LangWatch AI Gateway
Gateway that puts every LLM call through one endpoint on the customer's own provider keys, with per-person, team, project and customer spending budgets, provider fallback, a trace for every call and webhooks for billing end customers.
Find alternatives to LangWatch AI Gateway
What it does
- Model inference
- Evaluation and observability
Sources
- Pricing
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"$6 per 100,000 calls, the same rate as LangWatch tracing"; tokens are billed by the customer's own providers.
- Description
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Official documentation · 18 Oct 2026
"One endpoint for every model and coding agent, with a spending limit for each person, team and customer."
- What it does
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Official documentation · 18 Oct 2026
"A budget set to block refuses it with HTTP 402 and the code budget_exceeded"; fallback: "Turn fallback on for a key, and a 5xx, a 429 or a timeout sends the same request to the next provider that serves the model, up to 3 attempts."
- Sold within
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The gateway has its own rate: "Fixed $6 per 100,000 calls. Zero markup on tokens." and a "Sign up" button.
- Name
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Official documentation · 18 Oct 2026
Menu entry "AI Gateway"; page heading "Put every AI call in your company on a budget"
- URL
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Official documentation · 18 Oct 2026
The product's own page, linked from the Platform menu as "AI Gateway" (marked New).
- Deployment
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Official documentation · 18 Oct 2026
"It runs apart from the LangWatch app, remembers the keys it has seen and keeps accepting them for up to 6 hours while the app is unreachable."
- Interfaces
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Official documentation · 18 Oct 2026
"Set the base URL of the OpenAI or Anthropic SDK to the gateway and use a LangWatch key as the API key."
Also from LangWatch
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LangWatch LLM observability Developer tool
LangWatch LLM observability traces model calls, tool calls and retrieval steps while displaying costs, latency and errors.
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LangWatch Instant Evals Application
A LangWatch component that evaluates stored agent conversations, traces, and LLM calls against questions expressed in plain language.
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Langy AI agent
LangWatch's automated AI engineer: it reads production traces, clusters failures, writes tests and opens a pull request with CI checks, running on the customer's own model keys; you review and merge.
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