Toto-Open-Base-1.0
LegacyonlineDatadog/Toto-Open-Base-1.0151M params | 4K context | $0.00025 per forecast
Toto-Open-Base-1.0 is Datadog's first open observability-oriented forecasting foundation model, a 151M-parameter checkpoint purpose-built for the sparse, high-dimensional telemetry that infrastructure monitoring produces. It is the original Toto 1.0 release and a legacy hosted model on TSFM.ai: still useful for workflows that depend on its upstream fine-tuning or exogenous-variable path, but no longer the default for new zero-shot work.
Architecturally it is a decoder-only transformer with proportional factorized space-time attention and a Student-T mixture output head, the latter giving it native probabilistic, multivariate forecasts over correlated metric streams. Pretraining spans over 2T points total: roughly 1T internal observability metrics, public GiftEvalPretrain and Chronos data, plus synthetic series, and the official card states that no customer data was used.
On TSFM.ai, keep using Toto 1.0 only if you rely on its upstream fine-tuning or exogenous-variable workflow; the platform plans to deprecate it for new zero-shot usage once Toto 2.0 is validated in production. For new observability forecasting, start with the recommended Datadog/Toto-2.0-313m, which carries the newer u-muP-scaled architecture and quantile interface and is the designated replacement for this checkpoint.
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
- • Legacy generation: use Toto 2.0 for new zero-shot observability workloads unless you need Toto 1.0 fine-tuning or exogenous-variable support
Capabilities
Tags
Specifications
- Parameters
- 151M
- Architecture
- decoder-only transformer with proportional factorized space-time attention and Student-T mixture output
- Context length
- 4,096
- Max context
- 4,096
- Minimum history
- n/a
- Recommended history
- n/a
- Input step
- n/a
- Required target series
- 1
- Temperature
- Ignored
- Top P
- Ignored
- Max output
- 1,024
- Avg latency
- n/a
- Uptime
- n/a
- Plan limits
- 1,000 rpm free · 1,000,000 rpm with billing
- Accelerator
- T4
- Regions
- Virginia, US
- License
- n/a
Pricing
- Per forecast
- $0.00025
Performance
- Average latency
- n/a
- Availability
- n/a
- Plan limits
- 1,000 rpm free · 1,000,000 rpm with billing