Time-series foundation models as a service

One API. 49+ pretrained forecasters. No fine-tuning required.

What we host

Every major open-weights time-series foundation model, served behind one consistent inference API. See the full catalog collection for the exact 49 models you can call today.

Why a dedicated provider

General-purpose LLM inference stacks are a bad fit for forecasting. Time-series models have narrow context windows, variable history lengths, quantile outputs, exogenous covariates, and probabilistic sampling — none of which map cleanly onto OpenAI-style APIs. We built TSFM.ai for this surface: past_values, past_timestamps, past_covariates, future_covariates, static_covariates, quantiles, and num_samples are first-class.

Get started

curl -X POST https://api.tsfm.ai/v1/forecast \
  -H "Authorization: Bearer $TSFM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "amazon/chronos-2",
    "inputs": [{"target": [10, 12, 11, 13, 14, 15, 14, 16, 18, 17]}],
    "parameters": {"prediction_length": 24, "quantiles": [0.1, 0.5, 0.9]}
  }'

from tsfm import Tsfm

client = Tsfm()
forecast = client.forecast(
    model="amazon/chronos-2",
    inputs=[{"target": [10, 12, 11, 13, 14, 15, 14, 16, 18, 17]}],
    parameters={"prediction_length": 24, "quantiles": [0.1, 0.5, 0.9]},
)
print(forecast.predictions[0].mean)

Benchmarks

We publish continuously-updated scores for every hosted model on GIFT-Eval and Impermanent.

Contact

General: hello@tsfm.ai · Enterprise: sales@tsfm.ai · Website: tsfm.ai