Best Inference Providers for AI Agents: Low-Latency API Solutions

Inference providers offer managed APIs to access Large Language Models with high throughput and low latency, allowing agents to process tokens at near-human conversational speeds. Unlike self-hosting models on your own infrastructure, inference providers handle model serving, scaling, and infrastructure management. You send a request, the provider runs the model on specialized hardware, and returns the generated response. For AI agents making hundreds or thousands of API calls per day, provider choice directly affects cost, latency, and reliability.

Why agents need fast inference:

• Conversational responsiveness: Sub-second first-token latency keeps interactions natural
• High-frequency decision making: Agents calling LLMs in tight loops need low per-call overhead
• Cost savings: Serverless inference can reduce hosting costs by up to 90% versus provisioned instances
• Scalability: Handle traffic spikes without managing infrastructure

The market divides into two camps: general-purpose providers (OpenAI, Anthropic) focus on model quality, while specialized inference providers (Groq, Cerebras) focus on speed and cost for open-weight models.

We assessed providers across five dimensions:

1. Throughput (Tokens Per Second) Output speed measures how fast the provider generates text after the first token. Agents processing large responses need high TPS. Benchmark standard: Llama 3.1 70B.

2. Time to First Token (TTFT) Latency from request to first response token. Low TTFT keeps agent workflows responsive. Providers optimized for real-time use cases (voice, live chat) prioritize this metric.

3. Cost Efficiency Price per million tokens (input and output). Agents making thousands of calls daily need predictable, low per-token costs.

4. Model Selection Range of open-weight and proprietary models. Agents benefit from flexibility to choose models by capability (reasoning, code, vision) and cost.

5. Developer Experience API reliability, documentation quality, and ease of integration. Agents need stable endpoints with clear error handling. We tested each provider with Llama 3.1 70B and GPT-OSS-120B benchmarks where available, and reviewed third-party performance data from Artificial Analysis and Helicone's LLM API comparison.

Cerebras uses wafer-scale computing with its Wafer Scale Engine (WSE), the largest chip ever built for AI workloads. This enables extreme parallelism and memory bandwidth.

Performance:

• 1,800 tokens per second for Llama 3.1 8B
• 450 tokens per second for Llama 3.1 70B
• 2,946 tokens per second for GPT-OSS-120B

Best for: High-volume synchronous tasks requiring maximum throughput. Batch processing, bulk content generation, or agents making parallel calls to the same model.

Limitations:

• Higher cost per token than serverless alternatives
• Optimized for throughput over latency, so TTFT may be higher than Groq

Pricing: Competitive on a per-token basis, but best value when you can use the extreme throughput. Cerebras dominates when you need to process massive volumes of tokens quickly.

Groq builds purpose-built hardware around its Language Processing Unit (LPU) designed specifically for running large language models at scale with predictable performance and low latency.

Performance:

• 241 tokens per second for Llama 2 Chat 70B (more than double other providers)
• Sub-100ms time to first token in many cases
• Groq achieved 800+ tokens per second on Llama 3 in public benchmarks

Best for: Ultra-low-latency streaming, real-time copilots, and high-frequency agent calls where every millisecond of response time counts. Voice agents and interactive chatbots see the biggest benefit.

Limitations:

• Smaller model catalog than general-purpose providers
• Throughput slightly lower than Cerebras on some models

Pricing: Cost-competitive for real-time workloads. Pay for speed where latency matters most. If your agent calls the LLM in a tight loop or needs real-time conversational responses, Groq delivers the fast experience.

Run Inference Providers For AI Agents Low Latency API Solutions workflows on Fast.io

Fast.io gives teams shared workspaces, MCP tools, and searchable file context to run best inference providers for ai agents workflows with reliable agent and human handoffs.

Try the Free Agent Tier

Together AI offers high-performance inference for 200+ open-source LLMs with sub-100ms latency, automated tuning, and horizontal scaling.

Performance:

• 911.5 tokens per second for GPT-OSS-120B
• Sub-100ms time to first token
• Automatically tuned inference engine per model

Best for: Teams needing access to the latest open-weight models (Llama, Mistral, Mixtral, etc.) without managing infrastructure. Strong choice for multi-model agent systems.

Strengths:

• Largest catalog of open-weight models
• New models added within days of release
• Flexible pricing (pay-as-you-go and reserved capacity)
• Strong community support

Limitations:

• Not as fast as Groq or Cerebras on raw throughput

Pricing: Competitive per-token pricing with volume discounts.

Fireworks AI provides a high-performance inference platform focused on low latency and strong reasoning performance for open-weight models. Their proprietary FireAttention inference engine powers text, image, and audio inferencing with 4x lower latency than other open-source LLM engines like vLLM.

Performance:

• 4x lower latency than vLLM
• Tuned kernels for structured output (JSON mode, function calling)
• Enterprise-grade reliability

Best for: Agents using structured output, function calling, or JSON mode. Routing agent logic and tool-use workflows to Fireworks delivers strong performance.

Strengths:

• Faster structured output generation
• Strong support for function calling
• Built for reasoning-heavy models
• Production-ready infrastructure

Limitations:

• Smaller model catalog than Together AI

Pricing: Premium pricing for enterprise reliability and performance.

DeepInfra offers practical speed and predictable costs built for developers. Their serverless inference platform prioritizes cost efficiency and reliability over peak performance.

Performance:

• Competitive tokens per second (not the fastest, but fast enough)
• Predictable latency
• Strong uptime

Best for: Background and bulk traffic where cost matters more than peak speed. Route non-latency-sensitive agent tasks (summarization, batch processing, embeddings) to DeepInfra for maximum cost savings.

Strengths:

• Lowest per-token pricing
• Serverless scaling
• Reliable API
• Good model selection

Strategy: Use DeepInfra for background work and Groq/Cerebras for latency-critical paths. Multi-provider routing optimizes cost and performance.

Pricing: Industry-leading cost efficiency. Ideal for high-volume agents.

SiliconFlow offers an all-in-one platform for both inference and deployment with exceptional speed. In recent benchmark tests, SiliconFlow delivered up to 2.3x faster inference speeds and 32% lower latency compared to leading AI cloud platforms.

Performance:

• 2.3x faster inference vs competitors
• 32% lower latency
• Combined inference and deployment tooling

Best for: Teams wanting a single platform for inference, fine-tuning, and deployment. Good choice for teams building custom models alongside using hosted inference.

Strengths:

• Fast performance
• Integrated deployment tools
• Growing model catalog

Limitations:

• Less proven at scale than established providers

Pricing: Competitive pricing with integrated tooling.

Hugging Face provides the largest model library and serves as a major hub for open-source model access. Their Inference API offers access to thousands of models.

Best for: Experimentation, prototyping, and accessing niche or newly-released models. Agents needing flexibility to test different models quickly.

Strengths:

• Unmatched model selection
• Community support
• Easy integration

Limitations:

• Slower inference than specialized providers
• Not optimized for production-scale agent workloads

Pricing: Free tier for experimentation, pay-as-you-go for production. Consider how this fits into your broader workflow and what matters most for your team. The right choice depends on your specific requirements: file types, team size, security needs, and how you collaborate with external partners. Testing with a free account is the fast way to know if a tool works for you.

Performance metrics sourced from provider documentation and third-party benchmarks including Artificial Analysis, Helicone, and provider-published benchmarks.

The best agent systems don't rely on a single provider. Route traffic based on task requirements:

Latency-Critical (Voice, Chat, Real-Time): Route to Groq or Cerebras. Pay for speed where responsiveness matters.

Structured Output (Function Calling, JSON Mode): Route to Fireworks AI. Optimized kernels deliver faster structured generation.

Background Processing (Summarization, Batch): Route to DeepInfra. Cost efficiency wins for non-urgent tasks.

Experimentation (Testing New Models): Route to Hugging Face or Together AI. Fast access to new releases. This multi-provider approach optimizes cost and performance across different agent workflows. Implement routing logic at the application layer using simple conditional rules based on task type.

Inference providers handle model execution, but agents also need persistent file storage for:

• Input data: Documents, images, and files to process
• Output artifacts: Generated reports, images, and structured data
• Knowledge bases: RAG pipelines require storage for source documents
• Multi-agent coordination: Shared file access between agents

Fast.io offers cloud storage built specifically for AI agents:

• 50GB free storage (no credit card required)
• 251 MCP tools via Streamable HTTP and SSE transport
• Built-in RAG with Intelligence Mode (auto-indexes files, semantic search, AI chat with citations)
• Ownership transfer (agent builds, then transfers to human)
• Works with any LLM (Claude, GPT-4, Gemini, LLaMA, local models)

Agents get their own accounts and workspaces just like human users. Combine fast inference (Groq, Cerebras, etc.) with persistent storage (Fast.io) for complete agent workflows. Learn more at Fast.io's Agent Storage page.