Pricing Overview
Together AI uses a pure usage-based pricing model, which means you only pay for what you consume -- there are no monthly subscriptions or seat licenses. New accounts receive $5 in free credits, enough to test models before committing any spend. Serverless inference starts at $0.10 per million tokens for smaller models and scales up to $2.50 per million tokens for the largest open-source models. Dedicated GPU endpoints are available from $0.80 per GPU-hour on A100 hardware, and fine-tuning jobs start at $3 per million tokens. There is no minimum commitment, so teams can start small and scale as workloads grow. This pay-as-you-go structure keeps the barrier to entry low while giving enterprises the flexibility to run production workloads at predictable per-unit costs.
Plan Comparison
Together AI does not offer traditional subscription tiers. Instead, pricing varies by service type and model size. Below is a breakdown of the main pricing dimensions.
| Service | Pricing | Best For |
|---|---|---|
| Free Tier | $5 in credits (one-time) | Evaluation and prototyping |
| Serverless Inference (Small Models) | From $0.10/M tokens | Lightweight tasks: classification, embeddings, small chatbots |
| Serverless Inference (Large Models) | Up to $2.50/M tokens | Complex reasoning, code generation, long-context tasks |
| Dedicated Endpoints (A100 GPU) | From $0.80/GPU/hour | Steady-state production workloads needing guaranteed throughput |
| Fine-Tuning | From $3/M tokens | Custom model training on proprietary data |
Serverless inference is the most accessible entry point. You send API requests and pay per token with no infrastructure management. For workloads that need consistent latency and throughput, dedicated endpoints provide reserved GPU capacity billed hourly. Fine-tuning is charged per token processed during training, making it straightforward to budget for custom model development. Because all three services are metered independently, you can mix and match based on your workload requirements without being locked into a single pricing tier.
Hidden Costs and Considerations
While Together AI's per-token and per-hour rates are transparent, several factors can impact your actual bill. Larger context windows consume more tokens per request, driving up costs on long-document tasks. Dedicated endpoints bill by the hour regardless of utilization, so underused GPUs become expensive idle capacity. Fine-tuning costs compound with dataset size and the number of training epochs -- a multi-pass training run on a large dataset will multiply the base $3/M token rate accordingly. There are no egress fees or platform surcharges listed, but teams should monitor token consumption closely to avoid budget surprises.
Cost Estimates by Team Size
The following estimates assume serverless inference on a mid-range model at approximately $0.50 per million tokens, which is representative of popular open-source models in the 7B-13B parameter range.
| Team Size | Estimated Monthly Usage | Estimated Monthly Cost |
|---|---|---|
| Solo developer / Prototype | ~5M tokens | $2.50 (covered by free credits initially) |
| Small team (3-5 developers) | ~50M tokens | $25 |
| Mid-size team (10-20 developers) | ~500M tokens | $250 |
| Production workload (dedicated A100) | 1 GPU, 24/7 | ~$576/month ($0.80/hr x 720 hrs) |
| Enterprise (multi-GPU cluster) | 4 GPUs, 24/7 | ~$2,304/month |
These figures scale linearly with token volume. Teams running larger models at $2.50/M tokens should multiply the serverless estimates by 5x. Adding fine-tuning to the mix introduces a one-time training cost that varies with dataset size.
How Together AI Pricing Compares
Together AI occupies a distinct niche among AI platforms by focusing exclusively on open-source model hosting with usage-based billing. Here is how it stacks up against competitors in the space.
| Platform | Pricing Model | Starting Price | Key Difference |
|---|---|---|---|
| Together AI | Usage-Based | $0.10/M tokens (serverless) | Open-source models, no subscriptions, dedicated GPU option |
| Anthropic | Freemium | $20/month (Pro) | Proprietary Claude models, subscription-based, team plans available at $25/user/month |
| Fusedash | Usage-Based | $0.00 (free tier) | Token packs at $5, $15, $25; simpler pricing for lighter use cases |
| HypeScribe | Paid | $6.99/month (Starter) | Fixed subscription tiers; focused on transcription rather than general inference |
Together AI stands apart by offering direct access to open-source models without a monthly subscription. Anthropic targets users who want proprietary model quality through fixed monthly plans. Fusedash competes on affordability with prepaid token packs. HypeScribe serves a narrower use case with traditional tiered subscriptions. For teams that want to self-select open-source models and pay strictly by consumption, Together AI delivers the most flexible infrastructure-level pricing in this group.