Foglamp vs Zero: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Foglamp and Zero — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Foglamp

Freemium

Observability for AI agents: see the cost, latency, traces, and output quality of every LLM call with one SDK.

Two-Line SDK Instrumentation: Wrap your model once and every generateText / streamText call is automatically instrumented.
Per-Agent Spans and Spend: View per-agent spans, latency, and spend with the full call flow across orchestrator, researcher, writer, and critic.
Evals: Score production traffic with code checks and LLM judges, including PII checks and pass-rate scoring.
Distributed Traces: Waterfall every run with the exact prompt and response captured per span.
Alerts: Set threshold rules on cost, latency, and error rate to catch problems early.
Cost Intelligence: Know exactly what every call costs broken down by model, agent, and customer.

Catching Cost Regressions: Detect a sudden 10x cost spike days after shipping before it drains the budget.
Debugging Bad Output: Trace the exact prompt and response that produced a wrong or hallucinated answer.
Quality Gating with Evals: Continuously score production traffic to verify agents stay accurate and PII-safe.
Latency Monitoring: Alert when per-agent latency crosses a threshold so slow responses are caught fast.
Per-Customer Spend Analysis: Break down LLM spend by customer and model to understand unit economics.

Vercel Labs

Free

An experimental graph-first programming language where agents edit a compiler-checked program graph instead of raw source text.

Graph as the Program: A compiler-owned semantic graph of symbols, calls, types, effects and node IDs is the source of truth, so agents reason over program structure rather than parsing and regenerating text.
Hash-Guarded Patches: Every edit carries an expected graph hash and expected field values, so a stale or conflicting patch is rejected before it reaches the store instead of silently corrupting the program.
Compiler in the Loop: Shape, type, stale-state and repository metadata checks run as part of applying a patch, collapsing the write-build-test-inspect cycle into a single checked operation.
Readable Text Projections: The graph renders to reviewable .0 source projections so humans can read diffs, audit what an agent changed and make rare manual edits.
Structured JSON Diagnostics: The compiler emits machine-readable diagnostics rather than prose error text, so agents can act on failures without parsing terminal output.
Explicit Effects via World: Side effects are passed through an explicit World capability parameter, making what a function can touch visible in its signature.
Runtime Constraints by Design: Targets token efficiency, low memory, fast startup, fast builds, low latency and zero dependencies rather than relaxing systems goals for agent ergonomics.
Query and Patch CLI: zero init, zero query, zero patch and zero run give agents a direct command surface over the graph, with agent skills carrying the graph discipline instead of rigid human prompts.

Reliable Agent Code Edits: Let a coding agent make semantic changes that are rejected outright if its view of the program is stale, instead of producing plausible-looking but broken text diffs.
Reducing Agent Token Spend: Query the specific symbols, types and nodes relevant to a task rather than feeding whole files into context on every turn.
Outcome-Driven Development: Describe a desired result in conversation — add auth, fix a failing route, build a CRM API — and review the resulting projection rather than writing the code.
Auditable AI-Written Code: Review what changed through readable .0 projections and graph hashes, keeping a human checkpoint over agent-authored programs.
Language and Tooling Research: Explore what a compiler and program representation look like when machine editors, not human typists, are the primary writers.
Sandboxed Experimentation: Prototype agent-driven codebases in an isolated environment where breaking changes and pre-1.0 churn are acceptable.