odnoga vs everyone else
Honest comparison. We win where multi-tenant SaaS lives — prompts, per-end-user billing, Supabase-native. We name competitors where it matters.
Choosing between odnoga and calling a vendor directly? See the honest breakdown
Interactive filter — focus on what actually differs.
vs. Portkey · OpenRouter · LiteLLM
All four route requests across providers. Only odnoga ships the SaaS business layer: tenant identity, per-end-user Stripe billing, prompt A/B with rollback, Supabase-native SDK.
Swipe the table sideways →
| Feature | Portkey | OpenRouter | LiteLLM | odnoga |
|---|---|---|---|---|
| Multi-provider single API | ||||
| Drop-in OpenAI compatible | ||||
| Routing with fallback | per request | |||
| Provider keys stored server-side | ||||
| Prompt versions you can roll back | — | version param | ||
| Per-tenant scopes with budgets | ||||
| Per-end-user attribution | — | in progress | ||
| MCP server for agents | — | |||
| Billing your customers on your Stripe account | — | — | — | |
| Supabase Edge Function pattern with verified identity | — | — | — |
— means not established: we have not verified it, which is not the same as saying it is missing. Competitor rows last reviewed 2026-09-20.
vs. LangSmith · Langfuse · Helicone
These are SDKs you bolt on. odnoga is the gateway itself — prompts, tracing, routing, billing and tenant identity are one system, not four.
Swipe the table sideways →
| Feature | LangSmith | Langfuse | Helicone | odnoga |
|---|---|---|---|---|
| Request logging and traces | ||||
| Prompt registry with versions | — | |||
| Prompt A/B by traffic share | — | — | — | |
| Rollback by flipping a label | — | — | — | |
| Sits in the request path (routes traffic) | ||||
| Routing across providers with fallback | — | — | partial | |
| Per-tenant identity and scopes | — | — | — | |
| Billing your customers on your Stripe account | — | — | — | |
| Supabase Edge Function pattern | — | — | — |
— means not established: we have not verified it, which is not the same as saying it is missing. Competitor rows last reviewed 2026-09-20.
And the broader picture
Swipe the table sideways →
| Feature | Typical LLM proxy | Cloud provider AI | odnoga |
|---|---|---|---|
| Per-tenant billing and limits | |||
| Vendor key storage and rotation | partial | ||
| Prompt versions with A/B | partial | ||
| Same inputs compared across models | partial | ||
| Capture a real call into a test case | |||
| MCP server for agents | |||
| Routing with automatic fallback | manual | ||
| Drop-in OpenAI compatible | |||
| Many providers behind one API | |||
| Billing your customers on Stripe | |||
| Self-host option | sometimes | Enterprise |
— means not established: we have not verified it, which is not the same as saying it is missing.
Compare on evidence from your own product
The differentiator is not a checkbox in a feature table. In odnoga, one reviewed production call can become a frozen test case; the same prompt version and cases then run across 2–8 models, with responses, pass results, tokens, billed cost and latency in one dashboard.
vs. other AI gateways
They route, retry, log and version prompts — several do it well, and LiteLLM is open source if you want to run it yourself. odnoga does that as a managed service, and adds the one thing none of them does: when you are ready to charge your own customers for AI, the metering already there becomes their invoices, on your Stripe account. Use it for internal AI and never turn that on — plenty do.
vs. one cloud provider
A single vendor ties your model choice and your billing together. odnoga separates them: change the model with a string, and keep charging your customers the same way.
vs. building it yourself
A router, a key vault and a usage meter are a known amount of work, and plenty of teams build them. Billing is the part that keeps asking for attention afterwards: plans, proration, failed payments, refunds, and a per-customer cap that has to hold before the call goes out, not after the invoice arrives.
When NOT to pick odnoga
You would rather run and operate the gateway yourself. LiteLLM is open source and does most of this.
You run a single-tenant internal tool with one engineer. A bare provider SDK is simpler.
You need a self-hosted, fully offline runtime today. odnoga self-host is Enterprise-only.
Your bottleneck is model fine-tuning workflows. Use a training-focused platform — we route, we don't train.