Is DeepInfra cheaper than Together AI?

Yes, on every model both providers serve in our weekly verified checks. The gap ranges from about 40 percent (DeepSeek-V3, Llama 4 Maverick) to more than 3x (DeepSeek-R1, Qwen2.5 72B, Llama 3.1 8B) per 1M output tokens.

When is Together AI worth the higher price?

When you need its larger model catalog, fine-tuning, dedicated GPU endpoints, or enterprise features like sales-assisted contracts, or when its startup credit program covers your usage. Free credits beat cheap list prices while they last.

Do DeepInfra or Together serve GPT, Claude, or Gemini?

No. Both host open-weight models only: Llama, DeepSeek, Qwen, Mistral, Kimi, gpt-oss and similar. Frontier proprietary models come from OpenAI, Anthropic, or Google directly.

Which is faster, DeepInfra or Together AI?

Together generally has the stronger throughput reputation and markets its optimized serving stack; DeepInfra is mid-pack. Neither matches Groq or Cerebras on raw speed. Both use OpenAI-compatible APIs, so benchmarking both with your own prompts is easy.

Does Together AI offer startup credits?

Yes, Together runs a startup program that grants compute credits to eligible early-stage companies. DeepInfra has no comparable program; its low list prices are the discount. Perkstack tracks Together's program among 200-plus verified offers.

Where do these prices come from?

From Perkstack's verified dataset: we re-check posted provider pricing weekly across 88 model tables and publish the current numbers on our free rankings page. The figures in this post are a dated snapshot of that data.

DeepInfra vs Together AI in 2026: Prices, Models, Which to Pick

DeepInfra vs Together AI in 2026: verified per-model prices on DeepSeek, Llama, Qwen and Kimi, model coverage, speed, enterprise features, credits.

Short answer: if your only question is price per token on shared open-weight models, DeepInfra wins almost every head-to-head in our weekly verified checks, usually by 30 to 70 percent. Together AI earns its premium in three specific situations: you need the widest model catalog including fine-tuning and dedicated endpoints, you care about serving speed and enterprise SLAs, or you can get into its startup credit program and run on someone else's money for a while. Here is the full comparison, built from the same dataset that powers our live rankings, which we re-check weekly.

Head-to-head prices from our verified tables

These are per 1M output tokens, from our latest verified check of both providers' posted pricing. Every model below is served by both hosts.

Model	DeepInfra	Together AI	Gap
Llama 3.1 8B	5 cents	18 cents	Together 3.6x higher
Llama 4 Scout	30 cents	59 cents	Together ~2x higher
Qwen2.5 72B	40 cents	1.20 dollars	Together 3x higher
Llama 4 Maverick	60 cents	85 cents	Together ~42% higher
DeepSeek-V3	89 cents	1.25 dollars	Together ~40% higher
Kimi K2	2.00 dollars	4.50 dollars	Together 2.25x higher
DeepSeek-R1	2.15 dollars	7.00 dollars	Together 3.3x higher

The pattern is not subtle. Across every shared model in our tables, DeepInfra is cheaper, and on the popular workhorses (DeepSeek-R1, Qwen2.5 72B, Llama 3.1 8B) the gap is 3x or more. If you push serious token volume through any of these models, the annual difference is real money.

Two rows where Together holds its own in our data: it is the cheapest host we track for Llama 3.1 405B at 3.50 dollars per 1M output tokens, and it sits near the front on Qwen3 235B A22B at 60 cents, a table DeepInfra does not lead. And on gpt-oss-120b, DeepInfra's 17 cents is the lowest rate we track anywhere, well under what Groq and Fireworks charge for the same model.

Prices genuinely move between our checks, so treat this as a dated snapshot and confirm the current number for your exact model on the rankings page before you migrate anything.

Model coverage

Both are open-weight-only hosts: no GPT, no Claude, no Gemini on either. Within open weights:

DeepInfra covers the mainstream catalog well: Llama, DeepSeek, Qwen, Mistral, Kimi, gpt-oss, plus Whisper for transcription and cheap embedding models. Depth over breadth.
Together AI runs one of the largest serverless catalogs in the business, typically 200-plus models, and adds things DeepInfra does not: image models like FLUX, code models, and fast availability of new releases.
Together also offers fine-tuning as a first-class product and dedicated GPU endpoints when you outgrow serverless. DeepInfra has dedicated deployments too, but Together's tooling around training and custom models is more mature.

If the model you need exists on both, price the token gap. If it only exists on one, the decision is made for you.

Speed and reliability

Neither is a speed specialist the way Groq or Cerebras are, but their reputations differ:

Together has invested heavily in inference optimization and markets its serving stack on throughput. In practice it tends to sit at or above mid-pack on tokens per second for shared models.
DeepInfra is generally mid-pack: fine for chat products and batch pipelines, not the pick when p99 latency is your product.
Both expose OpenAI-compatible APIs, so running your own latency benchmark against both is an afternoon of work: same client, two base URLs. Do this with your real prompts before deciding; published benchmarks rarely match your traffic shape.

For failover, the compatible APIs cut both ways: it is trivial to keep the loser configured as a fallback.

Enterprise features and support

This is where Together justifies charging more:

Together offers dedicated endpoints, fine-tuning, GPU cluster rental, HIPAA-eligible configurations, and the kind of sales-assisted contracts larger companies need. It has raised at a multi-billion valuation and behaves like a platform company.
DeepInfra is leaner: pay as you go, rate limits that scale with your prepaid balance, self-serve everything. That leanness is exactly why the prices are low.

A rough rule: if your procurement process involves a security questionnaire, you will have an easier time with Together. If your procurement process is a credit card, DeepInfra's pricing is the argument.

Startup credits

Together AI runs a startup program that grants compute credits to eligible early-stage companies, which can flip the price comparison entirely for a year: expensive per token but free is cheaper than cheap. DeepInfra does not run a comparable credits program; its discount is the list price itself.

If you are early stage, the right move is usually to stack credits first and optimize list prices second. Together's program is one of many verified offers we track, alongside the big cloud programs. Work through the startup credits checklist and browse the catalog for the current verified list of 200-plus programs; a Perkstack membership pays for itself the first time one application lands.

Which should you pick

Pick DeepInfra if you are cost-driven, your models are in its catalog, and mid-pack speed is acceptable. For most indie builders and seed-stage products running DeepSeek, Llama, Qwen, or Kimi, it is the default answer, as our DeepInfra pricing breakdown shows in more detail.
Pick Together if you need its catalog breadth, fine-tuning, dedicated capacity, or enterprise checkboxes, or if you get accepted into its startup program and the credits cover your burn.
Do both if you can: credits on Together while they last, DeepInfra as the post-credits landing spot. OpenAI-compatible APIs make the eventual switch a config change.

Bottom line

On raw verified prices, DeepInfra beats Together AI on every shared model in our weekly checks, often by 3x on the models startups actually run. Together answers with a bigger catalog, stronger enterprise features, fine-tuning, and a startup credit program that can make its list prices irrelevant for a year. Check the live number for your model on the rankings, and if you have not claimed your credits yet, create an account and start there before paying either provider a dollar.