====== Inference Providers Comparison ======

AI inference providers are services that host and serve AI models via APIs, handling the compute infrastructure required to generate model responses. In 2026, the market splits between frontier model providers offering proprietary models and specialized inference providers optimizing for speed, cost, or model breadth with open-source models.((Source: [[https://infrabase.ai/blog/ai-inference-api-providers-compared|Infrabase AI Inference API Providers Compared 2026]]))

===== Market Landscape =====

The inference provider market has two tiers:

  * **Frontier model providers**: OpenAI, Anthropic, Google, and cloud platforms (AWS Bedrock, Azure OpenAI) offering proprietary models through polished APIs
  * **Specialized inference providers**: Groq, Together AI, Fireworks AI, DeepInfra, Cerebras, SambaNova, and others optimizing open-source model serving for speed or cost

Most specialized providers expose OpenAI-compatible API endpoints, enabling easy provider switching with a single ''baseURL'' change.((Source: [[https://infrabase.ai/blog/ai-inference-api-providers-compared|Infrabase AI Inference API Providers Compared 2026]]))

===== Frontier Model Pricing =====

^ Provider ^ Model ^ Input (per 1M tokens) ^ Output (per 1M tokens) ^ Context Window ^
| **Google** | Gemini 3.1 Pro | $2 | $12 | 2M tokens |
| **Anthropic** | Claude Opus 4.6 | Premium pricing | Premium pricing | 1M tokens |
| **OpenAI** | GPT-5.4 | Premium pricing | Premium pricing | 128K-200K tokens |
| **Anthropic** | Claude Sonnet 4.6 | Competitive | Competitive | 200K tokens |

Gemini 3.1 Pro offers the best value among frontier models at approximately 7x cheaper than Claude Opus 4.6, while scoring highest on reasoning benchmarks (94.3% GPQA Diamond).((Source: [[https://aitoolbriefing.com/comparisons/gpt-5-4-vs-gemini-3-1-pro-vs-claude-opus-4-6-march-2026/|AI Tool Briefing March 2026 Flagship Comparison]]))

===== Specialized Provider Comparison =====

^ Provider ^ Hardware ^ Speed ^ Pricing (per 1M tokens) ^ Model Catalog ^ Best For ^
| **Groq** | Custom LPU chips | 400-800 tok/s; sub-100ms TTFT | Higher per-token than GPU providers | Smaller catalog | Latency-critical apps (chatbots, interactive agents) |
| **Together AI** | GPUs | Competitive | $0.05-$0.90 (80-90% cheaper than OpenAI) | 100+ models | Inference + fine-tuning workflows |
| **Fireworks AI** | GPUs with FireAttention engine | 12x faster long-context vs vLLM | Competitive | Broad catalog | Production workloads with SLAs |
| **DeepInfra** | H100/A100 GPUs | ~0.6s avg latency | Most competitive for open-source | Llama 3.1, DeepSeek V3, broad | Cost-optimized high-volume inference |
| **Cerebras** | Custom wafer-scale chips | Ultra-fast inference | Competitive | Growing catalog | Speed-critical large model inference |
| **SambaNova** | Custom RDU chips | 2.3x faster, 32% lower latency | Competitive | Growing | High-performance deployments |

Groq's custom LPU (Language Processing Unit) hardware delivers 400-800 tokens per second for models like Llama 3, which is 5-10x faster than OpenAI's API.((Source: [[https://www.pkgpulse.com/blog/groq-vs-together-ai-vs-fireworks-ai-llm-inference-apis-2026|PkgPulse Groq vs Together AI vs Fireworks AI 2026]])) The tradeoff is a smaller model catalog and higher per-token pricing compared to GPU-based providers.

===== Speed vs Cost Tradeoff =====

Same model, different provider, wildly different performance. The hosting infrastructure shapes cost, speed, and throughput more than the model itself.((Source: [[https://machinelearningplus.com/gen-ai/inference-providers-benchmark/|Machine Learning Plus Inference Providers Benchmark]]))

Key findings from benchmarks:
  * **Groq** leads on raw speed (sub-100ms time-to-first-token) but has higher per-token costs
  * **DeepInfra** and **Together AI** offer the best cost-to-performance ratio for most workloads
  * **Fireworks AI** excels at long-context inference (12x faster than vLLM) with production-grade SLAs
  * **SiliconFlow** reports 2.3x faster speeds and 32% lower latency than competitors((Source: [[https://www.siliconflow.com/articles/en/the-top-inference-acceleration-platforms|SiliconFlow Inference Acceleration]]))

===== Cloud Platform Providers =====

^ Provider ^ Key Features ^ Pricing Model ^ Best For ^
| **AWS Bedrock** | RAG via Knowledge Bases, multi-model access, 50% batch discounts | Per-token with batch options | Enterprise with AWS infrastructure |
| **Azure OpenAI** | Enterprise OpenAI integration, compliance frameworks | Per-token | Microsoft ecosystem enterprises |
| **Google Vertex AI** | Native Gemini, BigQuery integration, AutoML | Consumption-based | GCP ecosystem enterprises |

AWS Bedrock offers a 50% discount for batch mode processing, making it attractive for non-real-time workloads.((Source: [[https://futureagi.substack.com/p/top-11-llm-api-providers-in-2026|FutureAGI Top LLM API Providers 2026]]))

===== Special Features =====

  * **Fireworks AI**: SOC 2 Type II and HIPAA compliance; multi-cloud GPU orchestration across 15+ regions; $1 free credits for new accounts((Source: [[https://futureagi.substack.com/p/top-11-llm-api-providers-in-2026|FutureAGI Top LLM API Providers 2026]]))
  * **Together AI**: Fine-tuning alongside inference on a single platform; broadest open-source model support((Source: [[https://infrabase.ai/blog/ai-inference-api-providers-compared|Infrabase AI Inference API Providers Compared 2026]]))
  * **Groq**: Free tier with 14,400 requests/day for prototyping((Source: [[https://www.pkgpulse.com/blog/groq-vs-together-ai-vs-fireworks-ai-llm-inference-apis-2026|PkgPulse Groq vs Together AI vs Fireworks AI 2026]]))
  * **RunPod/Modal**: Raw GPU access for custom model deployment with full stack control
  * **Replicate**: Multi-modal and open-source model focus with simple deployment

===== Choosing a Provider =====

^ Use Case ^ Recommended Provider(s) ^
| Latency-critical chatbots | Groq (fastest TTFT) |
| Cost-optimized high-volume | DeepInfra, Together AI |
| Combined inference + fine-tuning | Together AI, Fireworks AI |
| Enterprise compliance | AWS Bedrock, Fireworks AI |
| Best frontier model value | Google Gemini API |
| Best frontier model quality (coding) | Anthropic Claude API |
| Custom model deployment | RunPod, Modal, Baseten |
| Prototyping (free tier) | Groq (14,400 req/day free) |

===== Pricing Trends =====

Inference pricing has undergone a 280-fold collapse from 2022 to 2024, from $20 per million tokens to $0.07 per million tokens at GPT-3.5 performance levels.((Source: [[https://www.aboutchromebooks.com/machine-learning-model-training-cost-statistics/|ML Model Training Cost Statistics]])) This trend continues as specialized hardware, quantization, and competitive pressure drive costs down further. Open-source model inference through providers like DeepInfra and Together AI can be 80-90% cheaper than equivalent proprietary model APIs.

===== See Also =====

  * [[foundation_model_economics|Foundation Model Economics]]
  * [[coding_agents_comparison_2026|Coding Agents Comparison 2026]]
  * [[deep_research_comparison|Deep Research Comparison]]

===== References =====