Routing note: For ambiguous user intents, use the shared clarification templates in references/intent-clarification.md.

AI Gateway

Use TrueFoundry's AI Gateway to access 1000+ LLMs through a unified OpenAI-compatible API with rate limiting, budget controls, load balancing, routing, and observability.

When to Use

Access LLMs through TrueFoundry's unified OpenAI-compatible gateway, configure auth tokens (PAT/VAT), set up rate limiting, budget controls, or load balancing across providers.

When NOT to Use

• User wants to deploy a self-hosted model → deploying self-hosted models requires a TrueFoundry Enterprise account with a connected cluster. See https://truefoundry.com
• User wants to deploy tool servers → deploying workloads requires a TrueFoundry Enterprise account with a connected cluster. See https://truefoundry.com
• User wants to manage TrueFoundry platform credentials → prefer

  status

  skill; ask if the user wants another valid path

Overview

The AI Gateway sits between your application and LLM providers:

Your App → AI Gateway → OpenAI / Anthropic / Azure / Self-hosted vLLM / etc.
                ↑
         Unified API + Auth + Rate Limiting + Routing + Logging

Key benefits:

• Single endpoint for all models (cloud + self-hosted)
• One API key (PAT or VAT) instead of managing per-provider keys
• OpenAI-compatible — works with any OpenAI SDK client
• Rate limiting per user, team, or application
• Budget controls to enforce cost limits
• Load balancing across model instances with fallback
• Observability — request logging, cost tracking, analytics

Gateway Endpoint

The gateway base URL is your TrueFoundry platform URL +

/api/llm

{TFY_BASE_URL}/api/llm

Example:

https://your-org.truefoundry.cloud/api/llm

Authentication

Personal Access Token (PAT)

For development and individual use:

1. Go to TrueFoundry dashboard → Access → Personal Access Tokens
2. Click New Personal Access Token
3. Copy the token

Virtual Access Token (VAT)

For production applications (recommended):

1. Go to TrueFoundry dashboard → Access → Virtual Account Tokens
2. Click New Virtual Account (requires admin privileges)
3. Name it and select which models it can access
4. Copy the token

VATs are recommended for production because:

• Not tied to a specific user (survives team changes)
• Support granular model access control
• Better for tracking per-application usage

Calling Models

Python (OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    api_key="<your-PAT-or-VAT>",
    base_url="https://<your-truefoundry-url>/api/llm",
)

# Chat completion
response = client.chat.completions.create(
    model="openai/gpt-4o",  # or any configured model name
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
    max_tokens=200,
)
print(response.choices[0].message.content)

Python (Streaming)

stream = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Write a haiku about AI"}],
    stream=True,
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

cURL

curl "${TFY_BASE_URL}/api/llm/chat/completions" \
  -H "Authorization: Bearer ${TFY_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o",
    "messages": [{"role": "user", "content": "Hello!"}],
    "max_tokens": 200
  }'

JavaScript / Node.js

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "<your-PAT-or-VAT>",
  baseURL: "https://<your-truefoundry-url>/api/llm",
});

const response = await client.chat.completions.create({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "Hello!" }],
});

Environment Variables

Set these to use with any OpenAI-compatible library:

export OPENAI_BASE_URL="${TFY_BASE_URL}/api/llm"
export OPENAI_API_KEY="<your-PAT-or-VAT>"

Then any code using

openai.OpenAI()

Supported APIs

/chat/completions

/completions

/embeddings

/images/generations

/images/edits

/audio/transcriptions

/audio/translations

/audio/speech

/rerank

/batches

/moderations

Supported Providers

The gateway supports 25+ providers including:

openai/gpt-4o

openai/gpt-4o-mini

anthropic/claude-sonnet-4-5-20250929

google/gemini-2.0-flash

bedrock/anthropic.claude-3-5-sonnet

azure/gpt-4o

mistral/mistral-large-latest

groq/llama-3.1-70b-versatile

cohere/command-r-plus

together/meta-llama/Meta-Llama-3.1-70B

my-custom-model-name

Model names depend on how they're configured in your gateway. Check the TrueFoundry dashboard → AI Gateway → Models for exact names.

Adding Models & Providers

Currently done through the TrueFoundry dashboard UI:

1. Go to AI Gateway → Models
2. Click Add Provider Account
3. Select provider (OpenAI, Anthropic, etc.)
4. Enter API credentials
5. Select models to enable

Adding Self-Hosted Models (Cluster-Internal)

After deploying a self-hosted model:

1. Go to AI Gateway → Models → Add Provider Account
2. Select "Self Hosted" as the provider type
3. Enter the internal endpoint:

   http://{model-name}.{namespace}.svc.cluster.local:8000

4. The model becomes accessible through the gateway alongside cloud models

Security: Only register model endpoints that you control. External or untrusted model endpoints can return manipulated responses. Use internal cluster DNS (

svc.cluster.local

) for self-hosted models. Verify provider API credentials are stored securely in TrueFoundry secrets, not hardcoded.

Adding External OpenAI-Compatible APIs (NVIDIA, custom providers)

For externally hosted APIs that are OpenAI-compatible (e.g. NVIDIA Cloud APIs, custom inference endpoints), use

type: provider-account/self-hosted-model

auth_data

# gateway.yaml — External hosted API (e.g. NVIDIA Cloud)
- name: nvidia-external
  type: provider-account/self-hosted-model
  integrations:
    - name: nemotron-nano
      type: integration/model/self-hosted-model
      hosted_model_name: nvidia/nemotron-3-nano-30b-a3b
      url: "https://integrate.api.nvidia.com/v1"
      model_server: "openai-compatible"
      model_types: ["chat"]
      auth_data:
        type: bearer-auth
        bearer_token: "tfy-secret://<tenant>:<group>:<key>"

And in a virtual model routing target, reference it as

"<provider-account-name>/<integration-name>"

targets:
  - model: "nvidia-external/nemotron-nano"  # "<provider-account-name>/<integration-name>"

Apply with:

tfy apply -f gateway.yaml

WARNING:

provider-account/nvidia-nim

does not exist in the schema — do not use it. Use

provider-account/self-hosted-model

with

auth_data

for all external OpenAI-compatible APIs (as shown above).

Schema source of truth: For authoritative field names and types, read

servicefoundry-server/src/autogen/models.ts

in the platform repo. Do not guess field names from documentation alone.

Applying Gateway Config

Gateway YAML is applied directly with

tfy apply

# Preview changes
tfy apply -f gateway.yaml --dry-run --show-diff

# Apply
tfy apply -f gateway.yaml

Do NOT delegate gateway applies to a deployment skill. Gateway configs (

type: gateway-*

type: provider-account/*

tfy apply

Test after apply:

# Quick smoke test via curl
curl "${TFY_BASE_URL}/api/llm/chat/completions" \
  -H "Authorization: Bearer ${TFY_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nvidia-external/nemotron-nano",
    "messages": [{"role": "user", "content": "Hello!"}],
    "max_tokens": 50
  }'

Or via Python:

from openai import OpenAI
client = OpenAI(api_key="<PAT-or-VAT>", base_url=f"{TFY_BASE_URL}/api/llm")
resp = client.chat.completions.create(
    model="nvidia-external/nemotron-nano",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(resp.choices[0].message.content)

Note: One-off gateway config applies should use

tfy apply

directly. For CI/CD pipelines, integrate

tfy apply

into your existing automation.

Virtual Models & Load Balancing

Virtual models route requests across multiple model instances using a

gateway-load-balancing-config

"<provider-account-name>/<integration-name>"

Weight-Based Routing

name: chat-routing
type: gateway-load-balancing-config
rules:
  - id: weighted-chat
    type: weight-based-routing
    when:
      subjects: ["*"]
      models: ["openai/gpt-4o"]
    load_balance_targets:
      - target: "openai-main/gpt-4o"
        weight: 70
        fallback_candidate: true
        retry_config:
          delay: 100
          attempts: 1
          on_status_codes: ["429", "500", "502", "503"]
      - target: "azure-backup/gpt-4o"
        weight: 30
        fallback_candidate: true
        retry_config:
          delay: 100
          attempts: 1
          on_status_codes: ["429", "500", "502", "503"]

Latency-Based Routing

Automatically routes to the lowest-latency model (measures time per output token over last 20 minutes):

rules:
  - id: latency-chat
    type: latency-based-routing
    when:
      subjects: ["*"]
      models: ["openai/gpt-4o"]
    load_balance_targets:
      - target: "openai-main/gpt-4o"
        fallback_candidate: true
      - target: "azure-backup/gpt-4o"
        fallback_candidate: true

Priority-Based Routing

Routes to highest-priority healthy model with SLA cutoff (auto-marks unhealthy when TPOT exceeds threshold):

rules:
  - id: priority-chat
    type: priority-based-routing
    when:
      subjects: ["team:premium"]
      models: ["*"]
    load_balance_targets:
      - target: "openai-main/gpt-4o"
        priority: 0
        sla_cutoff:
          time_per_output_token_ms: 50
        fallback_candidate: true
      - target: "azure-backup/gpt-4o"
        priority: 1
        fallback_candidate: true

Sticky Sessions

Pin users to the same target for a duration:

rules:
  - id: sticky-chat
    type: weight-based-routing
    sticky_routing:
      ttl_seconds: 3600
      session_identifiers:
        - key: x-user-id
          source: headers
    load_balance_targets:
      - target: "openai-main/gpt-4o"
        weight: 50
      - target: "azure-backup/gpt-4o"
        weight: 50

Header Overrides Per Target

load_balance_targets:
  - target: "openai-main/gpt-4o"
    weight: 80
    headers_override:
      set:
        x-region: us-east-1
      remove:
        - x-internal-debug

Fallback Behavior

Fallback is configured per-target inside

load_balance_targets

• fallback_status_codes

  : defaults to

  ["401", "403", "404", "429", "500", "502", "503"]

• fallback_candidate: true

  marks a target as eligible for failover

• retry_config.on_status_codes

  controls which errors trigger retries

Apply

tfy apply -f gateway-load-balancing-config.yaml --dry-run --show-diff
tfy apply -f gateway-load-balancing-config.yaml

Note: Targets must be real catalog models, not nested virtual models.

Rate Limiting

Configure rate limits per user, team, model, or custom metadata using a

gateway-rate-limiting-config

name: rate-limits
type: gateway-rate-limiting-config
rules:
  - id: "team-rpm-limit"
    when:
      subjects: ["team:backend"]
      models: ["openai-main/gpt-4o"]
    limit_to: 20000
    unit: tokens_per_minute

  - id: "user-daily-limit"
    when:
      subjects: ["user:bob@example.com"]
      models: ["openai-main/gpt-4o"]
    limit_to: 1000
    unit: requests_per_day

  - id: "per-project-hourly"
    when: {}
    limit_to: 50000
    unit: tokens_per_hour
    rate_limit_applies_per: ["metadata.project_id"]

  - id: "global-fallback"
    when: {}
    limit_to: 500
    unit: requests_per_minute
    rate_limit_applies_per: ["user"]

Units:

requests_per_minute

requests_per_hour

requests_per_day

tokens_per_minute

tokens_per_hour

tokens_per_day

rate_limit_applies_per

user

model

virtualaccount

metadata.<key>

tfy apply -f gateway-rate-limiting-config.yaml

Budget Controls

Enforce cost limits per user, team, or metadata using a

gateway-budget-config

name: budget-controls
type: gateway-budget-config
rules:
  - id: "team-monthly-budget"
    when:
      subjects: ["team:engineering"]
    limit_to: 5000
    unit: cost_per_month
    budget_applies_per: ["team"]
    alerts:
      thresholds: [75, 90, 100]
      notification_target:
        - type: email
          notification_channel: "budget-alerts"
          to_emails: ["lead@example.com"]

  - id: "user-daily-budget"
    when: {}
    limit_to: 100
    unit: cost_per_day
    budget_applies_per: ["user"]

  - id: "project-daily-budget"
    when:
      metadata:
        environment: "production"
    limit_to: 200
    unit: cost_per_day
    budget_applies_per: ["metadata.project_id"]

Units:

cost_per_day

cost_per_week

cost_per_month

budget_applies_per

user

model

team

virtualaccount

metadata.<key>

Alerts: Configure threshold percentages with email, Slack webhook, or Slack bot notifications.

tfy apply -f gateway-budget-config.yaml

Observability

Request Logging

All gateway requests are logged with:

• Input/output tokens
• Latency (TTFT, total)
• Cost
• Model and provider
• User identity
• Custom metadata

Custom Metadata

Tag requests with custom metadata for tracking:

response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Hello"}],
    extra_headers={
        "X-TFY-LOGGING-CONFIG": '{"project": "my-app", "environment": "production"}'
    },
)

Analytics

View usage analytics in TrueFoundry dashboard:

• Requests/minute per model
• Tokens/minute per model
• Failures/minute per model
• Cost breakdown by model, user, team

OpenTelemetry Integration

Export traces to your observability stack:

• Prometheus + Grafana
• Datadog
• Custom OTEL collectors

Guardrails

For content filtering, PII detection, prompt injection prevention, and custom safety rules, use the

guardrails

MCP Gateway Attachment Flow

If a user has already deployed a tool server and wants to attach it to MCP gateway:

1. Verify deployment status and endpoint URL via the TrueFoundry dashboard
2. Register the endpoint as an MCP server (

   mcp-servers

   skill)
3. Confirm registration ID/name and share how to reference it in policies

Framework Integration

The gateway works with popular AI frameworks:

LangChain

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="openai/gpt-4o",
    api_key="<your-PAT-or-VAT>",
    base_url="https://<your-truefoundry-url>/api/llm",
)

LlamaIndex

from llama_index.llms.openai import OpenAI

llm = OpenAI(
    model="openai/gpt-4o",
    api_key="<your-PAT-or-VAT>",
    api_base="https://<your-truefoundry-url>/api/llm",
)

Cursor / Claude Code / Cline

Configure the gateway as a custom API endpoint in your coding assistant settings:

• Base URL:

  {TFY_BASE_URL}/api/llm

• API Key: Your PAT or VAT

Presenting Gateway Info

When the user asks about gateway configuration:

AI Gateway:
  Endpoint: https://your-org.truefoundry.cloud/api/llm
  Auth:     Personal Access Token (PAT) or Virtual Access Token (VAT)

Available Models (check dashboard for current list):
| Model Name        | Provider     | Type        |
|-------------------|-------------|-------------|
| openai/gpt-4o     | OpenAI      | Cloud       |
| my-gemma-2b       | Self-hosted | vLLM (T4)   |
| anthropic/claude   | Anthropic   | Cloud       |

Usage:
  export OPENAI_BASE_URL="https://your-org.truefoundry.cloud/api/llm"
  export OPENAI_API_KEY="your-token"
  # Then use any OpenAI-compatible SDK

<success_criteria>

Success Criteria

• The user can call LLMs through the gateway endpoint using an OpenAI-compatible SDK or cURL
• The user has a valid authentication token (PAT or VAT) configured for gateway access
• The agent has confirmed the target model name is available in the user's gateway configuration
• The user can verify successful responses from the gateway with correct model output
• The agent has provided working code snippets tailored to the user's language and framework
• Rate limiting, budget controls, or routing are configured if the user requested them

</success_criteria>

Composability

• Deploy model first: Deploy a self-hosted model (requires TrueFoundry Enterprise), then add to gateway
• Need API key: Create PAT/VAT in TrueFoundry dashboard → Access
• Rate limiting: Configure in dashboard → AI Gateway → Rate Limiting
• Routing config: Apply routing YAML directly with

  tfy apply

  ; for CI/CD pipelines, integrate

  tfy apply

  into your automation
• Tool servers: Deploy tool servers to your infrastructure, then register in gateway
• Check deployed models: Check the TrueFoundry dashboard to see running model services
• Benchmark through gateway: Use your preferred load-testing tool against gateway endpoints

Error Handling

401 Unauthorized

Gateway authentication failed. Check:
- API key (PAT or VAT) is valid and not expired
- Using correct header: Authorization: Bearer <token>

403 Forbidden

Model access denied. Your token may not have access to this model.
- PATs inherit user permissions
- VATs only have access to explicitly selected models
- Check with your admin to grant model access

429 Rate Limited

Rate limit exceeded. Options:
- Wait and retry (check Retry-After header)
- Request higher limits from admin
- Use load balancing to distribute across providers

502/503 Provider Error

Upstream provider error. The gateway will automatically:
- Retry on configured status codes
- Fallback to alternate models if routing is configured
If persistent, check provider status page or self-hosted model health.

Model Not Found

Model name not found in gateway. Check:
- Exact model name in TrueFoundry dashboard → AI Gateway → Models
- Provider account is active and model is enabled
- Your token has access to this model