Up to $50,000 in Together AI free credits is on the table through the Together AI Startup Accelerator. The credits can pay for serverless inference across 200+ open-source models, fine-tuning workflows, dedicated endpoints, and instant GPU clusters.
Startup founders trying to stretch runway, ML engineers shipping an LLM feature into production, and teams migrating off pricier proprietary APIs usually get the most out of this. It’s selection-based, though. You apply, then Together AI reviews your use case and funding stage.
This guide covers Together AI Startup Accelerator eligibility, the exact application steps, what the credits do (and don’t) cover, and a few practical ways to make the credits go further.
Program at a Glance
| Provider | Together AI |
| Credit Amount | Up to $50,000 (tiers: $15K / $30K / $50K) |
| Duration | Time-limited; terms set during onboarding |
| Eligibility | AI-native startups building applications; selection-based |
| Credit Card Required? | No to apply; platform may have $5 minimum purchase |
| Difficulty | Competitive; reviewed application, tiered by funding stage |
| Best For | Open-source LLM inference, fine-tuning, H100/H200 endpoints |
| Official Page | Together AI Program Page |
What You Actually Get
Together AI’s Startup Accelerator is a credits + support program for AI-native startups, with up to $50,000 in platform credits depending on how much you’ve raised. Those credits can be used for serverless inference (OpenAI-compatible API access to 200+ open-source models), fine-tuning (including LoRA workflows and DPO), dedicated endpoints (single-tenant GPU instances like H100/H200), and instant GPU clusters (Kubernetes or Slurm, with InfiniBand networking). You also get “forward-deployed engineering” time with Together AI engineers, plus go-to-market support like marketing amplification and co-selling opportunities. Community perks are real too: founder events, VC network access, and peer networking with other startups in the program.
The practical value depends on which tier you land in. Together AI publishes rough examples for the $15K tier: about 5,000 H100 GPU-hours on a dedicated endpoint at roughly $3/hour, around 500,000 FLUX image generations at about $0.03/image, or on the order of tens of billions of tokens of Llama 3.3 70B inference at around $0.88 per million tokens (input + output). Not bad, honestly, if your product is built around open-source models.
Who Qualifies (and Who Doesn’t)
Together AI positions this as an accelerator, but it’s not a self-serve free tier. You need to be an AI-native startup building an application, and acceptance is based on a mix of funding stage, adoption, technical fit, and alignment with open-source AI development.
- Your startup needs to be building an application (not infrastructure or a research-only project).
- Funding stage affects your tier: Build (up to $5M raised), Grow ($5M–$10M), Scale (over $10M).
- You should be ready to share real operational details like deployment timeline, GPU requirements, and monthly GPU spending.
- No credit card or payment is required just to apply.
If you’re hoping for a simple “create account, get free credits” flow, this isn’t it. And if your work is primarily infrastructure tooling or independent research with no product direction, you’re unlikely to be what they’re looking for.
How to Sign Up
Expect to spend about 20 minutes on the application if you gather your details first.
- Go to together.ai/startup-accelerator.
- Fill out the application form with your startup details (company info, funding stage, tech stack, use case).
- Wait for Together AI’s team to review your application (no public timeline is given for turnaround).
- If accepted, you are placed into the appropriate tier (Build, Grow, or Scale) based on your funding level.
- Credits are provisioned to your Together AI account and engineering hours are scheduled.
The program is described as a rolling application with no fixed cohort deadlines, so you don’t need to time a batch. Once you’re accepted, onboarding is where the credit terms (including expiration) get set.
What the Credits Cover
The platform credits are meant to cover Together AI’s core products for building and shipping with open-source models. That includes pay-per-token serverless inference through an OpenAI-compatible API, multiple fine-tuning approaches, and options for dedicated or on-demand GPU capacity.
| Service / Feature | What It Does | Included? |
|---|---|---|
| Serverless Inference | OpenAI-compatible API for 200+ open-source models. | ✓ |
| Fine-Tuning | SFT and DPO with LoRA on models like DeepSeek and Llama. | ✓ |
| Dedicated Endpoints | Single-tenant GPUs (H100/H200) billed per minute. | ✓ |
| Instant GPU Clusters | On-demand clusters with Kubernetes or Slurm and InfiniBand. | ✓ |
Notable exclusion: Reserved GPU Clusters are explicitly not covered by these credits (only Instant Clusters are). And this is an open-source model platform, so proprietary models like GPT-4 or Claude are not part of what you’re getting.
Limitations to Know About
Every credits program has catches. Together AI is pretty clear on a few of them, and a couple others are “ask during onboarding” items that can matter for budgeting.
- Credits exclude Reserved GPU Clusters, even though instant clusters are included.
- Together AI’s general platform mentions a minimum $5 credit purchase to unlock full API access (Build Tier 1), and it’s unclear if accelerator credits bypass that requirement.
- There’s no general free trial outside of this program, so you can’t count on a fallback “free $X” path.
- Credits are likely time-limited, but the specific expiration terms are set during onboarding rather than published up front.
When credits run out, you should assume you’ll need to pay to keep workloads running, because Together AI prices usage like a normal platform (per token or per time, depending on the product). Since the accelerator’s expiration rules are handled during onboarding, get the exact end date and any usage conditions in writing so billing doesn’t surprise you later.
Have Unused Together AI Credits?
Credits are great until they’re not. A lot of teams accept a big allocation, then ship slower than expected, change models, or move inference elsewhere, and the credits sit there ticking toward expiration. If you end up with unused Together AI credits you can’t realistically burn down, AI Credit Mart lets you sell them instead of watching them expire. You typically price them at a discount, and the buyer gets cheaper compute.
Need More Together AI Credits?
Once your accelerator credits run low, paying retail isn’t your only option. AI Credit Mart lists discounted Together AI credits from companies with surplus allocations that they won’t use in time. Prices typically land about 30–70% below face value, which can make a meaningful dent in inference or fine-tuning spend. It’s a simple way to extend runway without changing providers.
Tips for Getting the Most Out of Your Credits
- Treat the engineering hours as a deliverable, not a perk, and bring a clear “top bottleneck” list to the sessions.
- If your product depends on proprietary models, be honest early, because this program is designed around open-source models.
- Ask during onboarding whether accelerator credits bypass the platform’s minimum $5 credit purchase requirement for full API access.
- Plan around expiration, since terms are set during onboarding; pick a milestone (launch, migration, fine-tune) that burns credits predictably.
- Use the OpenAI-compatible API angle to reduce switching cost: start by swapping endpoints, then optimize performance once you’ve stabilized.
Frequently Asked Questions
Up to $50,000 in platform credits, tiered by funding stage ($15K Build, $30K Grow, $50K Scale). Together AI’s own rough examples for the $15K tier include about 5,000 H100 GPU-hours on a dedicated endpoint (at roughly $3/hour), around 500,000 FLUX image generations (about $0.03/image), or on the order of tens of billions of tokens of Llama 3.3 70B inference (around $0.88 per million tokens, input+output). Real spend varies a lot by model choice, batching, and how you structure prompts. If you’re doing heavy inference, the credits can disappear fast, so treat the program like a runway extension, not “free forever.”
No. There is no credit card or payment required to apply.
They’re likely time-limited, and the specific expiration terms are set during onboarding.
Yes. If you have Together AI credits you won’t use before they expire, you can list them on AI Credit Mart and sell them at up to 70% of face value. Companies regularly list surplus credits from startup programs and enterprise agreements.
AI Credit Mart has discounted Together AI credits available from companies with surplus allocations. Prices are typically 30-70% below retail.
Unused credits stop being usable, and you should expect to pay normal usage rates to keep services running after that.
No. The credits explicitly exclude Reserved GPU Clusters (instant clusters are included).
They ask for a lot more than a basic signup form: company fundamentals, funding history, customer metrics, GPU requirements, deployment timeline, current cloud providers, model architectures in use, monthly GPU spending, and your specific use cases. In other words, you’re being evaluated on seriousness and fit. If you can’t answer these cleanly, pause and gather the data first. It usually makes the review easier on their side too.
Up to $50K in credits plus real engineering help can move a serious open-source AI product forward fast. Apply if you fit, ship while the clock is running, and if you end up with surplus credits later, sell them instead of letting them rot.
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