Treat Free Tier Like a Hypothesis, Not a Feature

The pattern is familiar by now: a team adopts a free AI coding tier, the assistant passes a cheerful demo task, and the first real batch run dies at minute forty with no error message anywhere in the logs. The server was evicted, the model budget was already half consumed, and the failure gets filed as bad luck instead of a predictable property of the offer. Free tiers are never unconditional, so the conditions are the actual product.

A free offering is best treated as a hypothesis with three separately testable claims: the model is good enough for your chosen tasks, the server is available when you need it, and the token allowance covers your true burn rate. Each claim fails on a different kind of evidence, and none of them fails during a polished demo. The cheapest way to test all three is a time-boxed spike that leaves a written decision behind instead of a general impression.

This article runs that spike against MonkeyCode, an open-source AI coding assistant whose current free entry path, as of this writing, includes a ten-million-token model allowance and a free server option for running the assistant away from your local machine. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Those two variables matter because they are exactly what free tiers tend to hide: the competence ceiling of the model and the operational patience of the server.

The ninety-minute window is not arbitrary because it matches one honest working session on a real repair task instead of a toy example. A spike is a single experiment with one hypothesis, a hard stop, and evidence that supports either shipping the idea or killing it in the open. A longer trial blurs the conclusion across too many variables, while a shorter trial gives a slow cold start no chance to reveal itself.

The harness below performs the whole ritual with one command: it clones a repository into a disposable pair of directories, sends one bounded task to the assistant through the free server, and appends a single line of evidence to a JSONL log. The only inputs are a repository URL and a task file, while two environment variables point the assistant at whatever installation you happen to be auditing. Replace the invocation with your own tool's command and the template still works.

#!/usr/bin/env bash
# spike.sh: one bounded task, one JSONL evidence row, one verdict.
set -uo pipefail

REPO_URL="${1:?usage: spike.sh <repo-url> <task-file>}"
TASK_FILE="${2:?usage: spike.sh <repo-url> <task-file>}"
LOG_FILE="spike-log.jsonl"
BASE_DIR="$(mktemp -d)"

git clone --quiet --depth 1 "$REPO_URL" "$BASE_DIR/repo"
git -C "$BASE_DIR/repo" worktree add "$BASE_DIR/run" HEAD

export MONKEYCODE_SERVER_URL="${MONKEYCODE_SERVER_URL:-https://free-server.example}"
export MONKEYCODE_MODEL="${MONKEYCODE_MODEL:-free-default}"

START=$(date +%s)
"${SPIKE_AGENT_CMD:-monkeycode}" --worktree "$BASE_DIR/run" --prompt-file "$TASK_FILE" --timeout-min "${SPIKE_TIMEOUT_MIN:-90}" --tokens-cap "${SPIKE_TOKEN_CAP:-90000}" > "$BASE_DIR/out.json" 2>&1
AGENT_EXIT=$?
END=$(date +%s)

DIFF_ADDED=$(git -C "$BASE_DIR/run" diff --numstat HEAD 2>/dev/null | awk '{a+=$1} END {print a+0}')
TOKENS=$(python3 -c 'import json,sys
d=json.load(open(sys.argv[1]))
print(d.get("tokens_total", "unknown"))' "$BASE_DIR/out.json" 2>/dev/null || echo unknown)

echo "$(date -u +%FT%TZ) $((END-START)) $DIFF_ADDED $AGENT_EXIT $TOKENS" >> "$LOG_FILE"
cat "$BASE_DIR/out.json"

Every piece of captured evidence answers one of the three claims, and the disposable worktree keeps the trial reproducible from the same commit on any machine. The diff line separates real code from a prose apology, the exit code separates a finished task from a killed process, and the token figure is read from the tool's own metrics output when present. An "unknown" in that column is still evidence, since a free tier that cannot report its own consumption cannot be budgeted honestly.

The rubric for reading the row is deliberately coarse. A verdict of ship means the task completed inside the window with a meaningful diff and a token burn below a small fraction of the monthly allowance; adopt-with-conditions means completion happened but the log exposed a cold start, an eviction, or a near-cap burn; kill means the task ran out of time or ran out of tokens. A short verdict file makes the outcome legible to anyone who was not present during the experiment:

{
  "hypothesis": "free tier fixes a failing unit test within 90 minutes",
  "wall_seconds": 3180,
  "diff_added": 41,
  "agent_exit": 0,
  "tokens_burn": 182000,
  "verdict": "adopt-with-conditions",
  "conditions": [
    "server cold start near 4 minutes",
    "schedule long tasks outside evening eviction windows"
  ]
}

Honesty about limits is what keeps the spike useful, because the method does not prove sustained throughput, concurrent sessions, data-retention behavior, or long-term rate-limit stability. A free server that passes a Tuesday morning trial can still evict you on Thursday night, so the spike should be repeated whenever the offer changes instead of trusted forever. Teams handling regulated data, workloads with hard latency ceilings, or anything that legally requires a named host should not rely on a free tier at all, no matter how clean the evidence looks.

The wider shift in AI-assisted development is that every developer has become a reviewer of machine-written patches, and reviewers starve without evidence. The JSONL row is the audit trail, the verdict file is the review, and the spike is the rare meeting that ends with a written decision instead of another debate. If the free tier passes, you adopt with conditions written down; if it fails, you have saved a week of integration pain for the price of ninety minutes.

The harness is short enough to run against your own task list this afternoon, and MonkeyCode's free model allowance plus free server gives anyone a legitimate subject for the same trial while the open-source setup keeps the experiment inspectable. One weekend is enough to turn a marketing promise into a dated document, and that document keeps answering questions long after the offer changes. That is the whole promise of the spike: the evidence outlives the excitement.