Pctx is live · custom pricing · last verified 22 Oct 2026 · viability monitor
Pctx
Self-hosted context layer for AI agents in regulated firms — run on your cloud, on-prem or air-gapped.
Pick pctx when sensitive data is the reason agents haven't shipped. Self-hosting on your own cloud or air-gapped hardware answers the deployment question compliance actually asks, and AVP tracing means you can replay a run when someone asks what the agent did. The trade is real: you run the infrastructure and describe agents in code, so budget for an engineer, not a signup.
Verdict generatedVerified 8d ago · liveness 60/100 · cite: rightaichoice.com/tools/pctx
- Private equity funds running deal sourcing, diligence and portfolio monitoring agents over their own CRM and document archives
- Banks, insurers and financial firms whose data cannot leave the network under any circumstance
- Legal, healthcare and government teams with data-residency or fully air-gapped requirements
- Engineering teams that must replay exactly what an agent did, which sources it used, and what it cost
- Teams that want a fully managed hosted agent platform with no infrastructure to operate
- Users who need a drag-and-drop no-code agent builder rather than agents described in code
- Startups optimizing purely for the lowest monthly cost with no compliance driver
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Skip Port of Context if you want a fully managed hosted agent with no infrastructure to run, or if your team will only adopt agents through a drag-and-drop no-code builder rather than described ones.
Running on your own cloud, on-prem, or air-gapped hardware means the compute, storage, and egress bill lands on your infrastructure budget, not the vendor's.
That makes this a procurement conversation rather than a card-on-file purchase, and it sits in the enterprise self-hosted tier alongside other deployment-on-your-own-infrastructure agent platforms rather than alongside per-seat hosted agent tools or low-cost API wrappers. Budget for infrastructure and model
In short
Pctx — Self-hosted context layer for AI agents in regulated firms — run on your cloud, on-prem or air-gapped. Best for Private equity funds running deal sourcing, diligence and portfolio monitoring agents over their own CRM and document archives, Banks, insurers and financial firms whose data cannot leave the network under any circumstance, Legal, healthcare and government teams with data-residency or fully air-gapped requirements. Pricing is on request (custom quotes).
What is Pctx?
Pctx — Self-hosted context layer for AI agents in regulated firms — run on your cloud, on-prem or air-gapped. Best for Private equity funds running deal sourcing, diligence and portfolio monitoring agents over their own CRM and document archives, Banks, insurers and financial firms whose data cannot leave the network under any circumstance, Legal, healthcare and government teams with data-residency or fully air-gapped requirements. Pricing is on request (custom quotes).
Pctx (short for Port of Context) is a context layer platform built for firms whose data can't leave the building: private equity, banking, insurance, legal, healthcare and government. Rather than just wrapping a model, it connects your documents and records to the definitions and relationships that give them meaning, the tools an agent may call, and the rules for who can reach what, then records what every run did and which sources it drew on. Deployment is the front door. The MIT-licensed core runs in your own cloud account, your data center, or a fully air-gapped network via a single command (`pctx deploy --self-host`), with your own models and keys. The same agent runs on Claude, GPT, Gemini or open weights and reroutes automatically if a provider slows or goes down. Three mechanisms carry the pitch. Code Mode executes tool calls as code and keeps heavy data outside the model's context — in the Agentic AI Foundation case study, each run dropped from 93.6k input tokens to 31.8k at half the cost. Observability, built on the open Agent Voyager Project (AVP) spec, traces every tool call, retrieval and reasoning step with run replay, side-by-side comparison, output-to-source tracing and drift alerts. And every tool call runs isolated by default with explicit whitelisting, with MCP servers plugging into tools your team already uses. The proof point is a featured private equity case study: 150,000+ CRM records wired into agents, 100+ portfolio evaluations with no hallucinated answers, a relationship brief in under 60 seconds, and $0.03 model input cost per portfolio update. Against hosted agent builders, pctx trades convenience for residency and auditability — you supply the infrastructure and the operator.
Key features
- Self-hosted deployment into your own cloud account, data center, or air-gapped network
- Single-command self-host deploy with an open source, MIT-licensed core
- Context layer connecting firm data, definitions, relationships, and access rules
- Model-agnostic execution across Claude, GPT, Gemini, or open-weight models
- Automatic rerouting to another model when a provider slows or goes down
- Bring your own models and keys with control over what leaves the environment
- Code Mode: tool calls execute as code with heavy data kept outside model context
- Token and cost reduction measured in the Agentic AI Foundation case study (93.6k to 31.8k input tokens)
- Live tracing of every tool call, retrieval, and reasoning step with cost and reason
- Run replay and side-by-side comparison of agent sessions
- Output-to-source tracing back to the data an answer came from
- Drift detection that alerts you instead of just writing a log line
- Open agent-observability spec (Agent Voyager Project, AVP) as the tracing layer
- Isolated per-tool-call execution by default with explicit whitelisting
- MCP server integration so agents use tools your team already runs
Behind the verdict
Most agent platforms ask you to trust their cloud. Pctx asks you to trust your own rack. That inversion is the whole product, and for a regulated buyer it is the right one — deploying into your VPC or an air-gapped network with your own keys removes the objection that usually stalls agent pilots in banking, insurance and PE. We'd reach for this when residency, auditability or model portability are hard requirements, not nice-to-haves. The observability story is the part worth poking at in a trial: every tool call, retrieval and reasoning step is traced on the open AVP spec, with replay, side-by-side comparison and output-to-source tracing. When a partner asks why the agent flagged a deal, you can show the answer. The cost angle is real if you push volume. Code Mode keeps bulky data out of the model's context, and the Agentic AI Foundation case study measured a run dropping from 93.6k to 31.8k input tokens at half the cost — the kind of number that shows up on a monthly invoice. The PE case study's $0.03 per portfolio update is the sort of line item a CFO will actually read. Where it bites: you need an engineer to run it, agents are described in code rather than assembled in a drag-and-drop canvas, and the integration surface we could verify is narrow — Gmail via MCP is the concrete example on the page, plus whatever MCP servers your team wires up. If your stack lives in a dozen SaaS tools and you want prebuilt connectors, a hosted builder will get you further faster. Comparison to the closest alternative is blunt. Hosted agent builders win on time-to-first-agent and give you someone else's uptime guarantees. Pctx wins on where the data sits and what you can prove about each run afterward. Model-agnostic execution is the quiet insurance policy: switching from one
Models under the hood
as of 2026-10-25
Limitations
- Self-hosting means you supply the infrastructure, the keys, and someone to operate the deployment — an air-gapped install with local or open-weight models is supported but is an infrastructure project, not a signup.
- You describe agents rather than assembling them in a visual builder, so there is no drag-and-drop path and the learning curve is steep for non-technical users.
- Integrations run through MCP connectors, which is clean where your stack is already covered and means custom connector work where it isn't.
- Per-call isolation and whitelisting are defaults, not settings, so you decide what an agent may reach before it runs.
- The 98% token-cost reduction figure is a vendor claim tied to Code Mode workloads rather than a measured benchmark across every agent you might run.
as of 2026-10-11
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Is Pctx still active?
Pctx is live · custom pricing · last verified 22 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Pctx? 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 →What do users say about Pctx?
What people actually say about Pctx — is it worth it?
Our community sentiment research written Jul 6, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Pctx— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 22 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Pctx 12 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.
- — changed pricing page
- — changed summary, who should skip it, features, description, integrations, who it suits
- — changed pricing page
- — changed features, who should skip it, description, who it suits, summary, our verdict
- — changed domain expiry
- — changed pricing page, switching out, description, models, summary, our verdict, who it suits, who should skip it, setup time, hidden costs, pricing fit, limitations, switching in
Showing the 6 most recent of 12 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Pctx cost in 2026?
Pctx: Pricing is on request (custom quotes). Pricing last verified 22 Oct 2026.
Where the pricing makes sense
The company stage and team size where Pctx's pricing actually pencils out — and where peers do it cheaper.
That makes this a procurement conversation rather than a card-on-file purchase, and it sits in the enterprise self-hosted tier alongside other deployment-on-your-own-infrastructure agent platforms rather than alongside per-seat hosted agent tools or low-cost API wrappers. Budget for infrastructure and model
Has Pctx's pricing changed?
No plan price for Pctx has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Pctx?
Pick Pctx if you are Private equity funds running deal sourcing, diligence and portfolio monitoring agents over their own CRM and document archives, Banks, insurers and financial firms whose data cannot leave the network under any circumstance, Legal, healthcare and government teams with data-residency or fully air-gapped requirements, Engineering teams that must replay exactly what an agent did, which sources it used, and what it cost.
Look elsewhere if you are Teams that want a fully managed hosted agent platform with no infrastructure to operate, Users who need a drag-and-drop no-code agent builder rather than agents described in code, Startups optimizing purely for the lowest monthly cost with no compliance driver, Solo developers or small teams with nobody to run and maintain a self-hosted deployment.
Skip it if Skip Port of Context if you want a fully managed hosted agent with no infrastructure to run, or if your team will only adopt agents through a drag-and-drop no-code builder rather than described ones.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Pctx actually fits — and what changes day-one when you adopt it.
Point the context layer at the firm's CRM records, board minutes, quarterly reports, and LP letters; deploy with `pctx deploy --self-host` inside the firm's VPC; then ask the agent for a relationship brief on a target.
Outcome: A relationship brief returned in under 60 seconds with every claim traced back to its source document, and a run record the IC can replay — the pattern behind the reported 100+ portfolio evaluations with no hallucinated answers.
Stand up the MIT-licensed core on an air-gapped network with open-weight models, whitelist which tools each agent may call, and enable AVP tracing across tool calls and retrievals.
Outcome: Agents run with no external network calls, and every run is replayable and comparable side by side when an examiner asks what the agent did, which sources it drew on, and what it cost.
Wire the Gmail and Slack MCP connectors so agents operate where the team already works, routing inbox triage and thread follow-up through the agent rather than a new interface.
Outcome: No new UI to learn — triage happens in Gmail and Slack, and Code Mode keeps the heavy attachment and thread data outside the model context, which is where the token-cost saving comes from.
Use cases
- Automate deal sourcing by running agents across CIMs, expert call transcripts, and financial models.
- Run due diligence across contracts, SEC filings, and compliance records with every output traced to its source.
- Monitor portfolio companies by scanning quarterly reports, board minutes, and LP letters.
- Triage and respond to inbox messages through the Gmail and Slack MCP connectors.
- Extract structured data from unstructured material like handwritten notes, voicemails, and scanned signatures.
- Replay and compare past agent runs when compliance or an investment committee asks what an agent did and why.
- Run agents end to end on air-gapped networks with local or open-weight models and no external network calls.
- Keep agents running when a model provider slows or fails, via automatic rerouting to another model.
Setup time & first value
How long it actually takes to get something useful out of Pctx — broken out by persona, not the marketing-page minute.
For an engineering team on your own cloud: expect a working self-hosted deployment via `pctx deploy --self-host` in a day, with model keys and whitelisting configured, then days to wire the context layer over your document stores. On-prem or fully air-gapped with local or open-weight models, budget weeks — it's an infrastructure project. Non-technical teams should assume they need an engineer
Switching to or from Pctx
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a hosted agent platform: move execution into your own cloud or on-prem with `pctx deploy --self-host`, then repoint model keys at Claude, GPT, Gemini, or open weights.
- →From a semantic layer: keep the metric definitions and add the context layer on top, connecting documents, relationships, tool access, and per-call isolation rules.
- →From ad-hoc scripts and notebooks: replace one-off retrieval code with a context layer plus AVP tracing so every run is replayable and output-to-source attributable.
- →From an MCP-connected assistant: keep the existing MCP servers such as Gmail and Slack and point them at pctx so agents run on the tools the team already uses.
- ↗To a fully managed hosted agent platform: the trade is losing self-hosted and air-gapped deployment in return for not operating infrastructure yourself.
- ↗To a drag-and-drop no-code agent builder: the trade is losing described-agent flexibility and AVP run comparison in return for non-technical users being able to assemble agents.
- ↗To cloud-vendor-native agent tooling: keep execution inside one hyperscaler's stack, at the cost of the model-agnostic rerouting across Claude, GPT, Gemini, and open weights.
- ↗To building retrieval and tool-calling in-house: you gain full control of the code, and take on the context layer, per-call isolation, and observability work that pctx currently provides.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Pctx”, and we withheld 6: 6 could not be judged, because “Pctx” 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 Pctx.
Official links
What are the best Pctx alternatives?
The closest alternatives to Pctx we list are Mem0, pumaDB and DataGrout. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Mem0 | Freemium | Pricing not published | — |
| pumaDB | Freemium | Pricing not published | — |
| DataGrout | Freemium | Pricing not published | — |
Alternatives to Pctx
View all
Tools that pair well with Pctx
Common stack mates teams adopt alongside Pctx, with the specific reason each pairing earns its keep.
Mem0
Mem0 is a memory layer that gives AI agents and apps persistent context across sessions and users
pumaDB
Hosted MCP and REST memory that keeps AI agent context consistent across ChatGPT, Claude, Codex, and your own agents.
DataGrout
DataGrout is an operating layer for production AI agents: persistent memory, real-time cost control, and cryptographic receipts for every action.
Pctx vs its closest rival
Pctx's closest head-to-head is with Spider Cloud — Choose Spider Cloud if you need fast, low-cost web scraping for AI agents or RAG pipelines — its new Browser AI commands and AI Studio add-on make it powerful for live data extraction. Choose Pctx only if you must self-host agents on strictly private data and require per-step observability, governance, and compliance — it's an enterprise platform, not a scraping tool. Verdict generated
Head-to-head comparisons
Pctx vs Spider Cloud
Choose Spider Cloud if you need fast, low-cost web scraping for AI agents or RAG pipelines — its new Browser AI commands and AI Studio add-on make it powerful for live data extraction. Choose Pctx only if you must self-host agents on strictly private data and require per-step observability, governance, and compliance — it's an enterprise platform, not a scraping tool.
GeneratedPctx vs Presto Voice
Presto Voice and Pctx serve entirely different purposes: Presto optimizes drive-thru order taking for QSR chains, while Pctx enables secure, self-hosted AI agents for enterprises with sensitive data. Choose based on your domain—restaurant operations or private data workflows.
GeneratedPctx vs Temporal Ai
Choose Temporal if you need battle-tested durable execution for AI agents or microservices, especially if you want open-source flexibility, multiple SDKs, and cloud or self-hosted deployment. Choose Pctx if your priority is absolute data privacy with self-hosted AI agents, full audit trails, and you operate in a regulated industry like PE or law. For most teams building reliable workflows, Temporal's maturity and ecosystem are hard to beat.
GeneratedFrequently asked questions
Best-of guides
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