ML Platform

Use when The widest provider coverage, deployed the least committal way.

Skip when You want routing policy to live in a SaaS console.

Use when A managed gateway with guardrails and analytics already bundled.

Skip when Prompts cannot leave your infrastructure.

Use when Edge caching and latency dominate, on Cloudflare already.

Skip when Requests must not transit a third party's network.

Use when One key and one bill across many hosted models, while experimenting.

Skip when You need self-hosting or per-provider SLAs.

Use when Routing tuned on your own traffic rather than a public benchmark.

Skip when You want the routing rule to be plain config you can read.

Use when A Kubernetes shop that already runs Envoy and needs the gateway in-cluster.

Skip when You want a gateway configurable without a data plane.

Use when A high-throughput OpenAI-compatible proxy with a small footprint.

Skip when You need the breadth of a full routing stack.

Use when Cutting cost by matching query difficulty to model size, with a learned router.

Skip when You cannot evaluate the quality you would be trading away.

Use when Routing and prompt optimisation tuned on your own traffic.

Skip when Self-hosting or auditability is a requirement.

Use when Gateway, observability and guardrails as one managed deployment.

Skip when You want to adopt the pieces independently.

Use when Work spanning minutes or hours that must survive restarts and retries.

Skip when You need it live in a day — the adoption cost is real.

Use when Durable step functions in TypeScript, without running the engine.

Skip when You are not working in TypeScript.

Use when Long-running background jobs that must survive a deploy.

Skip when You need more than TypeScript.

Use when Data and ML pipelines framed as assets with explicit dependencies.

Skip when You want a task queue rather than a lineage-aware orchestrator.

Use when Python-native flows with a managed cloud option behind them.

Skip when You need deep lineage modelling.

Use when Scheduled batch data engineering, where DAGs are the shared language.

Skip when You are building a latency-sensitive product — it was not designed for one.

Use when Durable execution with a genuinely low-latency stateful API surface.

Skip when You need the largest ecosystem around the engine.

Use when Durable workflows as ordinary Python functions and decorators.

Skip when You want a separate service to operate.

Use when Hosting stateful containers that fits durable agent workers.

Skip when You want to own the infrastructure layer.

Use when Serverless GPUs and containers for batch inference and scheduled jobs.

Skip when Data locality rules forbid ephemeral compute.

Use when Prompt versioning and evaluation run by a product team.

Skip when Prompts or data cannot be sent to a vendor.

Use when A prompt registry with request logging and regression tests.

Skip when You need self-hosting.

Use when Open-source prompt management with a playground and versioning.

Skip when You need eval depth rather than prompt ergonomics.

Use when Designing, building and deploying ML systems as a discipline.

Skip when You only need LLM-specific material.