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Toto-2.0-313m

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Datadog/Toto-2.0-313m

313M params | 512 context | $0.00025 per forecast | Apache-2.0

Toto-2.0-313m is the main accuracy-oriented hosted Toto 2.0 checkpoint for observability telemetry and the recommended default of the family. At 313M parameters it sits high on the Toto 2.0 size ladder, pairing the new quantile interface with enough multivariate capacity to handle production monitoring workloads without jumping to a billion-parameter model.

It is a u-muP-scaled decoder-only transformer with alternating time/variate attention and a quantile output head, producing native probabilistic, multivariate forecasts across correlated metric streams. Toto 2.0 continues Datadog's observability-first pretraining line for sparse, high-dimensional telemetry, and Datadog positions the release as the current recommended zero-shot generation for BOOM-style infrastructure metrics.

On TSFM.ai this is the first Toto 2.0 checkpoint to reach for, and the designated replacement for the legacy Datadog/Toto-Open-Base-1.0. Drop down to Datadog/Toto-2.0-22m or Datadog/Toto-2.0-4m when latency, cost, or quick experimentation outweigh accuracy, and step up to Datadog/Toto-2.0-1B only for the highest-value workloads where richer multivariate telemetry structure justifies 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

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

Tags

datadogtoto-2observabilitymultivariatequantilerecommended

Specifications

Parameters
313M
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

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