DeepInfra Startup Program

• Program is DeepStart — credit-based, application-only, no public tier table.
• Credits apply to DeepInfra's serverless inference API (text, embeddings, images, audio).
• Best fit: pre-seed to Series A AI startups running OSS models in production or pilots.
• Discounted per-token pricing layered on top of the credits is the real long-term upside.
• Apply directly at deepinfra.com/deepstart — no third-party partner, no equity, no cohort.

What is DeepInfra, and what is DeepStart?

DeepInfra is a serverless GPU-inference platform that hosts open-source AI models behind a simple, OpenAI-compatible REST API. You pick a model — Llama 3, Mistral, Qwen, DeepSeek, Gemma, SDXL, Whisper, BGE embeddings, and many more — send a request, and DeepInfra spins up the GPU, runs the inference, and bills you per token or per second. There are no instances to manage, no quotas to negotiate for pilot workloads, and no minimum spend to get started.

DeepStart is the company's startup program. It layers two things on top of that base platform: a one-time credit grant you can spend on any serverless endpoint, and a discounted per-token rate that continues after the credits run out. Together they lower the largest line item in most early-stage AI startups — inference cost — without forcing you to commit to a single model vendor or sign a reserved-capacity contract.

Who qualifies for DeepStart?

DeepInfra positions DeepStart for early-stage AI startups — typically pre-seed through Series A — that are using or evaluating open-source models. The application is short: company name, stage, what you're building, your current or projected monthly inference spend, and a contact email. There's no published cap on funding raised, headcount, or geography, and no third-party partner portal to route through.

What DeepInfra is effectively filtering for is fit: are you a real AI-native team whose workload will land on the platform, and is the credit grant a meaningful accelerant rather than a token gesture? Bootstrapped solo founders, international teams, and AI-adjacent SaaS companies that use models as a feature (rather than a product) have all been approved in practice, though you'll only know for sure once you apply.

What you get with the DeepInfra DeepStart program

How to apply for DeepStart

1. Confirm fit.

   Make sure your stack runs (or can run) on open-source models hosted by DeepInfra. If you're locked to a closed frontier model, this isn't the right program.

2. Visit the program page.

   Go to deepinfra.com/deepstart and start the application form.

3. Describe your use case.

   Tell DeepInfra what you're building, which models you plan to use, your current or projected monthly inference spend, and your stage. Be specific — vague applications get smaller grants.

4. Wait for review.

   DeepInfra's team reviews applications manually, typically within 1–3 weeks. Larger or more complex requests can take longer.

5. Receive your credit + discount letter.

   On approval you'll get a credit grant amount, your discounted rate card, and the credit expiry window. Apply the credits to your existing or new DeepInfra account and start serving traffic.

DeepStart vs other inference-platform startup programs

DeepInfra sits in a crowded lane with Together AI, Fireworks AI, and Replicate. All four offer some form of startup discount, but they differ meaningfully on credit size, model catalog, and how the discount is delivered.

The honest summary: the four programs look similar on paper, but DeepInfra's underlying list price is the lowest in the category for the most common OSS chat models, so its effective discount — credits plus rate — is usually the deepest per dollar of API spend. If you need fine-tuning (Together), ultra-low latency chat (Fireworks), or heavy image/video workloads (Replicate), the calculus shifts.

Frequently asked questions

Final verdict

DeepInfra DeepStart is the rare startup credit program where the ongoing per-token discount matters more than the headline credit number. DeepInfra's list price on open-source serverless inference is already the lowest in the category, the API is OpenAI-compatible so migration is trivial, and the application is short and non-dilutive. The downsides — opaque credit sizing, no closed frontier models, cold starts on niche models — are real but bounded. For any AI startup whose unit economics depend on cheap OSS inference, this is a strong buy.