Router by Ramp vs Supernova: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Router by Ramp and Supernova — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Router by Ramp
Ramp
Ramp's LLM gateway routes each request to the cheapest model meeting your quality bar, cutting inference costs ~40% behind one endpoint and one bill.
Key features
- Cost-Aware Automatic Routing: Every request is matched to the lowest-cost model that still meets your performance requirements, reported to cut inference spend by about 40% on average.
- One Key for Every Model: Closed and open-source models from vetted providers sit behind a single endpoint, key and invoice.
- Rolling Strategy Updates: New cost-saving routing strategies and newly benchmarked default models roll in automatically without changing your integration.
- Score Versus Spend Reporting: Built-in benchmarking shows metric distributions and model summaries so you can see quality and cost side by side.
- Flex Tier Routing Share: A tunable split between default and flexible routing lets you dial how aggressively requests are shifted to cheaper models.
- US-Hosted Providers with ZDR: All vetted providers are US-hosted, with zero-data-retention options for sensitive workloads.
- Switchyard Integration: Works with Switchyard for model and provider routing, surfaced directly in the CLI's cost display.
- One-Command CLI Setup: Install and configure with a single curl command from agents.ramp.com, with an agent-friendly copy-paste flow.
Best for
- Trimming Production Inference Spend: Route high-volume, low-difficulty requests to cheaper models while keeping frontier models for the hard ones — Delphi reports a 92% model cost reduction across billions of tokens.
- Multi-Provider Consolidation: Replace separate OpenAI, Anthropic and open-model integrations with one endpoint and one bill.
- Model Benchmarking Before Migration: Test candidate models against your real workloads and compare score against spend before switching defaults.
- Finance and Engineering Alignment: Give CFOs a single, attributable AI spend line while engineers keep the best model for each workload.
- Compliance-Constrained Deployments: Keep inference on US-hosted providers with zero-data-retention options for regulated data.
- Agent Cost Control: Cap the runaway token spend of long-running agent loops by routing their routine steps to cheaper models automatically.
Supernova
Supernova
An encrypted Iceberg data lake with a built-in engine and MCP endpoint, so Claude and Codex can query every tool your company uses.
Key features
- MCP Endpoint for Claude and Codex: Point any MCP-speaking assistant at mcp.supernova.ai/mcp and every synced table becomes queryable in natural language.
- Encrypted Iceberg Lake: Open Apache Iceberg tables in object storage with table-level encryption, so the data stays in a portable open format you control.
- Zero-Copy Connections: Any engine that speaks Iceberg can read the lake directly, avoiding a second copy of your warehouse.
- Time Travel: Every table retains version history, so you can query the state of your data as of any earlier point.
- Built-In Frontier Models: Ask a question or describe a dashboard in plain language and Supernova generates the models and visualisations without a data team.
- TypeSQL: Schema-aware SQL that autocompletes across joins and type-checks before execution, catching errors the way a typed language would.
- Single-Binary CLI: One command-line tool connects sources, runs queries, tails live table changes and registers the MCP endpoint with Claude Desktop, from a laptop or CI.
- Git-Backed Dashboards: Models and dashboards are readable and writable through Git, putting analytics artefacts under normal version control.
Best for
- Conversational Revenue Analysis: Ask Claude which customers churned last quarter and why, with the answer computed over live Stripe and HubSpot tables.
- Warehouse Cost Reduction: Replace a multi-vendor pipeline-plus-warehouse stack with one usage-billed platform, which the vendor illustrates as $5,640/mo dropping to $540/mo for a hardware company.
- Dashboards Without a Data Team: Describe the dashboard you want in a sentence and have the models and charts generated for you.
- AI-Native Data Access Layer: Give internal agents a governed, encrypted single endpoint for company data instead of per-tool API integrations.
- Auditing Historical State: Use table version history to reconstruct what the numbers looked like before a pricing or schema change.
- CI-Driven Data Workflows: Drive connections, queries and change tailing from pipelines using the single CLI binary.