Is DeepInfra actually cheaper than OpenKey?

For models DeepInfra hosts directly, often yes — it's documented as cheaper than Fireworks on 30 of 37 compared models, and OpenKey doesn't change DeepInfra's underlying rate, it adds a flat 3% on top of whatever the provider charges. If your whole workload runs on one DeepInfra-hosted model at scale, going direct to DeepInfra may cost less. OpenKey's case is flexibility across 52 labs, not undercutting a single provider's best price.

Does DeepInfra have a free tier?

No — DeepInfra doesn't advertise a prominent free tier, unlike OpenRouter, Together AI, and AIMLAPI, which use free access as a way to get developers testing before they pay. OpenKey has 25 free models available, which is closer to that on-ramp model if you want to prototype before committing spend.

Why does DeepInfra's missing llms.txt file matter?

llms.txt is a machine-readable file AI assistants and coding tools read to understand and cite a site. DeepInfra's is confirmed empty, which limits how well AI-assisted developer tools can discover and reference it, compared to competitors like Together AI or Novita that publish more complete versions.

DeepInfra Alternatives (2026): Honest Comparison

If you're reading this, you've probably already hit DeepInfra's pricing page and wondered what else is out there. People go looking for a DeepInfra alternative for a few real reasons: they want a broader model catalog than one inference provider can host, they need a free tier to prototype before committing spend, or they want better AI-assistant discoverability (DeepInfra's llms.txt is confirmed empty, which matters if you're building with AI coding tools that read those files). None of this means DeepInfra is bad — it means different jobs need different tools. We build OpenKey, so read this knowing that. Here's the honest picture, including where DeepInfra wins.

Where DeepInfra is genuinely strong

DeepInfra earns its reputation as a price leader: it's documented as cheaper than Fireworks on 30 of 37 directly compared models, publishes explicit per-model pricing (rare transparency), and hosts one of the widest open-source catalogs of any direct inference provider — Kimi K2, Qwen3.5, GLM-5, DeepSeek V3.2, MiniMax-M2. It's backed by NVIDIA and Samsung Next. Where it falls short: no free tier to test-drive before paying, and no llms.txt file, which limits how well AI assistants can discover and cite it compared to Together AI or Novita.

Documented as the outright price leader — cheaper than Fireworks on 30 of 37 directly compared models
Hosts one of the widest current open-source model catalogs of any direct inference provider reviewed
Regularly included in independent benchmark sites (ArtificialAnalysis) for speed/cost comparisons, giving buyers third-party-verified numbers rather than only vendor claims
Explicit public pricing guides for individual models, unusual transparency versus peers
Backed by NVIDIA and Samsung Next investment, signaling infrastructure-level credibility

Documented complaints

Only complaints we could trace to a source are listed — each one is cited inline.

No llms.txt content (confirmed empty at deepinfra.com/llms.txt), limiting AI-assistant discoverability compared to Together AI, Novita, or AIMLAPI.
Source: competitors/deepinfra.com.md KB entry
No prominent free tier is advertised, unlike OpenRouter, Together AI, and AIMLAPI, which all use free-tier access as a top-of-funnel driver.
Source: competitors/deepinfra.com.md KB entry

The case for OpenKey

DeepInfra is a single provider — great pricing, but one catalog. OpenKey sits on top of 52 labs and 329 models (25 free) behind one OpenAI-compatible key, at a flat 3% fee over whatever the underlying provider charges. Take Claude Sonnet 4.5: provider list price is $3.00/M input tokens, OpenKey charges $3.00 × 1.03 = $3.09/M. No hidden markup layers, no separate pricing tiers to decode. You get DeepInfra's models alongside everyone else's, an indexable status page so you can check uptime history before you commit, a published error-semantics contract instead of guessing what a 500 means, whale-filtered model rankings with public methodology (so you're not reading skewed usage stats), and a no-signup playground to test a model before writing a line of code. If you specifically want DeepInfra's cheapest open-source rates for one model at massive scale, go direct — that's a real use case. If you want flexibility across providers without juggling five accounts, that's the case for OpenKey.

338
Models: 52
Labs: 25
Free models: 3%
Flat fee

The fee, worked

anthropic/claude-sonnet-4.5

Input · 1M tokens

$3.00 + 3%$3.09

Output · 1M tokens

$15.00 + 3%$15.45

Provider price sourced live. The 3% is our entire margin.

Browse all 338 models Read the docs

Other DeepInfra alternatives

Each of these is genuinely the right pick for somebody.

OpenRouter

A universal AI model gateway routing developer requests across 400+ LLMs from 60+ providers through a single OpenAI-compatible API endpoint.

Best for

Developers who want the widest possible LLM selection and provider fallback in one place and can tolerate variable per-provider consistency in exchange for breadth and community-vetted rankings.

LiteLLM

An open-source LLM gateway/proxy library that lets developers call 100+ LLM APIs using a standardized OpenAI-compatible format, self-hosted or run as a lightweight proxy server.

Best for

Teams with the engineering capacity to self-host and maintain their own gateway who want zero markup and full control over provider routing.

Together AI

A full-stack 'AI Native Cloud' platform for serverless inference, fine-tuning, and dedicated GPU clusters (H100/H200/GB200/B300) for open-source and frontier models.

Best for

Teams that want owned-infrastructure-grade performance (not just a routing markup) plus fine-tuning and dedicated GPU clusters under one vendor.

Fireworks AI

An enterprise AI inference platform, built by the ex-Meta PyTorch team, offering per-token serverless inference, dedicated deployments, fine-tuning, and compound AI (multi-step model orchestration).

Best for

Enterprises that need the lowest production latency and a real fine-tune-to-deploy pipeline, and that value a sales-led relationship over self-serve simplicity.

AI/ML API

A unified API platform giving developers access to 500+ models across text, image, video, audio, code, embeddings, and vision through a single OpenAI-compatible endpoint.

Best for

Developers who want one of the broadest text+media model catalogs plus free-tier and startup-credit access, and specifically those who need crypto payment support.

Novita AI

A San Francisco-based AI cloud platform offering 200+ model APIs (LLM, image, video, audio) plus GPU instances, serverless GPU, and agent sandboxes on one platform.

Best for

Developers who want model APIs and raw GPU/serverless compute in the same account at aggressive prices, and value very granular per-key budget controls.

Eden AI

A Lyon, France-based AI API aggregation and management platform giving businesses access to 70+ AI technologies and 100+ models (vision, NLP, speech, OCR) from providers like Google, AWS, and OpenAI through one platform.

Best for

Enterprise and non-technical teams that need multi-provider fallback and a no-code workflow builder for vision/NLP/speech/OCR tasks, not generative LLM or media routing.

Questions

Is DeepInfra actually cheaper than OpenKey?: For models DeepInfra hosts directly, often yes — it's documented as cheaper than Fireworks on 30 of 37 compared models, and OpenKey doesn't change DeepInfra's underlying rate, it adds a flat 3% on top of whatever the provider charges. If your whole workload runs on one DeepInfra-hosted model at scale, going direct to DeepInfra may cost less. OpenKey's case is flexibility across 52 labs, not undercutting a single provider's best price.
Does DeepInfra have a free tier?: No — DeepInfra doesn't advertise a prominent free tier, unlike OpenRouter, Together AI, and AIMLAPI, which use free access as a way to get developers testing before they pay. OpenKey has 25 free models available, which is closer to that on-ramp model if you want to prototype before committing spend.
Why does DeepInfra's missing llms.txt file matter?: llms.txt is a machine-readable file AI assistants and coding tools read to understand and cite a site. DeepInfra's is confirmed empty, which limits how well AI-assisted developer tools can discover and reference it, compared to competitors like Together AI or Novita that publish more complete versions.

DeepInfra facts

What it is: A serverless inference provider focused on cost-competitive, high-speed hosting of open-source LLMs and image/video models, billed per-token or per-second.
Fee structure, as documented: Per-token billing for LLMs; per-second billing for image/video inference. 'Pay only for what you use' with no long-term contracts documented. Example rate cited in the KB: Meta-Llama-3.1-8B at $0.03/$0.05 per million input/output tokens. No prominent free tier. Documented externally as the cost leader — cheaper than Fireworks on 30 of 37 shared models.
Model count: Wide open-source catalog documented as the broadest among direct inference providers reviewed, including the Kimi K2 family, Qwen3.5, GLM-5, DeepSeek V3.2, and MiniMax-M2
Best for: Cost-sensitive teams running open-source models at scale who want the lowest per-token price and don't need a free trial tier or bundled fine-tuning pipeline.
Website: deepinfra.com