$15K–$75K typical projects
Direct answer
I build production MCP servers as fixed-scope projects, typically $15K to $75K depending on how many tools the server exposes, the auth model, and whether it ships for internal use or to your customers. I'm a Guest Engineer at Expensify, Top Rated on Upwork with $100K+ earned and verified client reviews, and I bring 7+ years of production delivery to what is still a young ecosystem full of proof-of-concept quality code. A focused internal server ships in 3 to 5 weeks; a customer-facing server with OAuth and multi-tenant isolation runs 8 to 12 weeks.
MCP servers are how AI assistants get safe, structured access to your systems — your database, your API, your internal tools — and they're rapidly becoming a product surface, not just an integration. The difference between a weekend prototype and a production server is everything the prototype skips: authentication, tenant isolation, rate limits, and tool designs that models actually use correctly.
You need an MVP or v2 shipped on budget with someone who makes architecture decisions and owns delivery end-to-end.
You need a senior IC to augment the team, rescue a codebase, or lead mobile + AI integration without months of hiring.
You need a reliable senior subcontractor for client projects — clear communication, store-ready quality, white-label friendly.
30 minutes — goals, stack, timeline, budget range.
Fixed milestones, clear deliverables, start date.
Weekly demos, async Slack updates, production standards.
Store launch, documentation, knowledge transfer.
A B2B SaaS product's customers are asking to use it from Claude and other AI assistants. I design the tool surface around real customer jobs rather than mirroring the API, implement OAuth with tenant isolation, and ship a remote server with usage analytics. Outcome shape: a supported integration channel customers adopt, instead of them scraping your API through fragile wrappers.
An engineering team wants their AI assistant to query the data warehouse, check deploy status, and file tickets — currently all manual context-switching. I build an internal MCP server with read-write separation, per-user permissions inherited from existing SSO, and audit logging of every tool call. Outcome shape: the assistant becomes genuinely useful for operations work without becoming a security incident.
A developer on the team built an MCP server in a hackathon and now leadership wants it live, but it has no auth, leaks stack traces, and times out on large results. I audit it, keep the tool designs that work, and rebuild the transport, security, and error handling for production. Outcome shape: the prototype's value with none of its liabilities.
The mistake almost every first MCP server makes is mirroring an existing REST API one endpoint per tool. Models are not API clients — give them forty granular tools and they chain them badly, pick wrong ones, and burn tokens paginating. A production server exposes a small number of task-shaped tools built around what users actually ask for: not list-projects plus list-tasks plus get-task, but a single search across work items that returns exactly what the model needs to answer.
This is where I spend the first week of any engagement: studying real usage intent and designing a tool surface of typically five to twelve tools with tight descriptions, aggressive input validation, and responses sized for context windows rather than for completeness. Tool descriptions are prompt engineering with production consequences — a vague description means the model calls the tool wrong forever. I test every tool against actual model behavior, not just against a schema validator, because the schema passing means nothing if the model misuses the tool in practice.
An internal server for a trusted team, exposing read-mostly tools over one or two systems, is the $15K–$25K end: the security model rides on your existing network boundary and SSO, and the hard work is tool design and reliability. The price climbs with three things — write operations, external users, and system count.
Write operations need confirmation flows, idempotency, and audit trails, because a model that can create and modify records is a model that will eventually do so incorrectly. External users mean OAuth flows, tenant isolation that's actually tested, rate limiting, and abuse handling — that's the difference between an integration and a product, and it's most of the gap to $75K. System count multiplies the integration and failure-handling work: every backend the server touches brings its own auth, latency, and error semantics that must be translated into something a model can react to sensibly. A customer-facing server with writes across several backends is the top of the range.
MCP servers concentrate risk in a new way: they hand an LLM the ability to act on your systems, and LLMs can be manipulated by content they read. The canonical failure is indirect prompt injection — a support ticket or document containing text that instructs the model to exfiltrate data through another tool call. A production server has to assume the model is confusable and enforce boundaries server-side: per-user permissions checked on every call, not trusted from the client; write operations gated and logged; sensitive fields filtered out of responses by default.
The second class of mistakes is more mundane and just as common in the servers I've audited: secrets in environment variables logged on crash, stack traces returned as tool errors, no rate limits so one looping agent can hammer a backend, and tokens with far broader scope than the tools need. None of this is exotic security engineering — it's the standard production discipline that hackathon-pace MCP development skips. If a vendor's portfolio is demo servers, ask specifically how they handle injection and tenant isolation.
Week one is discovery and tool surface design: what users will ask the assistant to do, which systems answer those requests, and what the minimal tool set looks like. You get a written tool specification before implementation starts, because changing a tool design on paper is free and changing it after clients depend on it is not.
The build phase runs two to eight weeks depending on scope: server implementation, auth integration, the security controls above, and a test suite that includes model-behavior testing — running real conversations against the server and scoring whether the model picks the right tools with the right arguments. For customer-facing servers there's a beta period with usage analytics, because real users ask things you didn't predict and the tool surface always needs one revision. Handover includes deployment infrastructure, monitoring, docs for extending the tool set, and a session with your team on how to evolve tool descriptions without breaking existing behavior. You own all of it — no dependency on me to add the next tool.
If you have one fixed workflow you want automated — every day, summarize these records and post the result — you don't need an MCP server; you need a scheduled script that calls an LLM. MCP earns its cost when the interaction is open-ended: users asking varied, unpredictable questions where the model must choose which tools to use. Building a server for a single deterministic pipeline is buying flexibility you'll never use.
It's also premature if your underlying API is unstable. An MCP server is a contract with model behavior on top of a contract with your backend; when the backend churns weekly, you'll pay to redesign tools continuously. Stabilize the API first. And if your customers haven't asked to use your product from AI assistants, a two-week pilot server with three tools will teach you more about demand than a $75K build — I'd rather scope that pilot and have you come back for the full version with evidence in hand.
You approve milestones and a price before any build starts — no open-ended hourly surprises.
You see running software each week, not status reports, so you can course-correct early.
The person who scopes the work is the person who builds it — no junior layers, no agency markup.
Top Rated on Upwork with public client reviews and $100K+ earned, plus contributions to Expensify. Check the receipts before you commit.
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Production MCP servers typically cost $15K to $75K. An internal server with read-mostly tools over one or two systems lands at $15K–$25K. Customer-facing remote servers with OAuth, tenant isolation, write operations, and abuse handling occupy the upper half. The main cost drivers are write access, external users, and how many backend systems the tools touch — not raw tool count.
A focused internal server ships in three to five weeks including tool design, security review, and model-behavior testing. Customer-facing servers run eight to twelve weeks because OAuth, multi-tenancy, rate limiting, and a beta period with real users add unavoidable calendar time. A working prototype exists much sooner — usually week two — but prototype and production are very different claims in this ecosystem.
If customers are already asking to use your product from AI assistants, yes — it's becoming a standard integration channel, and the alternative is customers building fragile unofficial wrappers around your API. If demand is unproven, start with a small pilot server exposing three or four high-value tools and measure usage before committing to a full build. Treat it as a product surface with versioning and support, not a side project.
Projects typically fall in the $15K–$75K range depending on scope, integrations, and timeline. I provide a fixed-scope proposal after a 30-minute scoping call.
MVPs often ship in 8–12 weeks. Production systems with AI backends or RAG may run 12–20 weeks. Rescue and audit engagements can start within days.
Yes. I work with founders, CTOs, product teams, and agencies worldwide — US, UK, EU, and APAC time zones with async updates and weekly demos.
Yes. I specialize in React Native + Python (FastAPI) + AI (RAG, agents, OpenAI/Claude) under one senior owner — fewer handoffs, faster shipping.
Book a free 30-minute scoping call on this site, hire through Upwork, or email dhairyasenjaliya@gmail.com with your brief and timeline.
30-minute scoping call · Clear milestones · Senior engineer ownership