The first case is “I want to see what my LLM is doing.” Helicone's product is the analytics dashboard: every request as a row, expandable to see the full prompt and response, with latency, cost, and token counts attached, and filters across keys, users, models, and time. If your team needs to debug prompts in production, answer “what did the model say at 3am yesterday,” and compute cost per feature or per customer, Helicone is the tool built for that and InferAll is not in the conversation.
The second case is caching as a cost lever. Helicone's cache layer — exact-match and semantic — can cut costs meaningfully on repetitive workloads. Chatbots with similar queries, batch evals re-running on the same fixtures, any workload where a non-trivial share of calls are substantially the same. InferAll has no cache today, so the same requests hit the upstream every time. If your traffic is cache-friendly and the savings would be real, Helicone is the better pick on cost grounds alone.
The third case is “I already have my provider keys and my budget; I just need instrumentation.” Helicone proxies your existing OpenAI, Anthropic, and Google relationships — you keep the provider contracts, Helicone instruments the calls. If you don't want to change vendors, Helicone slots in without disrupting the upstream relationships you've already negotiated.
Sessions, user-level views, and the evals features round out the analytics story. We're not going to compete on that surface in 2026 and we're not going to pretend we're close.
And one more case worth naming: if you already have a team that knows Helicone, has wired their alerts into it, and has a dashboard rhythm that's working — the switching cost of moving to a different gateway isn't zero. Operational familiarity is a real asset. If the status quo isn't broken, the marginal feature delta of any new gateway probably doesn't justify the migration. Pick a gateway change when something forces it, not because a comparison page suggested it might be interesting.