Abstract: We introduce Toto, a time series forecasting foundation model with 151 million parameters. Toto uses a modern decoder-only architecture coupled with architectural innovations designed to account for specific challenges found in multivariate observability time series data. Toto's pre-training corpus is a mixture of observability data, open datasets, and synthetic data, and is 4-10$\times$ larger than those of leading time series foundation models. We source observability data exclusively from our own telemetry and internal observability metrics. Extensive evaluations demonstrate that \toto achieves state-of-the-art performance on general purpose time series forecasting benchmarks. Toto's model weights, inference code, and evaluation scripts are available as open source under the Apache 2.0 License.