Toto-2.0-1B

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Datadog/Toto-2.0-1B

1B params | 512 context | $0.00025 per forecast | Apache-2.0

Toto-2.0-1B is the large hosted checkpoint in Datadog's Toto 2.0 line. It is intended for high-value observability workloads, offline comparisons, and cases where multivariate telemetry structure is worth the higher inference cost.

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

HuggingFace

Training Data

Toto 2.0 continues Datadog's observability-first pretraining line for sparse, high-dimensional telemetry. Datadog positions the release as the current recommended zero-shot generation for BOOM-style infrastructure metrics.

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
• Fine-tuning and exogenous-variable support are planned upstream for Toto 2.0 but are not available in the current release

Capabilities

forecastingquantile-forecastingmultivariateobservabilityzero-shot

Specifications

Parameters: 1B
Architecture: u-muP-scaled decoder-only transformer with alternating time/variate attention and quantile output head
Context length: 512
Max context: 4,096
Minimum history: 32
Recommended history: 512
Input step: 32 points
Required target series: 1
Temperature: Ignored
Top P: Ignored
Max output: 2,048
Avg latency: n/a
Uptime: n/a
Plan limits: 1,000 rpm free · 1,000,000 rpm with billing
Accelerator: L40S
Regions: Virginia, US
License: Apache-2.0

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