10 Best LLM Hosting Platforms in 2026

Six months ago, your team was calling one API. Now you have a fine-tuned open-weight model, a retrieval pipeline, a streaming chat endpoint, and a batch job running overnight. The platform that worked for the prototype is no longer the platform that will work for production. Here is where that decision actually sits in 2026.

What Is an LLM Hosting Platform?

An LLM hosting platform is a managed infrastructure service that allows teams to serve large language models via API without building or maintaining GPU hardware, container orchestration, model loading logic, or inference optimization code themselves. Rather than renting a raw GPU, installing CUDA drivers, and serving a model with vLLM or TGI from scratch, a hosting platform abstracts that operational layer and exposes a production endpoint that applications can call directly.

The most basic form of LLM hosting is a serverless API: send a prompt, receive a completion, pay per token. The most sophisticated form involves dedicated GPU clusters with custom model weights, fine-tuning pipelines, RAG integration, guardrails, compliance controls, and enterprise SLAs, all managed by the platform rather than the engineering team.

In 2026, the market has segmented sharply across several dimensions. Speed-specialized platforms use custom silicon (LPUs, wafer-scale chips) to deliver token generation rates that GPU-based providers cannot match. Catalog-breadth platforms host hundreds of open-weight models with a single API key. Custom deployment platforms allow teams to bring their own fine-tuned weights and serve them at production scale. Enterprise platforms add compliance certification, private VPCs, and multi-cloud deployment to the inference layer. The ten platforms below represent the leading options across each of these categories.

1. Together AI

Together AI is the most versatile open-model hosting platform in 2026 by several measures: over 200 open-weight models available via serverless API, a Batch API that cuts per-token cost by 50% for asynchronous workloads, LoRA fine-tuning and full SFT and DPO training pipelines on the same platform that serves the fine-tuned endpoint, and Together Instant Clusters that scale from a single 8-GPU node to multi-node clusters of hundreds of interconnected NVIDIA Blackwell GPUs with API-first self-service provisioning.

The serverless inference catalog spans DeepSeek V3.1 at $0.60/$1.70 per million input/output tokens, DeepSeek V4 Pro at $2.10/$4.40, reasoning models, Qwen 3 variants, Kimi K2.6, and the cheapest hosted option at $0.05/$0.20 per million tokens. Cluster pricing runs $1.76 to $2.39 per GPU-hour for H100 nodes, $3.15 to $3.79 for H200, and $4.00 to $5.50 for B200, depending on commitment length.

Three 2025/2026 additions materially expanded the platform’s scope. The acquisition of Refuel.ai in May 2025 added structured data transformation at tens of millions of records per week. Sweden infrastructure launched in September 2025, cutting round-trip latency by 50 to 70 milliseconds for Northern and Central European users and enabling EU data residency compliance. A real-time voice agent platform launched in March 2026 co-locates speech-to-text, LLM, and text-to-speech on a single cloud with end-to-end latency under 500 milliseconds. Enterprise compliance includes zero data retention, SOC 2 Type II, and HIPAA.

Pricing: Serverless from $0.05/1M tokens. Batch API 50% off. Clusters from $1.76/GPU-hour (H100). Free tier: No free trial credits as of July 2025 (minimum $5 credit purchase). Startup accelerator offers up to $50K in credits. Best for: Teams that need fine-tuning plus serving on one platform, high-throughput batch workloads, multi-node GPU clusters, and voice agent infrastructure.

2. Hugging Face

Hugging Face is the world’s largest AI model repository, hosting 500,000-plus models, one million-plus datasets, and 200,000-plus Spaces, and it sits at the center of the open-source AI ecosystem in a way that no other platform can replicate. Its core competitive advantage is not inference speed or pricing, both of which are bested by specialized providers, but model breadth and community depth: every significant open-weight model release in 2026 lands on Hugging Face first.

For LLM hosting, Hugging Face offers three relevant products. The free Inference API provides rate-limited access to popular models hosted on Hugging Face’s servers, usable without any infrastructure setup and appropriate for prototyping and low-volume evaluation. Inference Endpoints provide dedicated GPU deployments of any Hugging Face model, including custom-uploaded fine-tunes, at hourly pricing starting from $0.06 per hour for CPU endpoints and scaling to GPU configurations priced per instance-hour. An Inference Providers tier routes API calls to third-party providers including Together AI, Fireworks AI, Replicate, and Cerebras from a single Hugging Face API key, giving developers access to multiple speed-specialized inference backends without separate accounts.

The critical practical note for production deployments: Hugging Face Inference Endpoints charge by the hour even when idle unless scale-to-zero is configured. For bursty workloads with long idle periods, the per-hour billing model is more expensive than per-token serverless providers. For sustained, high-volume production inference at the hosted catalog level, going direct to Together AI or Fireworks often costs less and adds features.

Pricing: Free Inference API (rate-limited). Inference Endpoints from $0.06/hr (CPU), GPU from higher hourly rates. Best for: Teams that need the broadest possible model catalog including niche, research, and custom-uploaded models, and practitioners who want one account for model discovery, fine-tuning exploration, and inference routing.

3. Fireworks AI

Fireworks AI occupies the speed-and-cost position in the hosted open-model market with a focus that reads like a clear engineering thesis: serve optimized open-weight models as fast and cheaply as possible, with structured output and function calling built in rather than bolted on. The platform compresses latency through kernel optimization on NVIDIA H100 infrastructure and provides grammar-constrained generation, JSON mode, and native function calling across its full model catalog.

Pricing tiers by model size: sub-4B models at $0.10 per million tokens, 4B to 16B models at $0.20 per million, and Llama 3.1 70B at competitive rates against other serverless providers. Fireworks is the only provider in major independent comparisons with a full post-training stack available self-serve: supervised fine-tuning, direct preference optimization, and LoRA adapter training are accessible without going through an enterprise sales process.

A 2026 Fireworks-published benchmark comparison of seven major inference providers, covering per-token pricing, P50 latency, and output throughput across twelve open-weight models, found that Fireworks and Together AI lead on catalog depth and price while Groq and Cerebras lead on raw throughput. For teams building compound AI systems with tool calls, structured JSON output, and retrieval integration, Fireworks’s native support for these patterns in a single API call makes it a natural fit.

Pricing: From $0.10/1M tokens (sub-4B models). Dedicated deployments and fine-tuning available. Free tier: Available. Best for: Teams prioritizing raw serving speed and cost on open-weight models with structured output requirements, agentic systems with function calling, and teams that need fine-tuning and serving on one platform without enterprise sales overhead.

4. Groq

Groq is the speed category winner for real-time LLM inference, and the December 2025 NVIDIA licensing deal, a non-exclusive license worth roughly $20 billion for Groq’s Language Processing Unit architecture, is the commercial validation of that position. The LPU achieves 460-plus tokens per second on Llama 4 Scout versus 100 to 150 tokens per second on the same model on H100 GPUs, a five-times-plus speed advantage that changes what is technically possible in latency-critical applications.

The LPU architecture achieves this speed through deterministic execution and SRAM-only memory: rather than loading model weights from HBM on every forward pass the way GPU inference does, the LPU keeps the entire model state resident in on-chip SRAM with ultra-high bandwidth, eliminating the memory bandwidth bottleneck that limits GPU throughput. The result is consistent, predictable token generation rates that do not degrade under load, the 2025 Gartner Cool Vendor recognition specifically cited this “what you see is what you get” performance characteristic.

The honest constraint: Groq’s catalog is open-source only. GPT-5, Claude, and Gemini are not available and will not be. If your application requires proprietary frontier models, Groq is not a standalone solution. For applications where speed is architecturally important, streaming chat interfaces where users notice lag, real-time coding assistants, voice agents where the LLM is inside a latency budget, agentic loops with dozens of sequential LLM calls per user session, Groq’s throughput advantage produces materially better user experiences.

Pricing: $0.59/$0.79 per million input/output tokens for Llama 70B class. Free tier with rate limits (30 RPM, 6K TPM on smaller models, no credit card required). Best for: Real-time chat, voice agents, streaming code generation, and agentic applications where tokens-per-second directly impacts user experience.

5. Cerebras

Cerebras is the absolute speed leader for LLM token generation in 2026, and the architecture behind that claim is categorically different from anything else on this list. The Wafer-Scale Engine 3 (WSE-3) is a single chip the size of a dinner plate containing 4 trillion transistors and 900,000 AI-optimized cores, with 44 gigabytes of on-chip SRAM delivering 21 petabytes per second of memory bandwidth. The entire model weight set is stored on-chip, which eliminates the inter-chip communication overhead that limits performance on multi-GPU systems.

In independent benchmarks on Llama 3.3 70B, Cerebras delivers approximately 2,100 tokens per second versus 750 for Groq and 100 to 150 for GPU-based H100 providers. For Llama 3.1 405B, Cerebras is the only provider that runs the model near real-time. Pricing is flat at $0.60/$0.60 per million input/output tokens for 70B class models, meaning output-heavy workloads like code generation or long-form reasoning cost the same as short queries, which produces predictable bills on workloads that typically generate long outputs.

The catalog is intentionally narrow: Llama 3.1 8B, 70B, and 405B are the production stable options, with preview access to additional models in testing. For teams that specifically need the Llama stack at extreme speed with predictable per-token pricing, this narrowness is acceptable. For teams that need access to DeepSeek, Qwen, Kimi, or other models, Cerebras is not a standalone solution.

Pricing: $0.60/$0.60 per million tokens (flat, symmetric input/output) for 70B models. Free tier available. Best for: Applications where absolute maximum token throughput determines quality: real-time reasoning, instant code completion, large-model inference (405B) that would be impractical on GPU infrastructure at production scale.

6. DeepInfra

DeepInfra is the lowest-cost serverless inference provider for standard open-weight models in the hosted market, and across multiple independent 2026 pricing comparisons, it holds the cheapest sticker price on the majority of commonly used models including the Kimi K2 family, Qwen 3.5 family, GLM-5, DeepSeek V4, MiniMax-M2, GPT-OSS 120B, and NVIDIA Nemotron. For cost-sensitive teams running high-volume inference on established open models, DeepInfra’s pricing represents genuine budget optimization rather than a marginal saving.

The platform’s catalog breadth is also one of its clearest strengths in the Q2 2026 comparison landscape: DeepInfra hosts more current open-source model releases than Groq or Cerebras, making it the better option for teams that need access to the latest model releases across multiple families without waiting for speed-specialized providers to add them.

The honest positioning: DeepInfra does not offer multimodal support, dedicated capacity, or the compliance certifications that regulated industries require. Replicate (now part of Cloudflare) was described in mid-2026 comparisons as better suited for prototyping and solo projects than high-scale production; DeepInfra serves production inference on standard models better than that, but it lacks the enterprise contract terms and VPC options that Baseten or Amazon Bedrock provide. For pure cost optimization on standard open-model inference at production scale, DeepInfra is the most cost-effective provider on this list.

Pricing: Among the lowest per-token rates for standard open models. Free tier: Generous free tier available. Best for: Cost-sensitive teams running high-volume inference on standard open-weight models who prioritize lowest per-token rate over compliance certifications or custom deployment capabilities.

7. Modal

Modal is the Python-native serverless compute platform for ML engineers who want to define their infrastructure in code and have it work the way they described it. Where most LLM hosting platforms expose a pre-configured inference endpoint, Modal lets engineers write a Python function, decorate it with @modal.function, specify GPU requirements, batch size, and concurrency, and deploy it to production infrastructure with a single command. Container builds, GPU scheduling, autoscaling, and cold start management are handled by Modal’s runtime.

The billing model is per-second for active compute with zero idle cost when scale-to-zero is configured, which makes Modal economically attractive for inference patterns with significant idle time between bursts. Thirty dollars per month in free compute credits is included. H100 GPU pricing is approximately $6.50 per hour when active. The platform supports fine-tuning through frameworks like torchtune and Unsloth with user-implemented training scripts, giving ML teams full control over the training loop without managed service constraints.

Modal is the right choice when the team’s inference requirements do not fit a prebuilt serving configuration: custom tokenizers, non-standard batching strategies, multimodal preprocessing pipelines, mixture-of-experts routing, or inference-time compute scaling through self-consistency and chain-of-thought sampling. For teams that want managed LLM endpoints without infrastructure code, Modal’s abstraction level is higher than necessary. For ML engineers who want full Python control of how models are served, Modal provides the cleanest implementation path available.

Pricing: Per-second billing when active. $30/month free compute credits. H100 approximately $6.50/hr active. Best for: ML engineers deploying custom models, fine-tunes, or non-standard inference pipelines who want Python-native infrastructure definition and per-second billing for workloads with significant idle time.

8. Replicate

Replicate, now part of Cloudflare since its 2025 acquisition, hosts the largest publicly accessible catalog of community AI models: 50,000-plus models spanning LLMs, image generation, video generation, speech, and specialized task models that were trained by researchers and builders and uploaded for others to run without deploying infrastructure. The Cog framework, Replicate’s open-source model packaging tool, converts any model into a versioned, deployable API endpoint with a standardized interface.

The acquisition by Cloudflare has strengthened the platform’s global CDN capabilities and edge deployment story: models served through Replicate benefit from Cloudflare’s network of 300-plus points of presence globally, reducing geographic latency for end users compared to single-region inference providers. For developers building applications in countries where major cloud providers have limited presence, the Cloudflare network integration represents a meaningful latency improvement.

Independent 2026 comparisons position Replicate as the best option for prototyping and solo projects rather than high-scale production inference: the catalog breadth makes it the fastest path from “I want to run this research model” to a working API call, without any infrastructure setup. For production workloads at scale, Fireworks or Together AI provide better per-token economics and more robust production SLAs. For community model discovery, research replication, and applications that need access to niche or specialized models not available on speed-optimized platforms, Replicate remains the most accessible starting point.

Pricing: Per-second billing based on hardware used. Free tier available. Best for: Developers who need to run research models, community models, or niche specialized models without infrastructure setup, and teams prototyping against a wide variety of model types before committing to a production provider.

9. Amazon Bedrock

Amazon Bedrock is the enterprise LLM hosting platform for organizations that are already on AWS and need access to a curated selection of frontier and open-weight models with the compliance posture, governance controls, and VPC isolation that enterprise procurement and security teams require. The 2026 version of Bedrock is a materially different product from its 2024 launch: Amazon Nova 2 models, Bedrock AgentCore for production-grade agentic infrastructure, OpenAI frontier models hosted on Bedrock, and a pricing model spanning on-demand, batch at 50% discount, and provisioned throughput.

Model availability covers Claude Sonnet 5 and the broader Anthropic Claude family (with promotional pricing of $2/$10 per million input/output tokens through August 31, 2026, then reverting to $3/$15), Amazon Nova Micro, Lite, and Premier, Meta Llama variants, Cohere Command, AI21, and Mistral models. Fine-tuning support covers Amazon Nova models (Micro and Lite for SFT, Nova 2 Lite for both SFT and reinforcement fine-tuning) and Claude 3 Haiku in US West Oregon only. Current-generation Claude models (Sonnet 5, Opus 4.8) do not support fine-tuning on Bedrock in any region.

Bedrock adds approximately 0 to 10% markup over direct provider pricing on some models, and Knowledge Bases (managed RAG) and Guardrails (content filtering and PII redaction) each carry their own billing at $1.00 to $5.00 per thousand API calls and $1.00 per GB of indexed data per month. For organizations on existing AWS enterprise agreements, Bedrock’s pricing often applies committed-use discounts that offset the markup versus direct provider access. For organizations not on AWS with no compliance requirement for AWS VPC isolation, the economics and feature depth of purpose-built LLM hosting platforms may exceed Bedrock.

Pricing: On-demand from $0.07 to $75 per million tokens depending on model. Batch 50% off on-demand. Provisioned throughput available. Best for: Enterprise teams on AWS with compliance requirements (SOC 2, HIPAA, FedRAMP eligibility), existing AWS enterprise agreements, and applications that require Guardrails, Knowledge Bases, and Bedrock AgentCore within a single AWS account and VPC.

10. Baseten

Baseten is the managed inference platform for enterprise teams deploying custom or fine-tuned models that require production-grade reliability, versioning, A/B testing, SOC 2 compliance, and developer experience tooling beyond what raw GPU rental or serverless inference APIs provide. The Truss framework, Baseten’s open-source model packaging abstraction, converts any custom model checkpoint into a production-ready inference service with autoscaling, health checks, and monitoring baked in without requiring the team to write Kubernetes configuration or container orchestration code.

Pricing for custom model deployment runs approximately $6.50 per H100-hour in replica-active time, with per-replica-hour billing that differs from the per-token billing model of serverless providers. For always-on custom model endpoints, the economics are straightforward: multiply replicas by GPU-hours by hardware rate. For intermittent workloads, scale-to-zero configuration is critical to avoid always-on replica costs that make the per-hour billing model more expensive than per-token serverless alternatives.

Enterprise capabilities include private VPCs, model versioning with rollback, multi-node fine-tuning and pre-training job management, A/B testing between model versions for quality validation before full traffic cutover, and custom model deployment of any open-weight or proprietary checkpoint the team provides. A 2026 comparison noted that Baseten targets production model APIs rather than one-off inference calls, with the Truss deployment abstraction specifically designed for engineering teams that own a model artifact and need to serve it professionally.

Pricing: Per replica-hour based on GPU type. H100 approximately $6.50/hr active. Custom enterprise pricing. SOC 2 Type II certified. Best for: Enterprise teams deploying custom fine-tuned models that require versioning, A/B testing, SOC 2 compliance, private VPCs, and production SLAs beyond what serverless inference platforms provide.

Choosing the Best LLM Hosting Platform for Your AI Workload

The most common mistake in LLM platform selection in 2026 is optimizing for the wrong variable. Teams optimize for token price when their bottleneck is latency. They optimize for latency when their bottleneck is compliance. They pick the platform with the broadest model catalog when they only need one model. The decision tree below reflects the variables that actually determine which platform is correct for a given situation.

• If your primary constraint is tokens-per-second, the decision is between Groq and Cerebras. Groq at 460-plus tokens per second on Llama 4 Scout and Cerebras at 2,100-plus on Llama 3.1 70B both operate at a throughput level that GPU-based providers at any price point cannot reach. Groq has the broader open-model catalog and a larger developer ecosystem. Cerebras wins on raw speed, especially for large models like 405B where it is the only provider that runs near real-time, and offers symmetric input/output pricing that makes long-generation workloads cost-predictable. For voice agents, streaming interfaces, and agentic loops with many sequential LLM calls, both are worth benchmarking before committing.
• If your primary constraint is cost per token on standard open models, DeepInfra consistently holds the lowest sticker price in independent Q2 2026 comparisons across the Kimi K2, Qwen 3.5, GLM-5, and DeepSeek V4 families. Together AI’s Batch API at 50% off on-demand rates is the strongest option for asynchronous workloads where 60-minute delivery SLAs are acceptable. Fireworks AI’s per-size-tier pricing is competitive for 4B to 16B class models.
• If your workload involves fine-tuned or custom model weights, the shortlist narrows to Together AI for combined fine-tuning and serving workflows on open models, Fireworks AI for the same with a faster average inference speed on its supported set, Modal for complete Python-native control over how the fine-tune is served, and Baseten for production-grade managed custom model deployment with versioning and A/B testing. Amazon Bedrock’s fine-tuning support is limited: Nova and Llama variants are supported, but current-generation Claude models are not fine-tunable on Bedrock in any region, which is a critical planning constraint for teams assuming Claude fine-tuning is part of the roadmap.
• If your constraint is model catalog breadth, Hugging Face Inference Endpoints or its Inference Providers routing tier cover the widest range of models by a significant margin. Replicate’s 50,000-plus model catalog is the broadest source for research and community models specifically. Together AI at 200-plus models and DeepInfra at the widest current open-model selection among speed-tier providers are the production options for catalog breadth.
• If your constraint is enterprise compliance, Amazon Bedrock is the default for teams on AWS with VPC isolation requirements, FedRAMP eligibility needs, and existing enterprise agreements. Baseten’s SOC 2 Type II certification and private VPC support serve teams that need enterprise compliance without AWS lock-in. Together AI’s SOC 2 Type II and HIPAA compliance, with zero data retention and EU data residency through Sweden infrastructure, serves regulated industries that need open-model hosting with enterprise compliance posture.
• If you are prototyping and cost and speed are both secondary to getting something running in the next hour, the free tiers on Groq (no credit card required), Cerebras, DeepInfra, Modal ($30/month credits), and Replicate each provide enough access to evaluate any of these platforms against a real use case before any financial commitment. The $5 minimum credit purchase requirement at Together AI (as of July 2025, after the $25 free credit was retired) is the only platform in this list that requires upfront payment to start.

Pricing on every platform in this list changes frequently. The pricing cited in this article reflects Q2 to Q3 2026 data from public pricing pages and independent benchmarks. Verifying current rates directly with each provider before making architecture decisions or budget commitments is the correct step for any production evaluation.