LLM gateways

An LLM gateway sits between your app and many model providers. Your app sends a prompt and optional settings to the gateway over an API or SDK. The gateway standardizes that request into a common format so you do not have to code to each provider’s quirks.

Before calling a model, the gateway applies policies. It authenticates the request, rate limits, and may redact PII. It can inject prompt templates, attach system instructions, and enforce schemas so outputs come back as valid JSON. Some gateways add retrieval by calling search or crawl tools such as Tavily or Exa, and can fetch internal data via services like Hasura.

Next, the gateway routes the call to a model. It can target providers such as Anthropic, Cohere, Mistral, Fireworks AI, Replicate, or Hugging Face based on rules like latency, capability, or content policy. Products such as ElseLane focus on automatic failover across providers, while platforms like Neon include an AI gateway as part of a broader backend.

During generation, it can stream partial tokens to your app. If a call fails or times out, the gateway retries or falls back to another model. It can also enable tools or functions and orchestrate multi step chains.

After the model responds, the gateway post processes the output. It can check safety filters, reformat text, enforce length or JSON schemas, and cache results. Finally, it logs traces, prompts, and metrics for observability and evaluation, and returns the structured result to your app.

Build one API for many models. A developer ships a chat assistant that can switch between Anthropic, Cohere, and Mistral without changing app code.

Keep assistants reliable. An operations team adds failover with ElseLane so customer chats keep working when a provider is unavailable.

Unify research and retrieval. An agent routes queries through Tavily or Exa for fresh context, then asks a model via the same gateway call.

Standardize outputs. A content team turns messy chats into structured briefs and reports that conform to a schema, similar to how Xeditai focuses on polished, structured documents.

Prototype and compare models. A product manager runs A or B tests across Fireworks AI, Replicate, and Hugging Face to see which prompt and model pairing works best for a task.

Power multi model playgrounds. Teams back internal chat hubs like ChatPlayground AI or connectors such as 3sparks with a gateway so users can try different models through one account.

• Software engineers and app developers: Get a single API, SDKs, and tooling for prompts, streaming, retries, and schema enforcement, which speeds up shipping features.
• Machine learning and data teams: Run evaluations, compare models, and log traces to improve prompts, safety, and grounding.
• Platform and infrastructure teams: Centralize auth, rate limits, quotas, and routing policies across business units and environments.
• Security and compliance teams: Apply PII redaction, content filters, and audit logging in one place, independent of the chosen provider.
• Product managers and analysts: Experiment with prompts and models, run A or B tests, and review analytics without changing the app code.
• Startups and agencies: Use a gateway to avoid lock in and switch models as needs evolve, while keeping one integration.

Vendors usually combine usage based billing with plan tiers and enterprise options.