Moss
In-process semantic search that returns retrieval results in under 10ms, built for voice agents, copilots and on-device AI.
If retrieval is what makes your voice agent feel sluggish, Moss attacks the problem at the architecture level rather than by tuning a remote vector store — the 3.1ms P50 measured against 351.8ms for ChromaDB and 432.6ms for Pinecone is a gap you can hear in a conversation. It is not a Pinecone replacement for batch analytics or multi-region replication; it is a retrieval layer for workloads where latency is on the critical path. Pick it with one specific low-latency job in mind. If you need managed multi-region infrastructure and heavy analytics over millions of documents, Qdrant or Pinecone remain the more natural fit.
Verified 4d ago · liveness 78/100 · cite: rightaichoice.com/tools/moss
- Voice AI developers who need sub-10ms context retrieval between user speech and LLM inference
- Teams building real-time copilots where a 300ms retrieval hop is visible to users
- On-device or edge AI apps that need offline semantic search via WebAssembly
- Regulated teams in healthcare or finance needing local query handling with SOC 2 and HIPAA paths
- Batch or offline retrieval jobs where a 350ms query is irrelevant
- Teams that need multi-region managed infrastructure and heavy analytics over millions of documents
- Cloud-only shops that do not want any retrieval running in-process or on-device
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Skip Moss if you need a managed multi-region vector database with heavy analytics over millions of documents, or if you are not writing Python or TypeScript.
Every paid plan is priced as a base fee plus usage, so the $30/mo Hobbyist and $200/mo Start-Up figures are floors, not ceilings.
The Developer tier starts at $0/mo plus usage, which puts Moss below managed vector databases that bill for always-on clusters — but Moss's paid tiers are base-plus-usage, so the $30/mo Hobbyist and $200/mo Start-Up rates are entry points rather than fixed bills. Against a managed cloud RAG pipeline, the vendor claims roughly 20% lower cost because there is no separate embedding API call on the hot path.
In short
Moss — In-process semantic search that returns retrieval results in under 10ms, built for voice agents, copilots and on-device AI. Best for Voice AI developers who need sub-10ms context retrieval between user speech and LLM inference, Teams building real-time copilots where a 300ms retrieval hop is visible to users, On-device or edge AI apps that need offline semantic search via WebAssembly. Free to start; paid plans from $30/mo.
What's new in Moss
Checked 4 days agoAcross the latest 1 update: 1 news mention.
What people actually say about Moss — is it worth it?
We scanned public community sources for Moss on Jul 6, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Moss? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Sub-10ms end-to-end semantic search (3.1ms P50 on 100K documents)
- Hybrid search combining semantic and keyword retrieval
- Runs in browser, edge, device, or cloud via WebAssembly
- Rust-based search runtime compiled to WebAssembly
- Real-time index updates: add, delete, and update documents
- Metadata filtering including geo support
- Cloud fallback when a local index is unavailable
- Continuous Sync Engine keeps indexes current
- Offline querying once an index is loaded, no network required
- Multi-index query via query_multi_index (Python SDK v1.1.0)
- Bulk index lifecycle management: load_indexes and unload_indexes
- Results tagged with source index_name for multi-index routing
- Rust-implemented embedding computation for built-in models
- Python and TypeScript SDKs (pip install moss / npm install @moss-dev/moss)
- Founding Agent, a pre-built voice AI agent demo with website crawl and PDF/DOCX knowledge ingestion
About Moss
Moss is a real-time semantic search engine that runs retrieval inside your own agent process instead of behind a network call to a hosted vector database. You install the Python or TypeScript SDK (`pip install moss`, `npm install @moss-dev/moss`), create a project key, and add documents with a few lines of code; the compiled WebAssembly runtime then executes queries in memory. Vendor-published benchmarks on 100K documents put Moss at 3.1ms P50 and 5.4ms P99, against 351.8ms / 538.5ms for ChromaDB, 597.6ms / 771.4ms for Qdrant, and 432.6ms / 934.2ms for Pinecone. The vendor also claims about 20% lower cost than a cloud RAG pipeline built on a vector DB plus an embedding API. The target buyer is a developer shipping a real-time AI system where retrieval sits on the critical path: a voice agent that has to fetch the caller's order status mid-sentence, a copilot surfacing internal documentation, or a mobile app that needs offline search. Moss compiles to WebAssembly so the same index runs in the browser, on mobile and desktop devices, at the edge, or in the cloud, with indexes syncing automatically when connectivity returns. Capabilities include hybrid semantic plus keyword search, metadata filtering with geo support, cloud fallback, a continuous sync engine, multi-index querying via `query_multi_index` (Python SDK v1.1.0), and bulk index lifecycle management with `load_indexes` / `unload_indexes`. Built-in embedding computation is implemented in Rust. It slots into an existing stack rather than replacing it: you keep your LLM provider, your voice layer (LiveKit, Pipecat, ElevenLabs, VAPI), and your frontend, and swap only the retrieval layer. The engineering blog documents the decision to write the search runtime in Rust and compile it to WebAssembly. The trade-off is that you give up the managed multi-region infrastructure and analytics surface of services like Pinecone or Qdrant in exchange for latency measured in single-digit milliseconds.
Behind the Verdict
Moss makes a clear architectural argument and backs it with a published benchmark script: keep retrieval in the same process as the agent, and you remove the network hop that costs 100–500ms before your LLM can even start generating. On 100K documents the vendor measures 3.1ms P50 / 5.4ms P99 against 351.8ms / 538.5ms for ChromaDB, 597.6ms / 771.4ms for Qdrant, and 432.6ms / 934.2ms for Pinecone. For a voice agent that has to answer "when is my appointment?" mid-conversation, that difference is the difference between a natural pause and an awkward one — the homepage's own demo shows the reschedule exchange resolving against a 4ms retrieval. The engineering is more than a wrapper. The search runtime is written in Rust and compiled to WebAssembly, which is what lets the same index run in a browser, on a phone, at the edge, or in the cloud, and it is why built-in embedding computation can happen locally rather than via an embedding API call on the hot path. The vendor has published a case study on Aside running AI memory across 80,000+ devices, 113M documents, 24.8B tokens and 150+ countries, which is the kind of production footprint that distinguishes this from a weekend prompt UI. Recent changelog entries show active development: Python SDK v1.14.0 added a client-level `cache_path` (with no cache_path set at either level, indexes are not cached on disk — worth knowing), the Portal onboarding was rebuilt so you can build and search a first index without leaving sign-up, and the Founding Agent knowledge tab now accepts website crawls plus up to 10 PDF or DOCX files at 20MB each (50MB total, OCR on paid plans). Where it does not fit: batch or offline retrieval jobs where a 350ms query is irrelevant, teams that need the managed multi-region footprint and analytics of a full vector database, cloud-only shops that do not want anything running in-process, and anything non-technical — you will be writing Python or TypeScript. Moss is a retrieval and infrastructure layer, not a model or a chatbot; the sources name no underlying LLM, so do not buy it expecting one. The honest framing is that you are trading managed infrastructure for latency that sits on the critical path instead of behind a round trip.
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Real-world workflow fit
Concrete scenarios for the personas Moss actually fits — and what changes day-one when you adopt it.
Installs the Python SDK, creates a project key in the Moss Portal, loads the customer's account and appointment records into an index, and wires the retrieval call into an existing LiveKit or Pipecat pipeline ahead of the LLM stage.
Outcome: The agent resolves mid-conversation lookups in single-digit milliseconds, so the caller hears a natural pause rather than the 100–500ms dead air a hosted vector DB round trip introduces.
Compiles Moss to WebAssembly inside the app, ships the index with the binary, and queries it from the device with no network call; the continuous sync engine refreshes the index the next time the phone has connectivity.
Outcome: Search works in airplane mode with no third-party vector store in the data path, which also keeps user queries off a centralized system.
Runs Moss alongside the existing LangChain stack, points the retriever at a local Moss index instead of the hosted vector store, and removes the separate embedding API call from the hot path.
Outcome: P99 retrieval latency drops from hundreds of milliseconds to single digits, and the team drops the cost line for a managed vector database plus per-call embeddings.
Use Cases
- Add real-time semantic search to a voice agent so it retrieves order or appointment details mid-conversation without lag.
- Build an AI copilot that pulls internal documentation with sub-10ms retrieval latency.
- Deploy an offline, on-device knowledge base for a mobile app using local indexing and querying.
- Replace a Pinecone- or ChromaDB-backed RAG pipeline to remove network round trips and cut P99 latency.
- Use Moss with LangChain as a locally running vector store to reduce infrastructure cost and complexity.
- Wire Moss into Next.js Server Actions to add instant search to a documentation site with no external database.
Limitations
- Moss is a retrieval and infrastructure layer for real-time semantic search, not an AI model or a chatbot; the scraped content and seed data name no underlying LLM.
- It presumes you are a developer working in Python or TypeScript — there is no no-code path.
- The captured pricing page did not include tier pricing or plan details in this run; refer to the vendor's pricing page for current rates.
as of 2026-10-07
Verification history
We have re-verified Moss 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Moss tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Developer
$0/mo + usage
Ideal for
Individual developers prototyping a voice agent or copilot who want to test retrieval latency locally before committing budget.
What this tier adds
Free entry point: $0/mo plus usage credits, Python and TypeScript SDK access, local and cloud retrieval, no credit card required to start.
Hobbyist
$30/mo + usage
Ideal for
Solo builders and small projects running a real agent in front of users but not yet at production scale.
What this tier adds
Adds higher usage limits on top of Developer, plus hybrid semantic and keyword search, metadata filtering with geo support, and cloud fallback with continuous sync.
Start-Up
$200/mo + usage
Ideal for
Startups running production voice and copilot workloads where retrieval latency is a product feature.
What this tier adds
Adds production-scale throughput for real-time agents, multi-index query and bulk index lifecycle management, and priority support.
Enterprise
Contact Us
Ideal for
Regulated or large organisations that need self-hosted or on-device deployment, compliance evidence, and contractual guarantees.
What this tier adds
Adds SOC 2 and HIPAA compliance paths, self-hosted and on-device deployment options, custom SLAs and volume pricing, and dedicated engineering support.
Where the pricing makes sense
The company stage and team size where Moss's pricing actually pencils out — and where peers do it cheaper.
The Developer tier starts at $0/mo plus usage, which puts Moss below managed vector databases that bill for always-on clusters — but Moss's paid tiers are base-plus-usage, so the $30/mo Hobbyist and $200/mo Start-Up rates are entry points rather than fixed bills. Against a managed cloud RAG pipeline, the vendor claims roughly 20% lower cost because there is no separate embedding API call on the hot path.
Setup time & first value
How long it actually takes to get something useful out of Moss — broken out by persona, not the marketing-page minute.
Voice AI and copilot developers: budget roughly 15–30 minutes, since the Portal onboarding now walks you from sign-up to a built-and-searched index and mints a project key in place. Engineers swapping out an existing vector DB retriever should allow an afternoon to re-index and validate latency. A non-developer pairing with an engineer needs longer, because everything is wired through the Python
Switching to or from Moss
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Pinecone: point your retriever at a local Moss index and drop the remote query call; teams typically switch to cut latency and simplify infrastructure.
- →From Qdrant: re-index your documents into Moss and remove the network round trip, keeping your LLM and voice stack unchanged.
- →From ChromaDB: replace the in-process Chroma client with the Moss SDK; benchmarked P50 drops from 351.8ms to 3.1ms on 100K documents.
- →From Weaviate: move critical-path retrieval into Moss while leaving non-latency-sensitive workloads where they are.
- ↗To Pinecone: move to a managed multi-region vector store when you need replication and analytics over millions of documents more than you need single-digit latency.
- ↗To Qdrant: migrate when your workload is batch or offline retrieval where a few hundred milliseconds does not matter.
- ↗To a managed cloud RAG pipeline: revert if you decide you do not want retrieval running in-process or on-device.
Integrations
Resources & Guides
- Documentationmoss.dev
Docs · Moss
Full product docs from moss.dev
- Documentationmoss.dev
Integrations · Moss
Full product docs from moss.dev
- Documentationmoss.dev
Api Reference · Moss
Full product docs from moss.dev
- Documentationmoss.dev
Voice Agents · Moss
Full product docs from moss.dev
- Documentationmoss.dev
Cookbook · Moss
Full product docs from moss.dev
- Resourcemoss.dev
Benchmarks · Moss
Helpful link from moss.dev
Tutorials & Learning
YouTube returned 6 videos for “Moss”, and we withheld 6: 6 could not be judged, because “Moss” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Moss.
Official links
Tools that pair well with Moss
Common stack mates teams adopt alongside Moss, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Moss vs Spider Cloud
Moss and Spider Cloud serve fundamentally different retrieval needs. For teams building latency-sensitive voice AI or on-device copilots, Moss's sub-10ms local semantic search is unmatched. For AI agents and RAG pipelines that rely on up-to-date web content, Spider Cloud's fast, cheap scraping with AI extraction is the clear choice. Choose Moss if milliseconds matter and your data is mostly internal; choose Spider Cloud if you need to fetch, structure, and pipe web data into your AI stack.
Moss vs Temporal Ai
Choose Temporal AI if your priority is durable execution—workflows that survive crashes, automatic retries, and human-in-the-loop—for AI agents or microservices. Choose Moss if your bottleneck is retrieval latency: it delivers sub-10ms semantic search for real-time voice AI and copilots, running locally or on-device. They serve complementary needs; many teams may use both.
Moss vs Presto Voice
For QSR chains needing a turnkey drive-thru automation solution with proven upselling, Presto Voice is the clear choice. For developers building custom voice AI agents that require ultra-low latency retrieval, Moss provides an unmatched, freemium-friendly semantic search engine. The two tools serve entirely different roles in the AI stack and are not direct competitors.
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