Toto-Open-Base-1.0
onlineDatadog/Toto-Open-Base-1.0151M params | 512 context | $0.5000 input | $1.50 output
Toto is Datadog's observability-oriented forecasting foundation model. The official model card positions it for high-dimensional, sparse, non-stationary telemetry, and reports strong results on BOOM while remaining competitive on broader benchmark suites. It is the most workload-specific model in the catalog when your data looks like production infrastructure metrics rather than clean academic series.
Model Classification
Family
Toto
Type
time series foundation model
Pretrained time-series model exposed on TSFM.ai for zero-shot or few-shot forecasting workloads.
Resources
Training Data
Over 2T points total: roughly 1T internal observability metrics, public GiftEvalPretrain and Chronos data, plus synthetic series; the official card states no customer data was used.
Recommended For
- • Infrastructure, observability, and telemetry forecasting
- • Sparse, noisy, high-dimensional operational metrics
Strengths
- • Built around real observability-like workloads rather than only clean academic datasets
- • Strong benchmark fit for BOOM-style evaluation
Limitations
- • More specialized than general-purpose forecasting families
- • May be less intuitive as a default pick for simple low-dimensional business series
Capabilities
Tags
Specifications
- Parameters
- 151M
- Architecture
- decoder-only transformer with proportional factorized space-time attention and Student-T mixture output
- Context length
- 512
- Max output
- 1,024
- Avg latency
- n/a
- Uptime
- n/a
- Rate limit
- n/a
- Accelerator
- NVIDIA GPU
- Regions
- Virginia, US
- License
- n/a
Pricing
- Input / 1M tokens
- $0.5000
- Output / 1M tokens
- $1.50
Performance
- Average latency
- n/a
- Availability
- n/a
- Rate limit
- n/a