DeepInfra vs Together AI: Cheapest Open Model Inference?
A focused comparison of DeepInfra and Together AI for serving open-weight models, covering per-token pricing, model breadth, latency, and fine-tuning options.
If you have decided to run open-weight models rather than proprietary frontier ones, the next question is who serves them most cheaply and reliably. DeepInfra and Together AI are two of the most prominent inference providers in this space. Both expose popular open models behind an OpenAI-style API, both bill by the token at rates well below most proprietary models, and both compete hard on price and throughput. This comparison digs into where they differ so you can pick the better fit for your workload rather than guessing from headline rates alone.
The Open Model Inference Market
Open-weight models gave teams an alternative to per-token rates set by a single vendor. Because the weights are available, many providers can serve the same model, which creates genuine price competition. That is good news for buyers: you can shop the same model across providers and pick on cost, latency, and reliability. DeepInfra and Together AI both built businesses on serving these models efficiently at scale, optimizing batching, quantization, and hardware utilization to keep per-token prices low. The result is that the same model can cost very different amounts depending on who serves it and how aggressively they batch.
Pricing and Cost Per Token
Both providers publish per-token pricing that varies by model size. Larger models cost more per token because they consume more compute per forward pass. The headline rates between the two are often close for the same model, so the deciding factor is rarely a single sticker price. Instead, look at your actual prompt and completion length distribution, your concurrency, and whether you can benefit from any volume or committed discounts.
| Factor | What to compare |
|---|---|
| Per-token rate | For the exact model you will deploy, not the cheapest one listed |
| Throughput | Tokens per second under your concurrency |
| Latency | Time to first token plus total generation time |
| Model freshness | How quickly new open models appear |
| Context length | Maximum context the served variant supports |
Model Catalog and Freshness
Both providers race to host new open models quickly after release. Together AI has emphasized a broad catalog and research-oriented features, while DeepInfra has emphasized simple, low-cost serving. For most buyers, the practical test is whether the specific model you want is available, well documented, and served at a context length your application needs. Always confirm the exact variant and quantization, because a heavily quantized deployment can be cheaper but may differ subtly in output quality from the full-precision weights. A small accuracy regression can be invisible in casual testing and costly in production.
Performance: Latency and Throughput
Cheapest per token does not always mean cheapest in practice. If a provider is slower, you may need more concurrency or accept worse user experience, and slow time to first token hurts interactive applications. Benchmark both providers with representative prompts at your expected concurrency. Measure time to first token for responsiveness and total tokens per second for throughput-bound batch jobs. The right winner for a chat product may differ from the right winner for an overnight batch pipeline.
- Interactive apps: prioritize low time to first token and stable tail latency.
- Batch jobs: prioritize raw throughput and per-token cost.
- Both: test with your real prompts, not synthetic short ones.
- Both: watch tail latency under load, since averages hide painful spikes.
Fine-Tuning and Customization
Beyond raw inference, both providers have offered paths to fine-tune or deploy custom open models. If your roadmap includes adapting a base model to your domain, evaluate each provider's tuning workflow, the formats they accept, how they price training, and how they price serving the resulting custom model. A provider that is cheapest for stock inference is not automatically cheapest once you add a custom endpoint that may not benefit from shared, heavily batched serving. Dedicated capacity for a custom model can carry very different economics.
Reliability and Support
For production traffic, uptime and support responsiveness matter as much as price. Evaluate each provider's status history, rate limit behavior under bursts, and how quickly support responds when something breaks at two in the morning. A provider that saves a fraction of a cent per thousand tokens but lacks dependable capacity during demand spikes can cost you far more in lost requests and engineering firefighting than the savings are worth.
API Compatibility and Migration
Both DeepInfra and Together AI expose an OpenAI-style API, which is a quiet but important advantage. It means the cost of trying one against the other, or switching later, is mostly a matter of changing a base URL and a key rather than rewriting your application. That compatibility lets you keep both as options and route traffic to whichever serves a given model best on price and latency at any moment. To preserve that flexibility, keep model identifiers and endpoints in configuration, and avoid leaning on provider-specific extensions unless you genuinely need them. The lower the switching cost, the more leverage you have to chase the better deal as prices and performance shift, which they do frequently in this competitive market.
Rate Limits and Scaling Behavior
As you scale, rate limits and how each provider handles bursts become as important as the base price. A provider that throttles aggressively during demand spikes can force you to over-provision or accept dropped requests, both of which raise effective cost. Review each provider's default limits, how to request increases, and how gracefully they degrade under load. Test a burst that resembles your worst-case traffic, not just a steady trickle, so you learn how the endpoint behaves precisely when your application needs it most. The cheapest per-token rate is cold comfort if the endpoint cannot absorb your real traffic shape without errors.
Which Is Cheaper?
There is no permanent winner. For stock open models at typical interactive workloads, the two are close enough that you should decide on a per-model basis using live pricing plus your own latency benchmark. DeepInfra often appeals to teams that want the simplest, lowest-cost path to a popular model. Together AI often appeals to teams that want a broader catalog and more research-leaning features. The disciplined approach is to shortlist the exact model you need, pull current per-token rates from DeployCue, run a short load test on both, and compute blended cost at your real traffic shape. That process, repeated whenever you add a model, keeps your inference spend honest as prices move.