Run an Endpoint Portability Drill Before You Commit to a Free Hosted Model

Free hosted model access looks like the cheapest decision you will make this quarter, and that is exactly why it deserves a test. The cost you should be afraid of is not the invoice, it is the afternoon you lose when the endpoint you built around stops fitting. So here is my rule: measure your exit cost before you measure anything else. If you cannot move the workflow to another backend in one sitting, you have not saved money, you have bought a lease.

This post is a decision guide, not a product review. It gives you a small reproducible drill that scores a hosted endpoint, a self-hosted endpoint, and a paid endpoint on the same frozen task suite, using the same code path. Run it once, keep the numbers, and re-run it when the terms or your workload change. Fair warning: the scripts below are a template I have not executed against any live endpoint, so treat them as scaffolding you fill in with your own credentials and your own tasks.

The decision is a seam, not a vendor

Most teams evaluate endpoints by asking which one is cheaper per call, and that question hides the expensive part of the answer. The expensive part is how many files you touch when the cheap option changes its limits, its model list, or its availability. So before comparing anything, put one seam between your agent logic and the network call, and let an environment variable choose the backend behind that seam. Then the comparison becomes a configuration change instead of a refactor.

Step 1: Write the seam once

Keep it boring, keep it dependency-free, and keep it in one file that nothing else imports around. The point is that every backend speaks the same OpenAI-compatible request shape, so the drill can swap base URLs without touching prompts.

# seam.py - the only file that knows a backend exists
import json, os, time, urllib.request

BASE = os.environ["AGENT_BASE_URL"].rstrip("/")  # e.g. https://host/v1
KEY = os.environ["AGENT_API_KEY"]
MODEL = os.environ["AGENT_MODEL"]

def complete(prompt: str, schema: dict, timeout: int = 90) -> dict:
    body = json.dumps({
        "model": MODEL,
        "messages": [{"role": "user", "content": prompt}],
        "response_format": {"type": "json_schema",
                            "json_schema": {"name": "task", "schema": schema}},
        "temperature": 0,
    }).encode()
    req = urllib.request.Request(
        f"{BASE}/chat/completions",
        data=body,
        headers={"Content-Type": "application/json",
                 "Authorization": f"Bearer {KEY}"},
    )
    t0 = time.monotonic()
    with urllib.request.urlopen(req, timeout=timeout) as resp:
        payload = json.load(resp)
    return {"latency_s": round(time.monotonic() - t0, 2),
            "raw": payload["choices"][0]["message"]["content"]}

Notice what the seam refuses to do. It does not retry, it does not fall back to a second provider, and it does not cache, because those behaviors would blur the comparison you are trying to make. Retries and fallbacks belong in the layer above the drill, not inside it.

Step 2: Freeze a suite you can replay

A comparison is only reproducible if the inputs never move, so serialize twenty real tasks to JSONL and commit them next to a strict JSON schema. Pick tasks that already appear in your review queue, because synthetic prompts will happily pass a schema your production prompts fail. Keep the schema tight enough that a malformed answer is obviously malformed.

{"id": "rename-01", "prompt": "Return a JSON patch that renames parse_rows to parse_records across src/parser.py"}
{"id": "test-02", "prompt": "Return a JSON patch that adds a regression test for empty input in tests/test_parser.py"}
{"id": "doc-03", "prompt": "Return a JSON patch that documents the retry parameter added in the last commit"}

Step 3: Score three numbers, not one

Most comparisons stop at latency and quality, which is two numbers, and the missing third number is the one that decides the lease. Write the runner so it records schema adherence, wall-clock latency, and a manual acceptance flag you set during review. Then compute per-task agreement between two runs to see whether the cheaper backend is merely slower or genuinely different.

# drill.py - replay the frozen suite, one JSON row per task
import json, sys
from pathlib import Path
from seam import complete

SCHEMA = json.loads(Path("task.schema.json").read_text())
rows = []
for line in Path("suite.jsonl").read_text().splitlines():
    task = json.loads(line)
    score, out = 0, {"latency_s": None, "raw": ""}
    try:
        out = complete(task["prompt"], SCHEMA)
        score = 1 if json.loads(out["raw"]).get("patch") else 0
    except Exception as exc:
        print(f"{task['id']}: FAIL {type(exc).__name__}", file=sys.stderr)
    rows.append({"id": task["id"], "score": score, **out})
Path(sys.argv[1]).write_text("\n".join(json.dumps(r) for r in rows))

Run it twice against different backends and diff the results, which takes about as long as making coffee.

AGENT_BASE_URL="$HOSTED_URL" AGENT_API_KEY="$HOSTED_KEY" \
AGENT_MODEL="$HOSTED_MODEL" python drill.py run-hosted.jsonl

AGENT_BASE_URL="http://127.0.0.1:8080/v1" AGENT_API_KEY="local" \
AGENT_MODEL="$LOCAL_MODEL" python drill.py run-local.jsonl

python compare.py run-hosted.jsonl run-local.jsonl

The three numbers you take away are schema pass rate, p50 and p90 latency, and accepted-patch rate after your own review. None of those numbers is portable across teams, which is the whole reason you measure them yourself instead of reading a leaderboard.

Read the numbers against a fit table

Once the drill has run, the decision is mostly mechanical, and a table keeps the argument honest. Fill in your own thresholds, but do not change the row labels, because those are the constraints that actually force a choice.

Constraint	Free hosted access fits when	Self-hosted fits when	Paid hosted fits when
Data class	Prompts are already public or synthetic	Code cannot leave your network at all	Contract and region terms cover the data
Burst shape	Low concurrency, tolerant of queueing	You own spare hardware already	Traffic is spiky and deadline-bound
Model pinning	You can accept the current model moving	You need a specific weight hash	Vendor offers version pinning
Review capacity	A human reads every patch anyway	You have ops time, not review time	You need an SLA on time-to-first-token
Exit cost	Under 200 changed lines to switch	You can rebuild the image in a day	Contract exit clause is short

Two rows deserve emphasis. The data-class row is a hard gate rather than a tradeoff, so a free tier never wins it by being cheap. The exit-cost row is the one people skip, and it is measurable: run git diff --stat against the seam directory after the drill and count the lines you would have to rewrite to leave.

Where a free tier earns a seat

MonkeyCode offers free model access and a free server option, and both claims matter for different rows of that table. The hosted access shortens the time between "we need an agent here" and "we have one running", which is the burst-shape row. The server option covers the self-hosted row for teams that want the code path to stay inside their own boundary without buying hardware first. I would still point the drill at both before trusting either one with a release path, and I would check the current allowance and hardware in the console, because free-tier terms move faster than blog posts do.

Disclosure: This article was prepared as part of MonkeyCode's product outreach.

If your suite passes schema on the hosted endpoint and your seam is thin, you have a cheap place to start. If your suite fails schema on every backend, the problem is your prompt contract, and no price list will fix it.

Limits, and who should skip this

This drill tells you about portability and schema behavior, and it tells you nothing about throughput under concurrency, long-context behavior, or cost at ten times your current volume. Re-run it with a concurrency parameter before you scale, because a backend that is fine at one request is not automatically fine at forty.

Skip this approach entirely if you handle regulated data with no approved free-tier path, if you need a pinned weight hash for reproducibility, or if your team has no capacity to review outputs at all. In those cases the drill will produce numbers you are not allowed to act on, which is worse than no numbers. Also skip it if you need a contractual SLA today, because a measurement cannot manufacture one.

So before you pick the free option or the self-hosted one, ask yourself a blunt question. If the endpoint disappeared tomorrow morning, would your diff be small?