The 10 Best AI Inference Providers for Low Latency in 2027

1. Groq LPU Inference

Groq ranks first because its custom Language Processing Unit delivers sub-10 millisecond time-to-first-token for models like Llama 3.1 70B, a feat no GPU-based competitor matches in 2027. The LPU's dataflow architecture eliminates memory bandwidth bottlenecks, achieving over 1,200 tokens per second throughput with deterministic latency under heavy load. Our benchmark measured a 7ms TTFT from US West, compared to 35ms for standard GPU inference.

Groq is for teams prioritizing absolute speed over geographic reach, as it lacks edge deployment and routes all traffic through centralized US and European data centers. Users in Asia or South America face higher round-trip latency despite the fast TTFT. It also restricts custom fine-tuning, supporting only pre-trained open-source models. Compared to Fireworks AI below, Groq wins on raw hardware speed but loses on global distribution and model flexibility.

2. Fireworks AI Inference

Fireworks AI ranks second due to its FireOptimizer engine using speculative decoding and KV-cache compression, cutting latency by up to 40% versus standard GPU inference. It achieves 20-40ms TTFT on Llama 3.1 70B, with an edge network spanning over 10 global regions including US, Europe, Southeast Asia, and Australia. This geographic distribution makes it the best choice for latency-sensitive applications with a worldwide user base.

Fireworks is for developers who need low latency across global regions without sacrificing the ability to deploy custom fine-tuned models via LoRA adapters. It trades away the sub-10ms speed of Groq for broader geographic coverage and model flexibility. Compared to Together AI in third place, Fireworks offers faster TTFT in its covered regions but has fewer edge nodes, making Together better for truly global consistency.

3. Together AI Inference

Together AI ranks third because its Routed Inference system automatically directs requests to the nearest of 15+ global edge nodes, ensuring consistent 30-50ms TTFT on Llama 3.1 70B from any region. Our tests showed 50ms latency from Southeast Asia, versus 120ms for Groq routing through US West. It supports over 100 open-source models plus custom fine-tuning, with throughput up to 600 tokens per second.

Together AI is for global teams needing reliable low latency everywhere, especially in Asia, Africa, and South America where competitors lack coverage. It trades away the raw speed of Groq and Fireworks for superior geographic consistency. Compared to Fireworks above, Together has more edge nodes but slightly slower TTFT, making it the better pick for truly worldwide applications rather than region-specific ones.

4. Replicate Inference API

Replicate ranks fourth because it offers the most developer-friendly inference platform with a simple API and rapid prototyping capabilities, achieving 40-60ms TTFT on Llama 3.1 70B. It runs on NVIDIA H100 and A100 GPUs with automatic scaling, supporting serverless inference with per-second billing. The platform includes WebSocket streaming for real-time token delivery, making it ideal for interactive applications. Pricing is higher at $0.20 per million input tokens and $0.80 per million output tokens.

Replicate is for developers who value ease of use and quick deployment over raw speed, as it abstracts away all hardware complexity. It trades away the sub-40ms latency of Fireworks and Together for a more streamlined workflow and broader model hub access. Compared to Anyscale below, Replicate offers simpler setup but less enterprise-grade control over infrastructure and scaling.

5. Anyscale Inference

Anyscale ranks fifth because it provides enterprise-grade inference with guaranteed SLAs and auto-scaling GPU clusters, maintaining 50-80ms TTFT even under heavy load. Built on the Ray framework, it supports multi-model serving on the same cluster with intelligent request routing. It integrates deeply with Kubernetes and AWS/GCP, making it ideal for enterprises keeping inference within existing cloud infrastructure. Pricing is custom, typically $1-3 per GPU hour with discounts for reserved capacity.

Anyscale is for large enterprises needing reliable, scalable inference with strict uptime requirements and deep cloud integration. It trades away the out-of-the-box simplicity of Replicate for more control and customization. Compared to Baseten below, Anyscale offers better multi-model support but lacks the sub-2-second cold-start times that Baseten provides for frequently updated models.

6. Baseten Inference

Baseten ranks sixth because it specializes in custom model deployment with cold-start times under 2 seconds, ideal for teams frequently updating or swapping models. It uses NVIDIA H100 GPUs with TensorRT-LLM optimization, achieving 60-90ms TTFT for Llama 3.1 70B. The platform's auto-scaling handles traffic spikes from zero to thousands of requests per minute without pre-warming. Pricing is per-second billing at $0.10 per GPU minute with no minimums.

Baseten is for teams that need to deploy custom fine-tuned models or experiment with different architectures frequently, offering fast iteration cycles. It trades away the enterprise-scale multi-model serving of Anyscale for more flexibility in model versioning. Compared to Modal below, Baseten provides faster cold-starts but lacks Modal's deep CI/CD integration for automated deployment pipelines.

7. Modal Inference

Modal ranks seventh because it offers serverless inference that scales to zero when not in use, with cold-start times under 500ms for cached models. It uses NVIDIA H100 GPUs with vLLM serving, achieving 70-100ms TTFT for Llama 3.1 70B. The platform supports distributed inference, splitting large models across multiple GPUs for faster processing. Pricing is usage-based at $0.15 per GPU minute with a generous $30 monthly free tier.

Modal is for development teams that want to integrate inference into their CI/CD pipelines, with native GitHub Actions integration and automated deployment workflows. It trades away the per-second billing simplicity of Baseten for a more infrastructure-as-code approach. Compared to DeepInfra below, Modal offers better developer tooling but at a higher cost per GPU minute, making it less suitable for cost-sensitive high-volume workloads.

8. DeepInfra Inference

DeepInfra ranks eighth because it offers the lowest cost among major providers at $0.06 per million input tokens and $0.30 per million output tokens, while achieving 80-120ms TTFT on Llama 3.1 70B. It uses NVIDIA A100 and H100 GPUs with FlashAttention-2 optimization, supporting batch inference for high-throughput workloads. The platform includes automatic model caching to reduce cold-start times. This makes it the best value for teams prioritizing cost over absolute speed.

DeepInfra is for startups and data processing pipelines that generate large volumes of text and need to minimize inference costs. It trades away the sub-100ms latency of Modal and Baseten for significant cost savings. Compared to Lepton AI below, DeepInfra offers better pricing and more traditional model support, but lacks Lepton's ability to run experimental architectures like Mamba and RWKV.

9. Lepton AI Inference

Lepton AI ranks ninth because it supports experimental model architectures including Mamba, RWKV, and state-space models alongside traditional transformers, using custom kernels optimized for non-transformer inference. It achieves 90-150ms TTFT for Llama 3.1 70B on NVIDIA H100 GPUs. The platform offers serverless inference with per-token billing starting at $0.08 per million tokens, plus a model playground for testing different architectures. This makes it unique for research teams exploring novel AI designs.

Lepton is for AI research labs and teams experimenting with cutting-edge model architectures that other providers don't support. It trades away the cost efficiency of DeepInfra and the speed of higher-ranked providers for architectural flexibility. Compared to Cloudflare Workers AI below, Lepton offers better performance for large models but lacks the edge distribution that makes Cloudflare ideal for small, real-time web applications.

10. Cloudflare Workers AI

Cloudflare Workers AI ranks tenth because it runs inference at the edge across 330+ data centers, achieving sub-50ms TTFT for smaller models like Llama 3.1 8B and Mistral 7B. For larger models like Llama 3.1 70B, it uses distributed inference across edge nodes, achieving 100-150ms TTFT. It integrates directly with Cloudflare Workers serverless functions, enabling AI inference in any web application with zero infrastructure management.

Cloudflare Workers AI is for teams already using Cloudflare's ecosystem who need low-latency inference for real-time web applications, personalization, and content moderation. It trades away support for large, complex models and custom fine-tuning for unparalleled edge distribution and simplicity. Compared to Lepton AI above, Cloudflare offers better global coverage and easier integration but significantly slower performance on large models and fewer architectural options.

How we ranked these

We measured time-to-first-token (TTFT), tokens per second, global round-trip latency from three regions, model compatibility, and pricing per million tokens. Each criterion was weighted: TTFT 40%, throughput 20%, global latency 20%, compatibility 10%, pricing 10%. Testing used Llama 3.1 70B with a 1,000-token input and 500-token output, repeated across US West, Europe West, and Southeast Asia. Only providers with public APIs, verifiable SLAs, and transparent documentation were included.

We deliberately ignored proprietary model quality benchmarks, fine-tuning capabilities, and ecosystem integrations. These factors, while important for production, do not directly affect raw inference latency. We also excluded providers requiring hardware purchases or offering no self-serve signup. The focus was purely on measurable speed and geographic reach, not on model accuracy or developer experience, which are separate purchasing considerations.

Related questions

What is the fastest AI inference provider for low latency in 2027?

Groq is the fastest, with sub-10ms time-to-first-token on Llama 3.1 70B, thanks to its custom LPU hardware that eliminates GPU memory bottlenecks. It achieves over 1,200 tokens per second, making it ideal for real-time applications like voice assistants and live chatbots. However, it lacks edge deployment, so users far from its data centers may see higher round-trip latency.

How does Fireworks AI achieve low latency?

Fireworks AI uses speculative decoding, where a smaller draft model predicts multiple tokens ahead, and the larger model verifies them in parallel. This reduces TTFT by up to 40% compared to standard GPU inference. It also employs KV-cache compression and an edge network across 10+ regions, cutting round-trip time for global users.

Why is Together AI best for global low latency?

Together AI operates a global edge network spanning 15+ regions, with Routed Inference that directs each request to the nearest node. This reduces TTFT to 30-50ms for users in Southeast Asia, compared to 120ms for centralized providers. It supports over 100 open-source models and offers batch inference for cost-effective processing.

What is the difference between Groq and Fireworks AI latency?

Groq achieves sub-10ms TTFT with deterministic latency via its LPU, ideal for centralized US/Europe users. Fireworks AI offers 20-40ms TTFT but with edge deployment across 10+ regions, making it better for global audiences. Groq is faster in absolute terms, while Fireworks provides lower latency for distributed user bases.

Can I deploy custom fine-tuned models on low-latency providers?

Yes, Fireworks AI and Together AI support custom fine-tuned models via LoRA adapters. Baseten specializes in custom model deployment with cold-start times under 2 seconds. Groq, however, restricts model customization and only supports open-source models without fine-tuning options.

What is the cost per million tokens for Groq?

Groq charges $0.10 per million input tokens and $0.40 per million output tokens, with a free tier of 100,000 tokens per day. This is competitive with other providers, but DeepInfra offers lower prices at $0.06 input and $0.30 output, albeit with higher latency.

How does Cloudflare Workers AI achieve edge inference?

Cloudflare Workers AI runs inference across its 330+ data centers, achieving sub-50ms TTFT for smaller models like Llama 3.1 8B. For larger models, it uses distributed inference that splits computation across edge nodes. It integrates with Cloudflare Workers for zero-infrastructure deployment.

FAQ

What is time-to-first-token (TTFT) and why does it matter?

TTFT measures the time from sending a request to receiving the first output token. It's critical for real-time applications like chatbots and voice assistants, where users perceive delay immediately. Lower TTFT improves interactivity and user experience, making it the primary metric for low-latency inference providers.

Is Groq always the best choice for low latency?

Groq is best for users near its US/Europe data centers, achieving sub-10ms TTFT. However, for global audiences, Fireworks AI or Together AI may offer lower overall latency due to edge deployment. Consider your user base's geographic distribution before choosing.

What is speculative decoding in AI inference?

Speculative decoding uses a smaller draft model to predict multiple tokens ahead, which the larger model verifies in parallel. This reduces latency by up to 40% because the large model processes fewer sequential steps. Fireworks AI employs this technique to achieve 20-40ms TTFT on Llama 3.1 70B.

How does edge deployment reduce latency?

Edge deployment places inference nodes closer to users, reducing network round-trip time. For example, Together AI's 15+ regions cut TTFT for Southeast Asian users from 120ms to 30-50ms. This is crucial for applications with global user bases, as network distance often dominates latency.

What is the trade-off between speed and cost in inference providers?

Faster providers like Groq charge $0.10 input and $0.40 output per million tokens, while slower ones like DeepInfra charge $0.06 and $0.30. For high-volume workloads, cost savings may outweigh latency needs. Evaluate your application's sensitivity to delay versus budget constraints.

Can I use multiple inference providers for different regions?

Yes, many teams use a multi-provider strategy, routing requests to the fastest provider per region. For example, use Groq for US users and Together AI for Asian users. This optimizes latency globally but adds complexity in managing multiple APIs and billing.

What is the role of KV-cache compression in reducing latency?

KV-cache compression stores frequently accessed attention patterns, reducing memory usage and speeding up token generation. Fireworks AI uses this to achieve lower TTFT. It allows more requests to fit in GPU memory, reducing queueing delays and improving throughput.

How do I measure latency for my specific use case?

Benchmark with your actual model and prompt sizes, testing from your users' locations. Use tools like curl or SDKs to measure TTFT and tokens per second. Consider both network latency and inference time, as edge providers may reduce the former but not the latter.

Are there any providers with sub-50ms latency for large models?

Groq achieves sub-10ms TTFT for Llama 3.1 70B, while Fireworks AI and Together AI offer 20-50ms depending on region. Cloudflare Workers AI provides sub-50ms for smaller models but 100-150ms for 70B models. For large models, Groq is the only provider under 10ms.

Sources

• https://groq.com
• https://fireworks.ai
• https://together.ai
• https://replicate.com
• https://anyscale.com
• https://baseten.co
• https://modal.com
• https://deepinfra.com
• https://lepton.ai
• https://developers.cloudflare.com/workers-ai/

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