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

A side-by-side comparison of Foglamp and Paritok — 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.

Freemium

Non-destructive compression gateway that drops between coding agents and LLMs to cut input tokens by up to 85% without changing the agent.

Drop-In Gateway: One environment variable (ANTHROPIC_BASE_URL) reroutes your agent through Paritok — no agent, prompt, or tool changes.
Tool Schema Compression: 46-schema tool blocks (~29K tokens) drop to ~8K per turn by keeping relevant tools and stubbing the rest, frozen per conversation for cache stability.
Code-Native 4B Model: A 4B compression model trained on 45K real agent trajectories keeps identifiers, paths, and errors while shrinking file reads and outputs to ~26% of original.
read_original Recall: Every compressed segment is tagged; the agent asks read_original(ref) and gets the exact bytes locally without spending an extra turn.
Stale History Summarization: Turns beyond a configurable recent window get summarized once when your context budget fills, so recent turns stay pristine and overflows drop to zero.
Multi-Agent Compatibility: Works today with Claude Code, Cursor, Codex, OpenHands, and any OpenAI-compatible upstream — Anthropic and OpenAI both supported.
Compounding Savings: Saved share grows across a session — 25% at 1 turn, 54% at 10 turns, 63% at 20 turns — against a 96,500-token baseline.
Open Weights and Benchmark: SWE-bench Lite floor of 86.5% quality retained at 25.7% compression rate, with weights and training pipeline published.

MCP-Heavy Workflows: Cut input bills for agents that ship 70+ MCP tool schemas on every turn.
Long Coding Sessions: Run 3× longer coding-agent sessions before context saturation forces a hard compact.
Bill Reduction: Estimate 54% off input tokens on a 5-developer team at 20-turn Claude Sonnet sessions (~$6,550/year).
Self-Hosted Privacy: Route agent traffic through your own hardware with no data leaving your network — 8GB GPU is enough.
Enterprise Cost Governance: Add compression at the gateway layer so all coding agents on the team benefit without engineering per-agent.
Cursor/Codex/Claude Code Fleet: Standardize compression across a mixed toolchain of coding agents behind one gateway.