Torque vs Typesense
Torque serves the Typesense v30.1 API, so switching is a config change - and in our benchmarks it runs 9.5x faster. Typesense is the open-source original, with built-in high availability and a managed cloud.
Typesense is a popular open-source (GPL-3.0) search engine written in C++, with typo tolerance, faceting, vector search, built-in Raft high-availability clustering, and a managed Typesense Cloud. Torque is built to be API-compatible with it: it implements the Typesense v30.1 API - and because Typesense's current release, v30.2, is a bug-fix update on that same API, your existing schemas, queries, and client libraries keep working.
That makes this less an "alternative" and more a same-API choice. Torque is written in Rust, adds GPU-accelerated search and a built-in MCP server, and in our own benchmarks it runs 9.5x faster on queries and 7.8x on ingestion, on the same hardware. Typesense, in turn, is fully open source and offers built-in high-availability clustering, which Torque does not. The right pick depends on which of those matters most to you.
How they compare
| Torque | Typesense | |
|---|---|---|
| Foundations | ||
| Language | Rust, in-memory | C++, in-memory |
| Storage & persistence | In-memory index, persisted as memory-mapped segment files (roaring bitmaps, columnar) | In-memory index; raw documents stored on disk in RocksDB |
| Bulk ingestion | Binary TCP streaming protocol and binary file upload, plus HTTP import | HTTP JSONL import |
| License | Commercial (Truespar EULA) | Open source (GPL-3.0) |
| Deployment | Self-hosted | Self-hosted or Typesense Cloud |
| Client SDKs | Five (Python, Node.js, Go, Java, .NET); Typesense clients also work | Official Typesense clients |
| Search features | ||
| Typo tolerance | Yes (SymSpell) | Yes (Levenshtein) |
| Faceting & filtering | Yes (roaring bitmaps) | Yes |
| Vector & hybrid search | Yes (RaBitQ quantization) | Yes (float32) |
| Conversational & NL search | Yes (any OpenAI-compatible LLM) | Yes |
| GPU-accelerated search | Yes (CUDA) | No (Cloud offers GPU for embedding generation only) |
| Built-in MCP server for AI agents | Yes | No |
| Where Typesense leads | ||
| Open source | Commercial license | GPL-3.0, fully open source |
| High-availability clustering | Single-node | Built in (Raft-based) |
| Managed cloud | Self-hosted only | Typesense Cloud |
| Community & maturity | Focused, newer | Large, established community |
Based on Typesense's published documentation, license, and Typesense Cloud pricing as of July 2026. Torque figures reflect the current 0.6 release. Benchmark figures below are our own measurements - see the methodology on the performance page.
Performance
Because both engines implement the same API, we can benchmark them head to head on identical data and queries. On 309,000 real-world records (67 fields with nested objects, geo, and facets), measured over HTTP on the same hardware, CPU-only:
- 9.5x faster on the geometric mean of 15 query patterns, winning every pattern.
- 7.8x faster ingestion - 69,000 documents per second over Torque's binary TCP protocol, versus HTTP JSONL import into Typesense. Bulk loads over the binary file format are faster still.
- Biggest gaps on sorting, grouping, and faceting; smallest on plain text search, where both are already fast.
A search store, not a database
Torque and Typesense sit in the same place in your stack. Both keep their index in memory for speed, and both are secondary data stores: you keep the source of truth in your primary database and sync a searchable copy into the engine. Neither is meant to hold the only copy of your data.
They share the same limits, too. Because the index lives in RAM, neither is a good fit for high-volume application logs, where keeping the full dataset in memory gets expensive and instant search-as-you-type is not the point. For that, a system built for logs - such as Elasticsearch - is the better tool.
Where each one fits
Choose Typesense when a fully open-source (GPL-3.0) engine matters, you want built-in high-availability clustering, or you would rather run on managed Typesense Cloud with a large, established community behind you.
Choose Torque when you want the same Typesense API with more query throughput on our benchmarks, faster bulk loads over the binary ingest protocol, GPU-accelerated search, and a built-in MCP server for AI agents - running on your own hardware, with commercial support.
Migrating from Typesense
This is the easy one. Because Torque implements the Typesense v30.1 API, moving is a configuration change: point your existing Typesense clients at Torque and your schemas, queries, and integrations work unchanged. Re-index your data over the HTTP import or the faster binary protocol, and you are running.