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# Top 7 AI Inference Providers for Production LLM Workloads in 2026
- URL: https://www.edgewisely.com/top-7-ai-inference-providers-for-production-llm-workloads-in-2026/
- Published: 2026-08-29T04:47:01.000Z
- Updated: 2026-08-29T04:47:01.000Z
- Description: Who this is for: engineering teams choosing where to run open and proprietary models in production, and what's changed in the category since inference became a venture-funded arms race in its own right.
- Author: John Karpentar
- Tags: Roundups, AI

# Top 7 AI Inference Providers for Production LLM Workloads in 2026

**Who this is for: engineering teams choosing where to run open and proprietary models in production, and what's changed in the category since inference became a venture-funded arms race in its own right.**

An AI inference provider runs a model for you over an API — you send a prompt, it returns tokens, and you never touch a GPU. The category has consolidated around a handful of names since 2025: Together AI, Fireworks AI, and Groq lead on scale and speed, Baseten and DeepInfra compete hard on price and infrastructure control, and Replicate and Anyscale occupy narrower niches that are both now entangled in acquisitions. As of August 2026, the leaders are the seven below, ranked by scale, breadth, and how stable a bet each looks for a production workload.

## How we picked these

We ranked on four criteria: breadth of the model catalog (how many open and proprietary models you can call from one account), infrastructure maturity (dedicated deployment options, uptime, enterprise controls like SOC 2 or HIPAA), pricing transparency (published per-token or per-GPU-hour rates versus quote-only), and independence — whether the company's ownership or roadmap is currently in flux. We excluded raw GPU rental clouds (that's a separate category) and self-hosted serving frameworks like vLLM or Ray Serve, which you run yourself rather than call as a service.

## Quick comparison

| Company                                                    | Best for                                                 | Deployment                        | Pricing model                                    |
| ---------------------------------------------------------- | -------------------------------------------------------- | --------------------------------- | ------------------------------------------------ |
| [Together AI](https://www.together.ai/?ref=edgewisely.com) | One vendor across serverless, dedicated, and fine-tuning | SaaS (serverless + dedicated GPU) | Per-token (serverless), per-GPU-hour (dedicated) |
| [Fireworks AI](https://fireworks.ai/?ref=edgewisely.com)   | Compliance-sensitive enterprises                         | SaaS + BYOC                       | Per-token + per-GPU-hour                         |
| [Groq](https://groq.com/?ref=edgewisely.com)               | Latency-critical applications                            | SaaS (GroqCloud)                  | Per-token, free/dev/enterprise tiers             |
| [Baseten](https://www.baseten.co/?ref=edgewisely.com)      | Deploying custom or proprietary models at scale          | SaaS + dedicated                  | Per-GPU-minute + per-token (Model APIs)          |
| [DeepInfra](https://deepinfra.com/?ref=edgewisely.com)     | Cost-sensitive teams running open models at volume       | SaaS (own data centers)           | Per-token, among the lowest published rates      |
| [Replicate](https://replicate.com/?ref=edgewisely.com)     | Multimodal (image/video/audio) model experimentation     | SaaS (part of Cloudflare)         | Per-second compute or per-output                 |
| [Anyscale](https://www.anyscale.com/?ref=edgewisely.com)   | Teams already building on Ray                            | Hosted or BYOC                    | Consumption-based credits / per-hour             |

## 1\. Together AI

[Together AI](https://www.together.ai/?ref=edgewisely.com) is a serverless inference cloud that lets developers call open-weight and some proprietary models through a single API, with per-token pricing that varies model by model — from $0.05/$0.20 per million input/output tokens for small models like GPT-OSS 20B up to $3.00/$7.00 for reasoning models like DeepSeek R1\. Beyond serverless calls, it offers dedicated GPU endpoints billed per hour (around $6.49/hr for an H100), a Batch API that processes up to 30 billion tokens asynchronously at roughly half the synchronous price, and fine-tuning on top of the same infrastructure. The company raised $800 million in a Series C led by Aramco Ventures in July 2026 at an $8.3 billion valuation, with annual bookings that topped $1.15 billion by Q2 2026 and customers including Cursor, Cognition, and Decagon.

**Best for:** teams that want one vendor covering serverless inference, dedicated capacity, and fine-tuning without switching platforms.

**Pros** \- Per-token rates span nearly two orders of magnitude ($0.05–$9 per million tokens), so cheap models are genuinely cheap - Dedicated endpoints, serverless, batch, and fine-tuning all live in the same account and API surface - $8.3B valuation and $1.15B in annual bookings signal it will still be around for multi-year commitments - Cached-input and batch discounts can cut costs roughly in half for reusable or asynchronous workloads

**Cons** \- The wide pricing range across 100+ models makes upfront cost forecasting harder than with a flatter-rate provider - Dedicated endpoints require a GPU-hour commitment rather than pure pay-per-token - Enterprise support and on-prem/BYOC terms are quote-only, not published - Multi-tenant serverless inference can show more latency variance than single-tenant, custom-silicon competitors like Groq

![Together AI's website describing its AI Native Cloud platform for inference, fine-tuning, and training](https://storage.ghost.io/c/54/5a/545a66b3-60ef-480c-80ae-765bac52f6ec/content/images/2026/08/together.png)

Image: [Together AI](https://www.together.ai/?ref=edgewisely.com)

## 2\. Fireworks AI

[Fireworks AI](https://fireworks.ai/?ref=edgewisely.com) runs a cloud inference platform for more than 400 open-source models, with serverless endpoints for LLMs, vision, and audio, and no cold starts on its hosted models. Per-token pricing starts around $0.10 per million tokens for small models and rises to roughly $0.90 per million for large ones, while on-demand GPU deployments range from $2.90/hour (A100) to $9.00/hour (B200). The platform supports LoRA and full-parameter fine-tuning plus reinforcement fine-tuning, and its SOC 2, HIPAA, and GDPR compliance make it a more common pick for regulated industries than smaller competitors. Fireworks' valuation reached $17.5 billion following a Series D round, per Edgewisely's earlier [coverage of the raise](https://www.edgewisely.com/fireworks-ai-series-d-17-5-billion-valuation/).

**Best for:** enterprises in regulated industries that need SOC 2/HIPAA compliance alongside inference speed.

**Pros** \- SOC 2, HIPAA, and GDPR compliance out of the box, ahead of most inference-only competitors - 400+ open models available serverless with no cold-start penalty - Cached tokens get a 50% discount, and batch inference gets another 50% off for async workloads - Supports LoRA, full-parameter, and reinforcement fine-tuning on the same platform

**Cons** \- $17.5B valuation puts pressure on the company to keep growing revenue fast, which can mean pricing or product changes as it scales - On-demand GPU rates ($2.90–$9.00/hr) sit in a similar range to competitors, so the price advantage is mainly on serverless tokens, not dedicated compute - Regulated-industry features (HIPAA workloads, dedicated deployments) typically require a sales conversation rather than self-serve signup - New account credit is a modest $1, not enough for meaningful load testing before committing

![Fireworks AI's homepage describing fast inference for open and closed AI models](https://storage.ghost.io/c/54/5a/545a66b3-60ef-480c-80ae-765bac52f6ec/content/images/2026/08/fireworks.png)

Image: [Fireworks AI](https://fireworks.ai/?ref=edgewisely.com)

## 3\. Groq

[Groq](https://groq.com/?ref=edgewisely.com) runs inference on its own custom LPU (Language Processing Unit) chips rather than Nvidia GPUs, which lets it post token-generation speeds of 500–1,000+ tokens per second — among the fastest in the category. Pricing runs $0.05–$0.90 per million tokens depending on the model (Llama 3.1 8B at the low end, Llama 3.3 70B around $0.59/$0.79 in/out), across free, developer, and enterprise tiers, with 50% discounts for batch processing and prompt caching. The company says more than five million developers run workloads on its platform each week. Groq's trajectory took a sharp turn in late 2025 when it signed a non-exclusive inference-technology licensing deal with Nvidia reportedly worth around $20 billion; by August 2026, Groq raised $350 million at a $3.5 billion valuation — roughly half its $6.9 billion valuation from September 2025 — after losing talent to Nvidia in the wake of the deal.

**Best for:** latency-sensitive applications where raw tokens-per-second matters more than model catalog breadth.

**Pros** \- Custom LPU silicon delivers some of the fastest per-token generation speeds available as a hosted API - A genuinely usable free tier (no credit card required) for prototyping, with 30 requests per minute - Batch and cached-prompt discounts each cut costs by half - Reports 5+ million developers running workloads weekly, suggesting broad real-world adoption

**Cons** \- The Nvidia licensing deal and the roughly 50% valuation drop between September 2025 and August 2026 raise real questions about Groq's independent roadmap and talent retention - Custom LPU hardware means new models need to be specifically compiled for Groq's silicon, so its catalog trails the largest multi-tenant marketplaces - The free tier's 30 RPM limit is restrictive for anything beyond prototyping - Enterprise pricing and SLAs are not published and require direct sales contact

![Groq's website describing its LPU-based inference cloud](https://storage.ghost.io/c/54/5a/545a66b3-60ef-480c-80ae-765bac52f6ec/content/images/2026/08/groq.png)

Image: [Groq](https://groq.com/?ref=edgewisely.com)

## 4\. Baseten

[Baseten](https://www.baseten.co/?ref=edgewisely.com) is an inference platform built around deploying and serving custom or proprietary models, billing dedicated deployments per GPU-minute across tiers from T4 ($0.01052/min) up to B200 ($0.16633/min), with replicas that scale to zero and stop billing when idle. It also offers Model APIs, a pay-per-token option for pre-optimized models similar to its serverless-marketplace competitors. The Basic plan has no monthly minimum; Pro and Enterprise tiers, aimed at high-volume or compliance-heavy customers, are quote-only. Baseten's scale grew fast through 2026: a $300 million Series E in January valued it at $5 billion, followed by a $1.5 billion Series F in June that priced the company at $13 billion, with Sacra estimating $600 million in annualized revenue by March 2026 — up roughly 19x year over year.

**Best for:** teams deploying their own fine-tuned or proprietary models rather than calling a shared model marketplace.

**Pros** \- Granular per-minute GPU billing across many tiers, with idle replicas scaling to zero and not billed - No monthly minimum on the free Basic plan - $13B valuation and reported $600M ARR indicate substantial engineering investment and staying power - Supports both dedicated model deployment and pay-per-token Model APIs from one platform

**Cons** \- Per-GPU-minute pricing across many hardware tiers is harder to estimate upfront than a flat per-token rate - Pro and Enterprise tiers are quote-only, so real enterprise costs aren't public - Reported \~19x year-over-year revenue growth puts strain on support and reliability consistency during hypergrowth - Less oriented toward "pick a model from a catalog and call it" than Together, Fireworks, or DeepInfra — you're generally deploying your own model artifact

![Baseten's website describing its inference platform for deploying AI models in production](https://storage.ghost.io/c/54/5a/545a66b3-60ef-480c-80ae-765bac52f6ec/content/images/2026/08/baseten-2.png)

Image: [Baseten](https://www.baseten.co/?ref=edgewisely.com)

## 5\. DeepInfra

[DeepInfra](https://deepinfra.com/?ref=edgewisely.com) runs its own hardware across eight U.S. data centers rather than renting from a cloud provider, which its founders — former engineers from messaging app imo.im — say lets it control the full stack from GPU to API and pass through some of the lowest published per-token rates in the category: Llama 3.1 8B and Mistral 7B from $0.06 per million tokens, DeepSeek R1 from $0.55 per million. It also resells some proprietary models, including Claude Sonnet 5 (around $2.00/$10.00 per million in/out) and Gemini 2.5 Pro (around $1.25/$10.00). Founded in 2022, DeepInfra raised an $18 million Series A in April 2025 and a $107 million Series B in May 2026, led by 500 Global with participation from Nvidia and Samsung Next — a modest $136 million in total funding compared to the billion-dollar rounds at Together, Fireworks, Groq, and Baseten.

**Best for:** cost-sensitive teams running high-volume open-model workloads who don't need enterprise compliance certifications.

**Pros** \- Among the lowest published per-token rates in the category for popular open models - Owns its own hardware across eight U.S. data centers, giving it direct control over infrastructure costs - No monthly subscription, minimums, or seat fees — pure usage-based billing - Also resells proprietary frontier models (Claude, Gemini) alongside open ones from the same account

**Cons** \- Total funding of $136 million is an order of magnitude smaller than competitors' war chests, a real question mark for long-term stability - Narrower compliance and enterprise feature set than Together, Fireworks, or Baseten — no published SOC 2/HIPAA equivalent - Capacity is tied to eight U.S. data centers, which can limit geographic and latency options outside North America - Smaller company means less dedicated account management for large enterprise deals

![DeepInfra's website listing its machine learning inference models and pricing](https://storage.ghost.io/c/54/5a/545a66b3-60ef-480c-80ae-765bac52f6ec/content/images/2026/08/deepinfra.png)

Image: [DeepInfra](https://deepinfra.com/?ref=edgewisely.com)

## 6\. Replicate

[Replicate](https://replicate.com/?ref=edgewisely.com) is a marketplace of more than 100,000 AI models accessible through a REST API, historically strongest for image, video, and audio generation models rather than frontier text LLMs. Billing is per-second of compute rather than per-token: a CPU prediction runs $0.000025/second, a T4 GPU $0.000225/second, an H100 $0.001525/second, with popular models like FLUX billed as a flat $0.003–$0.04 per image instead. There's no subscription or minimum spend. Cloudflare announced its acquisition of Replicate on November 17, 2025 to fold the platform into its developer cloud; terms weren't disclosed, though Replicate had previously raised more than $23 million from Y Combinator, Sequoia, and other investors.

**Best for:** teams generating or experimenting with image, video, and audio models rather than running production text LLMs.

**Pros** \- One of the largest model marketplaces (100,000+ models) available through a single API - No subscription or minimum spend — pure pay-as-you-go - Now backed by Cloudflare's infrastructure and can route through Cloudflare's AI Gateway at standard Replicate rates - Per-model flat pricing (e.g., per image) is simple to reason about for specific use cases

**Cons** \- Per-second-of-compute billing is awkward to compare against token-priced LLM competitors - Product direction and independence are now subject to Cloudflare's roadmap following the November 2025 acquisition, with no new Replicate-specific pricing tiers announced as of mid-2026 - Historically weaker on frontier text-generation models than Together, Fireworks, or Groq - Cold-start latency has been a known pain point for less popular models in the catalog

![Replicate's documentation describing how to run AI models through its API](https://storage.ghost.io/c/54/5a/545a66b3-60ef-480c-80ae-765bac52f6ec/content/images/2026/08/replicate.png)

Image: [Replicate](https://replicate.com/docs?ref=edgewisely.com)

## 7\. Anyscale

[Anyscale](https://www.anyscale.com/?ref=edgewisely.com) is the commercial platform built around Ray, the open-source distributed computing framework, and Ray Serve, its model-serving library. Anyscale manages Ray infrastructure so teams don't have to run their own clusters, billing through consumption-based Anyscale Credits with entry points around $3–$100 for smaller workloads and custom enterprise pricing for committed contracts, across either a fully hosted option or a Bring-Your-Own-Cloud deployment. Ray Serve itself supports response streaming, dynamic batching, and multi-node/multi-GPU serving across any ML framework. On July 30, 2026, Nscale announced it would acquire Anyscale for approximately $1.65 billion — a deal that, as of this writing, had not yet closed.

**Best for:** teams already building on Ray who want a managed layer instead of running their own Ray clusters.

**Pros** \- Built on Ray, a mature, widely adopted open-source framework, not a proprietary black box - Framework-agnostic serving (PyTorch, TensorFlow, and others) rather than locked to one model format - Choice of fully hosted or Bring-Your-Own-Cloud deployment - Ray Serve supports advanced serving patterns (dynamic batching, multi-node/multi-GPU) that simpler API providers don't expose

**Cons** \- The pending $1.65B Nscale acquisition (announced July 30, 2026) introduces near-term uncertainty about pricing, product direction, and support - Ray has a steeper learning curve than calling a simple inference REST API - Best suited to teams already invested in Ray — less of a fit if you just want to call a hosted model - Pricing entry points and credit-based billing are less transparent than straightforward per-token rates

![Anyscale's platform page describing its managed Ray infrastructure for AI serving](https://storage.ghost.io/c/54/5a/545a66b3-60ef-480c-80ae-765bac52f6ec/content/images/2026/08/anyscale.png)

Image: [Anyscale](https://www.anyscale.com/platform?ref=edgewisely.com)

## How to choose

If you want the broadest catalog with the least switching cost as you grow from prototype to fine-tuning, Together AI or Fireworks AI cover the most ground. If raw latency is the deciding factor — a voice agent, a real-time coding assistant — Groq's LPU silicon is worth testing despite the company's ownership uncertainty. If you're deploying your own fine-tuned or proprietary model rather than calling a shared one, Baseten's per-minute GPU billing and scale-to-zero replicas fit better than a token-priced marketplace. Cost-sensitive teams running high-volume open models should benchmark DeepInfra's rates directly. For image, video, or audio generation, Replicate's marketplace remains the most exhaustive, and if your team already runs Ray clusters, Anyscale removes the operational overhead — pending the outcome of its acquisition.

## Frequently Asked Questions

### What's the difference between an AI inference provider and a GPU cloud?

A GPU cloud (like CoreWeave or Lambda) rents you raw compute — you install and manage the serving software yourself. An inference provider like Together AI or Groq runs the model-serving stack for you; you send a prompt and get tokens back through an API, without managing GPUs or serving frameworks directly.

### Do these inference providers support fine-tuning?

Several do. Together AI, Fireworks AI, and Baseten all support fine-tuning or custom model deployment on the same infrastructure used for inference. DeepInfra, Groq, Replicate, and Anyscale are more focused on serving pre-existing models rather than training pipelines.

### How is inference pricing usually structured?

Most providers bill per million input/output tokens for text models, with rates varying widely by model size and provider — from roughly $0.05 to $9 per million tokens as of August 2026\. Baseten and Anyscale also offer per-GPU-minute or credit-based billing for dedicated deployments, and Replicate bills per second of compute for many models.

### Can I self-host instead of using a managed inference provider?

Yes — frameworks like vLLM, Ray Serve, and Triton let you run inference on your own infrastructure. That trades a per-token bill for infrastructure management overhead, and is a separate category from the hosted providers covered here.

### Which inference provider is fastest?

Groq's custom LPU silicon posts some of the fastest published tokens-per-second figures (500–1,000+) among hosted providers, though actual latency depends on model, region, and load. Teams with strict latency requirements should benchmark directly rather than rely on published figures alone.

## Editor's note — sources:

Together AI, Fireworks AI, Groq, Baseten, DeepInfra, Replicate, and Anyscale pricing and funding figures were sourced from each company's own pricing/blog pages, TechCrunch, Bloomberg, Businesswire, SiliconANGLE, and Edgewisely's prior coverage of Fireworks AI's Series D, all current as of August 2026\. Figures change frequently — verify current rates directly with each provider before purchasing.