Oxlo.ai vs Supermemory

Oxlo.ai and Supermemory are both inference engines & infra tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Oxlo.ai

Oxlo.ai is an inference hosting service offering an OpenAI-compatible API across 45+ open-source models, from DeepSeek R1 671B and Kimi K2.6 to Whisper and Kokoro TTS, under a flat-rate paid plan. Zero data retention and no training on your requests are stated guarantees — making it a credible option for teams handling regulated or sensitive data. The flat pricing story is the headline: the vendor's own cost calculator shows per-token competitors pulling ahead at low volume, so the math only tips in Oxlo.ai's favor once your monthly token spend is high enough. No self-hosted option exists, so teams with infrastructure mandates that require on-premises deployment are blocked. Community footprint is thin — no visible case studies or third-party benchmarks beyond what the vendor publishes.

Supermemory

Supermemory wraps memory, retrieval, user profiling, data connectors, and document extraction into one API so your agent doesn't reassemble context from scratch on every request. The retrieval layer claims sub-300ms latency using hybrid search with reranking, and the memory layer maintains a knowledge graph that merges contradictions and evolves facts over time rather than appending chunks blindly. Connectors to Slack, Notion, Drive, Gmail, GitHub, and S3 sync automatically — no ETL pipeline to maintain. The core memory engine is proprietary and hosted-only; self-hosting requires an enterprise agreement, so teams with strict data residency requirements hit a wall before they ship.

Attribute	Oxlo.ai	Supermemory
Pricing	Paid	Paid
Price	$80/month	$0 - $399+/mo
Free trial	1 days	No
Open source	No	Yes
Has API	Yes	Yes
Self-hosted option	No	No
Platforms	Web, API	Cloud-hosted (SaaS); MCP server; Browser plugins (Chrome); IDE integrations (Claude Code, Cursor, VS Code)
Released	—	2024
Pros	OpenAI-compatible API endpoint, so existing codebases pointing at OpenAI need only a base URL and key change to redirect inference — no SDK rewrite required. Flat monthly pricing absorbs token-volume spikes without changing your bill, which means a product going unexpectedly viral does not trigger an emergency finance conversation. Stated zero data retention and no training on customer requests, so teams handling regulated or sensitive data have a documented privacy basis to point auditors toward. Single API covers text, embeddings, image, audio transcription, TTS, and object detection models, which means one billing relationship and one authentication pattern instead of four separate vendor contracts. Kimi K2.6 benchmarks published on the page show competitive scores against GPT-5.4 and Claude Opus 4.6 on coding and agentic tasks, giving teams a credible high-capability model option without routing to proprietary frontier labs.	Knowledge graph memory that merges and contradicts facts across sessions, which means your agent doesn't tell a user something they already corrected two conversations ago. Sub-300ms hybrid search with reranking baked into the retrieval layer, so you avoid building and tuning a separate retrieval pipeline to hit production latency targets. Persistent user profiles that carry preference, behavior, and identity context across sessions, which means a support agent or personalized chatbot doesn't reset its understanding of the user on every ticket. Real-time connectors to Slack, Notion, Drive, Gmail, GitHub, and S3 with automatic sync, so your agent's memory reflects live changes in the tools your users actually work in — no manual import jobs to maintain. Multi-format extraction for PDFs, web pages, images, and audio consolidated into one provider, which means you don't wire together separate parsing services before you can ingest mixed document types.
Cons	The flat monthly pricing is only cheaper than per-token competitors once your volume is high — the vendor's own calculator shows Groq, Fireworks AI, and OpenRouter all coming in below the flat rate at 100K input tokens per month. Teams with modest or unpredictable workloads pay a premium for the pricing insurance. No self-hosted or VPC deployment option exists. Teams whose security policy or compliance mandate requires that model inference never leave their own infrastructure cannot use this service and will route to providers offering dedicated or on-premises deployment. The service is inference-only with no workflow tooling, agent framework, or built-in RAG pipeline. Teams expecting a managed end-to-end stack discover they are responsible for retrieval, memory, and orchestration layers entirely — at which point teams with limited backend capacity evaluate competitors that bundle those layers.	The core memory engine is not self-hostable without an enterprise agreement — teams with data residency requirements or strict policies against sending user memory to a third-party managed service cannot deploy this in production without negotiating a contract first, and most either wait on procurement or replace the memory layer with a self-managed vector store. The knowledge graph and memory update logic are proprietary and closed; when retrieval behaves unexpectedly — returning stale facts or failing to surface a contradiction — there is no source code to inspect. Teams debugging production retrieval issues work from API responses and vendor support, not from the system itself. The free tier is capped at defined token and query limits, meaning a team validating the tool at scale will exhaust the free tier before they have enough production data to make a confident architecture decision — at which point cost exposure begins before the build is complete. Agent frameworks that manage their own memory or context windows require explicit integration work to hand off to Supermemory rather than their native store; teams already deep in a framework with memory primitives — LangGraph, for example — often find the integration layer adds complexity that exceeds the benefit for their specific architecture and abandon Supermemory in favor of the framework's native memory tooling.

Bottom line

Supermemory is open source. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Oxlo.ai and Supermemory?

Oxlo.ai is Paid, while Supermemory is Paid and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Oxlo.ai better than Supermemory?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.