploy Phi-4 locally within an internal corporate microservice with zero egress fees, configure a Docker Compose runtime utilizing Ollama with pre-warmed weights:
services:
phi4-inference:
image: ollama/ollama:latest
container_name: phi4-inference-service
ports:
- "11434:11434"
volumes:
- ./ollama_data:/root/.ollama
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
environment:
- OLLAMA_NUM_PARALLEL=4
- OLLAMA_KEEP_ALIVE=24hPhi-4 Edge Cluster Scaling & Latency Guardrails
In edge Kubernetes deployments utilizing Triton Inference Server, Phi-4 INT8 processes reasoning prompts with a deterministic P95 latency under 220 milliseconds. When operating on air-gapped on-premise hardware, horizontal pod autoscalers (HPA) should scale replica counts based on GPU duty cycle metrics, maintaining individual card memory utilization below 85% to accommodate bursts in context length.
Phi-4 Triton Inference Dynamic Batching & Memory Pinning
When hosting Phi-4 via NVIDIA Triton Inference Server, enable dynamic batching with a maximum queue delay of 15 milliseconds. Memory pinning reduces host-to-device PCIe transfer overhead by 30%, ensuring sustained high tokens-per-second throughput across concurrent worker threads.