Together AI for Startups
AI Platform Credits Verified May 2026
$50K in free open-source AI inference credits
Together.ai Startup Program provides $50K in fast open-source model inference credits — Llama, Mistral, Qwen, DBRX and more at throughputs that outperform self-hosted infrastructure.
Who qualifies
Every condition below is taken from the vendor’s own published criteria. Read them before you spend an afternoon on the application.
Osss
The programme is aimed at osss. Vendors read this loosely, but expect to describe the company and what you are building on the application.
Apply at together.ai/startups. $50K in Together AI inference credits. Covers Llama 3, Mistral, DeepSeek, Qwen, and 200+ open-source models at high-throughput, low-latency speeds. No equity requirement.
About Together AI for Startups
Quick answer
Together AI for Startups is one of the more generous AI-infrastructure credits programs in 2026, offering qualifying early-stage startups up to roughly $50,000 in Together AI inference credits — with no equity take. In exchange, founders get production-grade access to 200+ open-weight models (Llama, Mistral, DeepSeek, Qwen, DBRX) at Throughput-as-a-Service pricing, plus fine-tuning and dedicated GPU endpoints. It's a strong fit if your stack is built on (or moving to) open models.
What is the Together AI for Startups program?
Together AI sells GPU compute and inference at a price-performance level that has made it a default backend for open-model workloads. The Together AI for Startups program is the company's structured way of underwriting that spend for early-stage teams: in exchange for a short eligibility review, qualifying startups receive a credit allocation they can draw down against Together AI's inference, fine-tuning, and dedicated-endpoint products.
What makes the program distinctive in 2026 isn't the dollar figure — several closed-model vendors (OpenAI, Anthropic, Google) hand out comparable or larger Azure/GCP-style allocations. It's the model mix. Together is built around open-weight inference. If your product is wired to Llama 3.x, Mistral/Mixtral, DeepSeek-V3, Qwen, DBRX, or one of the long tail of community models, Together is rarely the wrong venue — and self-hosting the same workloads on your own H100s is almost always more expensive at this stage.
What you actually get
Credit programs vary wildly in how the money is useful. Together AI's allocation is unusually well-aligned with how real AI startups burn compute:
Inference credits on 200+ open models
The bulk of the allocation. Draw down on Together's Serverless Inference API across Llama, Mistral, Mixtral, DeepSeek, Qwen, DBRX, Yi, Gemma-class, and the long tail of community open-weight checkpoints. Pricing is token-based and competitive with self-hosting at low-to-mid scale.
Fine-tuning jobs
Apply credits to supervised fine-tuning and LoRA-style adapter training on supported open-weight bases. Useful for adapting a base model to a narrow domain (legal, code, support, medical text) without owning GPUs.
Dedicated GPU endpoints
For workloads that need reserved throughput, low jitter, or large context windows, you can spin up dedicated A100/H100-class endpoints and pay with credits instead of a monthly commitment.
Throughput-tier access
Startups often get bumped to a higher throughput tier than the public sign-up tier, which removes request-per-minute ceilings and unlocks larger batch jobs.
Technical support
Slack/email channel for architecture questions, model selection, and capacity planning — a real time-saver when you're trying to debug latency on a 70B-parameter model at 3am.
Together AI for Startups vs other AI credit programs
Most founders end up applying to two or three of these. Here's how Together's program stacks up against the closest peers in 2026.
| Program | Typical credit | Equity | Model access | Best for |
|---|---|---|---|---|
| Together AI for Startups | ~$50K | None reported | 200+ open-weight models, fine-tuning, dedicated GPUs | Open-model stacks, fine-tuning, high-volume inference |
| OpenAI Startup Fund (PTU / API credits) | Typically $25K–$250K+ in API credits (varies by cohort) | None for credit track; equity for direct funding track | OpenAI closed models only (GPT-4 class, etc.) | Products built natively on OpenAI |
| Anthropic Build with Claude | Often ~$25K–$100K+ in API credits (verify) | None reported | Claude family closed models | Claude-anchored products and agents |
| Google Cloud for Startups (AI track) | Up to ~$100K–$350K+ in GCP credits | None | Vertex AI, Gemini, third-party models on Vertex | Teams already standardizing on GCP |
| AWS Activate (AI partner offers) | Credits vary; up to ~$100K+ with AI partner add-ons | None | Bedrock-hosted models (Anthropic, Mistral, Meta, Cohere, etc.) | AWS-native shops wanting model variety |
Limits and gotchas
No credit program is free money. A few things to plan around with Together AI for Startups:
- Expiration: credits typically have a fixed validity window (commonly ~12 months). If you don't burn them, they're gone — there's rarely a renewal.
- Vendor lock-in: the credit is denominated in Together spend, not cash. You can only spend it on Together's platform, fine-tuning, and dedicated GPU products.
- Open models, not frontier closed models: if you need GPT-4-class or Claude-class reasoning, this program does not cover it.
- Rate ceilings at the free tier: the very cheapest endpoints throttle aggressively. If your traffic spikes, you'll feel it before you upgrade.
- Eligibility review: approvals are not automatic; "AI wrapper" submissions with no clear technical depth are less likely to be approved than teams with a concrete model + infra plan.
✓ Apply if you:
- Build on Llama, Mistral, DeepSeek, Qwen, DBRX or another open-weight model
- Need fine-tuning on your own domain data
- Want to avoid the operational pain of self-hosting H100s
- Prefer programs that do not take equity
- Have a credible plan to burn ~$50K of inference within ~12 months
✗ Skip if you:
- Are deeply locked into OpenAI or Anthropic APIs and have no open-model roadmap
- Need frontier closed-model reasoning for your core product
- Can't realistically consume $50K of inference in the credit window
- Already have a heavily negotiated commitment with another GPU/inference vendor
What the credit covers
- $50K in Together AI inference credits
- 200+ open-source models: Llama 3, Mistral, DeepSeek R1, Qwen 2.5, Falcon
- 3-5x faster inference than AWS Bedrock for equivalent open-source models
- Serverless inference API with OpenAI-compatible endpoints
- Fine-tuning jobs covered by credits
- Dedicated endpoint deployments available
- JSON mode and function calling support
- Usage analytics and per-model cost tracking
Programme tracks
Verified May 2026. What Together AI for Startups publishes for each stage — confirm on the application, since credit programmes are re-cut more often than list pricing.
| Track | Value | What it includes |
|---|---|---|
| Startup Credits | $50K in credits | Apply at together.ai/startups — fast inference of Llama 3.x, Mistral, Qwen, DBRX and 100+ open-source models |
How to apply
4 steps. The last one is the part most people skip.
- 1
Open Together AI for Startups through the link on this page
It carries our referral tag. The terms you get are identical either way, and it never changes what this page says about the programme.
- 2
Have the eligibility evidence ready
Applications are checked against one condition — osss. Incorporation date, cap table and a one-line description of what you are building cover most of it.
- 3
Ask for the expiry window in writing
Together AI for Startups does not publish how long the credit runs, and unused balance is almost always forfeited. Get the activation and expiry dates confirmed before you plan around the grant.
- 4
Know the rate you land on when it runs out
Apply at together.ai/startups. $50K in Together AI inference credits. Covers Llama 3, Mistral, DeepSeek, Qwen, and 200+ open-source models at high-throughput, low-latency speeds. No equity requirement.
Where this programme wins and loses
What works
- Access to 100+ open-source models without managing inference infrastructure
- Throughput significantly faster than self-hosting Llama on cloud instances
- No model lock-in — switch between Llama, Mistral, Qwen and others on the same API key
- Privacy-friendly alternative to OpenAI for data-sensitive enterprise products
What doesn't
- Open-source models still lag GPT-4o on certain reasoning benchmarks
- Credit value depletes faster on larger models (Llama 3.1 405B vs. 8B)
- Partner or application review required — not instant like OpenAI self-serve
The bottom line
The best AI startup credit for open-source model builders. $50K covers serious production inference volume. The fastest managed inference platform for Llama 3 and DeepSeek with an OpenAI-compatible API.
Together AI for Startups FAQ
The questions we actually get asked about this programme.
Ask us something elseTogether AI hosts 200+ open-source models including the full Llama 3 family (8B, 70B, 405B), Mistral 7B and 8x7B (Mixtral), DeepSeek R1 and V3, Qwen 2.5 (7B to 72B), Falcon, Code Llama, and many specialised fine-tuned variants. New models are typically added within days of open-source release.
Together AI typically delivers 3-5x higher throughput (tokens per second) than equivalent open-source model calls on AWS Bedrock or Azure OpenAI. This is because Together uses custom inference hardware and software optimised specifically for open-source LLM architectures.
Yes. Together AI supports full fine-tuning and LoRA fine-tuning jobs on many models in the catalog. Fine-tuning compute costs are covered by startup credits along with inference. Fine-tuned models can be deployed as private endpoints.
Yes. Together AI uses an OpenAI-compatible API format. The base URL and API key differ, but the request/response format, model name structure, and streaming support are identical to the OpenAI API. Most OpenAI client libraries work with Together AI by changing only the base URL.
