ModelHub API vs Spanlens

ModelHub API and Spanlens 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.

ModelHub API

ModelHub is a hosted API gateway that puts 45 Chinese and global LLMs — DeepSeek V4, Qwen 3, GLM-4, Doubao, Kimi — behind a single OpenAI-compatible endpoint. You swap your base_url, keep your existing SDK, and your token bill drops. The vendor states prompts are never stored and payments run through Paddle under PCI Level 1 certification. The ceiling appears fast: no self-hosted option, no agentic tooling, no fine-tuning surface. Teams that need dedicated infrastructure or low-latency SLAs will exhaust what the service offers and contact the Enterprise tier — or leave.

Spanlens

Spanlens sits in front of your LLM provider via a single baseURL change, recording every call's cost, latency, tokens, and full request-response body with no SDK rewrite required. Agent runs surface as waterfall span trees so you can identify the one step consuming 80% of wall-clock time. The model recommender flags GPT-4o calls that look like classification tasks and shows the cost delta if you swap — with numbers from your own traffic, not benchmarks. The eval and experiment layer lets you replay a fixed dataset across prompt versions before you ship, so quality regressions don't surprise you in production. PII scanning and anomaly detection run at log time, which matters when sensitive data crosses the wire at 3 a.m. with nobody watching.

Attribute	ModelHub API	Spanlens
Pricing	Paid	Paid
Price	$15/month	$29/mo
Free trial	No	No
Open source	No	Yes
Has API	Yes	Yes
Self-hosted option	No	Yes
Platforms	Web, CLI, API, OpenAI-compatible SDK	Node.js, Python, Next.js, Edge, self-hosted
Pros	OpenAI SDK compatibility via a base_url swap, so existing codebases require no refactoring and teams avoid the integration cost of adopting a net-new client library. Per-token pricing on DeepSeek V4 Flash starting at $0.15/M tokens, which means high-volume workloads — batch summarization, large-scale code generation — that would exhaust an OpenAI budget stay economically viable. No Chinese phone number or regional payment method required, so international developers who hit identity-verification blocks on direct Chinese model APIs can provision access in minutes. Prompts are never stored and never used for model training, according to the vendor, so teams with baseline data-handling policies avoid the contractual exposure that comes with providers who retain inference data. 45 models behind one key, so switching from DeepSeek to Qwen or Doubao for a specific task is a model-name change in the request body — not a new vendor contract, new SDK, or new auth flow.	Proxy-layer instrumentation via a single baseURL change, so existing code requires no structural rewrite and every provider call is captured from day one rather than after a manual instrumentation sprint. Per-user and per-route cost attribution, which means you can identify the specific customer or endpoint burning disproportionate budget before it compounds across a billing cycle. Agent waterfall trace trees with critical-path highlighting, so a slow or expensive step in a multi-agent run is pinpointed in seconds instead of reproduced manually in a staging environment. Experiment runner replays a fixed dataset across prompt versions and models with quality, cost, and latency compared side by side, which means you ship with evidence that v8 is better than v7 rather than finding out the hard way in production. Self-hosted deployment via Docker Compose under MIT license, so teams with data residency or audit requirements can run the full platform without sending trace data to a third-party cloud.
Cons	No self-hosted or on-premise deployment option exists. Teams under data-residency mandates that prohibit routing prompts through third-party cloud infrastructure cannot use ModelHub at all — those teams go directly to self-hostable model weights via Ollama or a private cloud deployment. The service provides chat completion inference only, with no built-in tool-use framework or agent runtime. Teams building multi-step agents that branch based on tool output must wire a separate orchestration layer — LangChain, LlamaIndex, or equivalent — on top of ModelHub, meaning they are maintaining two systems from the first agent they ship. Latency is shared-infrastructure latency with no published p99 SLA outside the Enterprise tier. Production applications where response time is a user-experience constraint — real-time voice, interactive copilots — will hit unpredictable queuing during demand spikes and have no contractual recourse short of negotiating an Enterprise deal. The full model catalog and multiple API keys are gated behind paid tiers; the free credit covers evaluation only. Teams that prototype on free credit and then need concurrent key distribution for a multi-service architecture face a hard paywall before they finish scoping the project.	PII detection is regex-based and runs at log time as a flag — not a pre-storage redaction guarantee. Teams operating under HIPAA or SOC 2 controls where sensitive data must never reach a log store, even briefly, need a dedicated redaction layer upstream of Spanlens or a different architecture entirely. The LLM-as-judge eval scoring is a single 0–1 scalar per response. Teams needing structured, multi-criteria evaluation rubrics — for example, factual accuracy scored separately from tone and policy compliance — hit the ceiling of what the built-in scorer expresses and end up maintaining a custom eval harness alongside Spanlens. At high request volumes where the proxy layer adds measurable latency to every call, teams running latency-sensitive production paths at scale have moved to SDK-side instrumentation tools or full APM platforms with LLM plugins, where the observability path is out of band rather than in the critical path.

Bottom line

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

Frequently asked questions

What is the difference between ModelHub API and Spanlens?

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

Is ModelHub API better than Spanlens?

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.