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Namespaces let you scope experimentation and model usage to specific contexts, such as a customer, tenant, or platform. They support two key use cases:
  • Run different experiments per namespace: override a function’s default A/B test for specific contexts (e.g. customers).
  • Restrict models and credentials to a namespace: limit a model or API key to a specific context (e.g. a per-customer fine-tune that should only be served to that customer).

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Run different experiments per namespace

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Configure

By default, a function’s experimentation configuration applies to all inference requests. With namespaces, you can override this for specific customers or contexts. For example, imagine you’re A/B testing prompts and models across your customer base, but an enterprise customer (acme_corp) needs a variant with a prompt tailored to their brand voice. You can define separate variants with custom prompts for that customer and route them using a namespace:
tensorzero.toml
[functions.draft_email]
type = "chat"

# Two general-purpose variants
[functions.draft_email.variants.default_gpt]
type = "chat_completion"
model = "openai::gpt-5-mini"
templates.system.path = "functions/draft_email/default_gpt/system.minijinja"

[functions.draft_email.variants.default_claude]
type = "chat_completion"
model = "anthropic::claude-haiku-4-5"
templates.system.path = "functions/draft_email/default_claude/system.minijinja"

# Two variants tailored for `acme_corp`
[functions.draft_email.variants.acme_corp_gpt]
type = "chat_completion"
model = "openai::gpt-5-mini"
templates.system.path = "functions/draft_email/acme_corp/system.minijinja"

[functions.draft_email.variants.acme_corp_claude]
type = "chat_completion"
model = "anthropic::claude-haiku-4-5"
templates.system.path = "functions/draft_email/acme_corp/system.minijinja"

# Default: A/B test across all customers
[functions.draft_email.experimentation]
type = "static"
candidate_variants = ["default_gpt", "default_claude"]

# acme_corp: A/B test their custom variants
[functions.draft_email.experimentation.namespaces.acme_corp]
type = "static"
candidate_variants = ["acme_corp_gpt", "acme_corp_claude"]
This pattern also works for adaptive experiments. Here’s an example of a support_agent function that runs independent adaptive experiments for different customers:
tensorzero.toml
# Default: optimize using feedback from all inferences
[functions.support_agent.experimentation]
type = "adaptive"
candidate_variants = ["prompt_A", "prompt_B"]
metric = "user_rating"

# acme_corp: optimize using feedback from `acme_corp` inferences only
[functions.support_agent.experimentation.namespaces.acme_corp]
type = "adaptive"
candidate_variants = ["prompt_A", "prompt_B"]
metric = "user_rating"
Both experiments optimize for user_rating, but the default experiment uses feedback from all inferences while the acme_corp experiment only uses feedback from acme_corp inferences. This means each can converge on a different variant.

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Run inference

To use a namespace, pass it at inference time using the namespace parameter (or tensorzero::namespace in the OpenAI-compatible endpoint). For example: 
response = client.chat.completions.create(
    model="tensorzero::function_name=draft_email",
    messages=[...],
    extra_body={
        "tensorzero::namespace": "acme_corp",
    },
)
The namespace is automatically stored as the tensorzero::namespace tag on the inference record. This lets you filter and query inferences by namespace in the TensorZero UI and directly in the database. If a request provides a namespace that doesn’t have a specific configuration (e.g. "some_other_customer"), the default experimentation configuration is used. In other words, unknown namespaces don’t cause errors; they simply fall back to the default configuration.

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Restrict models and credentials to a namespace

Namespaces can also restrict which models and credentials (API keys) are available in a given context. This is particularly helpful when you have per-customer fine-tuned models or API keys that should only be used for the correct customer’s inferences.
tensorzero.toml
[models.acme_corp_ft]
routing = ["fireworks"]
namespace = "acme_corp"

[models.acme_corp_ft.providers.fireworks]
type = "fireworks"
model_name = "accounts/acme-corp/models/ft-v1"
TensorZero enforces that a namespaced model can only appear in experimentation configurations for its own namespace. Any inference request that uses a namespaced model without providing the matching namespace (or with a different namespace) will be rejected.