A category of “AI gateway” middleware is consolidating around a clear set of production problems: multi-provider routing, cost tracking, prompt versioning, latency monitoring, and eval management. Portkey, Helicone, and Braintrust are the most visible players; LangSmith and Weights and Biases are adjacent from the ML tooling side.
The problems these products solve are real. Most production AI applications end up calling multiple providers (for redundancy, cost optimization, or capability reasons), and the operational overhead of managing that across a bespoke integration layer is significant. Gateway products abstract the provider API surface, add retry logic, route traffic based on latency or cost, and provide the observability that lets teams actually understand what their AI application is doing in production.
What distinguishes them: Portkey is the most provider-agnostic, supporting 100+ models with a consistent API surface and sophisticated routing rules. Helicone is the simplest to instrument — one line proxy change — and has the best cost dashboard for teams primarily optimizing spend. Braintrust is more focused on eval management and prompt iteration, sitting closer to the development workflow than pure production observability.
The market pressure they’re responding to: as AI becomes a production dependency rather than an experiment, the tooling requirements converge on what exists for other production services — distributed tracing, alerting, cost attribution, change management. Gateway products are porting those expectations to the LLM context.
The risk for standalone gateway players: the major cloud providers are building equivalent functionality into their AI platforms (AWS Bedrock routing, Azure AI Foundry), and OpenAI’s enterprise tier includes some of this natively. The window for independent gateway products to capture enterprise contracts is probably 18-24 months before the platform players consolidate the space.