AI Boost vs Spanlens

AI Boost 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.

AI Boost

MCP server for capturing and injecting developer expertise as searchable, reusable context for LLM agents.

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	AI Boost	Spanlens
Pricing	Paid	Paid
Price	—	$29/mo
Free trial	No	No
Open source	No	Yes
Has API	Yes	Yes
Self-hosted option	No	Yes
Platforms	Web (MCP server), integrates with Cursor, Claude Code, and generic MCP clients	Node.js, Python, Next.js, Edge, self-hosted
Pros	Solves real gap: agents get curated, structured expertise instead of noisy memory replays Private by default with strong commitments: never indexed, sold, or used for training Seamless integration: one MCP config, works across all agent clients and projects Semantic + keyword indexing ensures correct boosters surface at the right moments	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	Pricing model and tiers not clearly communicated on the vendor site Requires agents to support MCP protocol (limits compatibility to newer tools) Booster quality and relevance depend on human curation; poor capture = poor suggestions	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 AI Boost and Spanlens?

AI Boost 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 AI Boost 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.